- Email: [email protected]
Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle
ISA Transactions xxx (xxxx) xxx
Contents lists available at ScienceDirect
ISA Transactions journal homepage: www.elsevier.com/locate/isatrans
Practice article
Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle ∗
A. Namigtle-Jiménez a , R.F. Escobar-Jiménez b , , J.F. Gómez-Aguilar c , C.D. García-Beltrán b , A.C. Téllez-Anguiano d a
Posgrado del Tecnológico Nacional de México / CENIDET. Int. Internado Palmira S/N, Palmira C.P.62490, Cuernavaca, Morelos, Mexico Tecnológico Nacional de México / CENIDET. Int. Internado Palmira S/N, Palmira C.P.62490, Cuernavaca, Morelos, Mexico c Conacyt-Tecnológico Nacional de México / CENIDET. Int. Internado Palmira S/N, Palmira C.P.62490, Cuernavaca, Morelos, Mexico d Tecnológico Nacional de México / Instituto Tecnológico de Morelia, Av. Tecnológico 1500. Col. Lomas de Santiaguito. C.P. 58120, Morelia, Mich., Mexico b
article
info
Article history: Received 20 December 2018 Received in revised form 30 October 2019 Accepted 4 November 2019 Available online xxxx Keywords: Electronic fuel injection rail system Fault detection and diagnosis scheme Artificial neural networks FPGA
a b s t r a c t In this research, fault detection and diagnosis (FDD) scheme for isolating the damaged injector of an internal combustion engine is formulated and experimentally applied. The FDD scheme is based on a temporal analysis (statistical methods), as well as in a frequency analysis (fast Fourier transform) of the fuel rail pressure. The arrangement of the scheme consists of three coupled artificial neural networks (ANNs) to classify the faulty injector correctly. The ANNs were trained considering five different scenarios, one scenario without fault in the injection system, and the other four scenarios represent a fault per injector (1 to 4). The Levenberg–Marquardt (LM), BFGS quasi-Newton, gradient descent (GD), and extreme learning machine (ELM) algorithms were tested to select the best training algorithm to classify the faults. Experimental results obtained from the implementation in a VW fourcylinder CBU 2.5L vehicle in idle operating conditions (800 rpm) show the effectiveness of the proposed FDD scheme. © 2019 ISA. Published by Elsevier Ltd. All rights reserved.
1. Introduction Nowadays, automatic control area has become a powerful tool to deal with different types of problems, such as tracking trajectories [1,2], regulation [3], as well as fault diagnosis [4] and fault-tolerant control [5]. In literature, fault detection and diagnosis (FDD), fault detection and isolation (FDI), and faulttolerant control (FTC) systems have been studied by different authors [6–8] since the early diagnosis of a fault allows the operator to perform changes in the system or stop it. Over the years, the internal combustion engine (ICE) has been modified and significantly improved for increasing its efficiency and for satisfying the ecological standards. For improving the ICE performance, new mechanical designs and lighter materials, as well as more instrumentation devices and control strategies have been developed. However, adding more electronic devices to the system increases the probability of failures. Fault diagnosis in sensors and actuators is a complex task to carry out because a failure can cause different symptoms in the internal combustion engine. ∗ Corresponding author. E-mail address: [email protected] (R.F. Escobar-Jiménez).
Regarding the ICEs most commons faults, in [9] the author compiled information of 628 engines (including engines with spark ignition (SI) or compression ignition (CI)), the results showed at least 16 common faults. According to the percentage of occurrence, they can be classified as follows, electrical and ignition control system faults, cooling system faults and, fuel injection system faults. To detect the engine faults the author used different diagnosis methods; the on board diagnostics (OBD) system, measurements of electrical values, organoleptic methods including endoscopy and examining the engine during dynamometer stand tests, however, the author did not consider the fault isolation at the injection system. Over the past few years, several authors have chosen to use signal processing techniques to monitor system failures [10], mainly using vibration signals. Regarding fault detection in a combustion engine, in [11], the authors presented a misfire and valve clearance fault detection based on the multi-sensor vibration signal monitoring (4 sensors), and the Fourier transform to extract the signals features, the fault classification was carried out by three different methods (ANN, support vector machines and K-nearest neighbor), reaching 100% accuracy. Other interesting works based on vibration signals or acoustic emission for fault diagnosis can be found in [12–16].
https://doi.org/10.1016/j.isatra.2019.11.003 0019-0578/© 2019 ISA. Published by Elsevier Ltd. All rights reserved.
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
2
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
Fig. 1. Electronic fuel injection system, the black arrow indicates the fuel flow direction.
According to [17,18] the methods based on signals for sensor fault detection, and sensor fault detection and isolation are very efficient in terms of implementation because they only require information of the input and output signals corresponding to the experimental data, taking into account the defects of interest so that a behavior pattern can be generated and then classified. There are statistical and non-statistical fault classification methods in which artificial neural networks are generally used applying different learning algorithms [19,20], however, some of these approaches require physical or analytical sensor redundancy to carried out the fault diagnosis. In [21], the authors used the kernel extreme learning machine (ELM), to build a backup airratio model with the aim of implement a fault tolerance air-ratio control strategy. Other works related to classifying faults in the fuel injection system have been presented mainly in diesel engines. In [22], a fault diagnosis method was designed for the fuel injection system of a diesel engine. The authors proposed a methodology based on vibration signals analysis. First, the signal feature extraction in the time and frequency domains was carried out. After, the backwards propagation neural network was trained to classify 6 different types of faults. The authors reported a high accuracy of the proposed method. In [23], the authors presented a fault diagnosis method in a MATLAB human–machine environment. This method uses the vibration signal and the pressure waveform signal taken by the vibration sensor in the outer wall of the high-pressure oil tube near the injector. The time-domain characteristic values of both signals such as the root mean square (RMS) value, the variance, the average amplitude, are used as data samples. Finally, a radialbased neural network is used to classify the failures. As in the work above mentioned, the authors in [24] work on a human– machine interface, the purpose of the research was to design a virtual diagnosis system for the fuel injectors in diesel engines. The method consisted of measuring the pressure in the high-pressure pipe and the extracting the features of the temporary signal; after, a neural network was trained to carry out the identification of the type of failure. The results showed the identification of two types of failures. The literature review shows different approaches to carried out the fault detection in several subsystems of the internal combustion engines, which are Model-based, vibration or acoustic analysis-based, or threshold-based. Most of the investigations carried out fault detection or classification in the injectors are focused on diesel engines and did not consider the isolation of the damaged injector. Furthermore, only a few research works
were experimentally tested, and they use two or more measured signals for the fault detection. In the present manuscript, the goal is to detect and isolate a faulty injector using only one measured signal (fuel pressure). However, due to the fuel pressure is regulated by a mechanical regulator, the pressure changes caused by a fault in either one of the four injectors are very difficult to detect in the signal. There are different advantages of isolating the damaged injector, for example, the dead-time of the vehicle caused by a bad diagnosis can be avoided, and it may be possible to design an FCT for improving the engine combustion. The main contributions of this work are the following:
• The design of an FDD scheme based on the extraction of •
•
•
•
features of the pressure signal from the injection rail of an ICE. The FDD is capable of extracting the features of the pressure signal from the injection rail even when the pressure regulator compensates the pressure variation caused by an injector fault. The FDD scheme is capable of detecting and isolating accurately the faulty injector in an electronic fuel injection (EFI) system using an arrangement of three ANNs. It was carried out an offline comparison between four different training methods to select the best one, for its implementation in a vehicle. The FDD scheme was validated online in an EFI system of a four-cylinder VW 2014 CBU 2.5-L classic engine using an field-programmable gate arrays (FPGA) device.
2. Materials and methods The EFI system (see Fig. 1) is one of the most important subsystems of a vehicle. The EFI system includes the fuel tank, the injection rail, the fuel pump, the fuel filter, the fuel injectors, etc [25]. The task of the electronic fuel injection system is feeding the fuel into the cylinders to ensure the combustion process the ICE. The injection pressure contains information about the injection system conditions [26]. Fig. 2 shows the FDD scheme to classify faults in the injector of an EFI system. The FDD is carried out in two steps. In the first step, the offline fault classification is carried out. This step consists of 3 stages. Stage (1a) is dedicated to getting the features vector from temporal and frequency pressure signal. Stage (2a) is dedicated to the offline training of the ANNs. Finally, in stage (3a) is carried out the fault classification. In the second step, the
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
3
Fig. 2. FDD scheme to classify faults in the injector of an EFI system.
online fault classification is carried out. This step consists of 3 stages. Stage (1b) is dedicated to obtain the features vector from the online pressure signal. Stage (2b), the ANNs are implemented in the FPG using the obtained weights, and bias from Step 1-stage (2a) to carry out the fault diagnosis. Finally, in stage (3b) the fault classification considering the fault signatures is carried out. To simplify the understanding of the document Table 21 shows the abbreviations used in this document. 2.1. Time domain features extraction
P =
The features extraction is a fundamental part of designing signal-based fault detection and diagnosis systems because these analysis provide particularities of the signal that are related to the system behaviors. For achieving good results in the design of FDD systems, it is recommended to perform the signal analysis not only in the time domain but also in the frequency domain, separately or in collaboration [27–30]. In this investigation, the feature extraction of the pressure variable is carried out considering the scenarios of interest, such as injection rail without failures in injectors and faults in each one of the four injectors, using time statistical methods assuming that the obtained signal, x(t), is a group of discrete data. The parameter of the signal x(t) territory and the probabilistic density function p(x) have a close relationship [22]. Then, methods such as average (X ) value (Eq. (1)), root mean square (Xrms ) value (Eq. (2)), peakto-peak average (|X |) value (Eq. (3)), and variance (Dx ) (Eq. (4)) are calculated. ∞
∫ X =
xp(x) dx
(1)
−∞
√∫
∞
x2 p(x) dx
Xrms =
(2)
−∞
∫
∞
|X | =
|x|p(x) dx
(3)
−∞
Dx =
1 N −1
The time series analysis is included as an analytical method. For this case, the parametric model is used as a base, the aim is to find a systemic mathematical model adaptable to the time series. From an autoregressive (AR) model, the parameters φ1 and φ2 are obtained as characteristic parameters of the fault diagnosis model [22]. Finally, the power (P) contained in the signal of interest was obtained by using Eq. (5). n 1∑
n
x2 [n]
(5)
i=1
Therefore, C is the feature vector that is represented as shown in Eq. (6), which is applied to the pressure signal including all the scenarios of interest. C = {|X |, Dx , Xrms , Dsx , P , φ1 , φ2 }T
(6)
2.2. Frequency domain feature extraction As mentioned, the data acquisition gathers information corresponding to the pressure signal is time dependent. The information and patterns of this signal cannot be directly appreciated, so it is necessary to gather this information and relate it to the characteristic parameters [15,22,28,31]. To develop the analysis in the frequency domain, the FFT is used (see Eq. (7)). F (µ) =
N −1 1 ∑
N
f (x)e−j2πµx/N
(7)
x=0
where F (µ) and f (x) represent the frequency-domain function, and the time domain function, respectively. Once the pressure signal was analyzed using the FFT, a features vector F, using four windows of 139 ms for each scenario of interest is obtained. The feature vector F is composed by the amplitude of the first 14 frequencies of each signal of interest without considering the first or the fundamental frequency, F = fi T for i ∈ [2; 15]. 2.3. Artificial neural network as an offline fault classifier
N
∑
(xi − |X |)2
i=1
(4)
Once the C and F features vectors were obtained, the neural network is trained in order to isolate the faulty injector. Fig. 3
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
4
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
To develop the present work, an arrangement of three ANNs is proposed, each one of them with the structure above mentioned. As is shown in Fig. 4, one of the ANNs is trained with the characteristic parameters obtained by the time-domain feature extraction, while the other is trained with the feature vector F . The ANNs were trained considering five different scenarios, one scenario without fault in the injection system, and the other four scenarios represent a fault per injector (1 to 4). The LM, BFGS quasi-Newton, GD, and ELM algorithms were tested to select the best training algorithm to classify the faults. The confusion matrix and the receiver operating characteristics (ROC) curve are used to validate the result of the classification of the signal of interest in the considered scenarios.
Fig. 3. Multilayer perceptron neural network model.
shows the general structure of the neural network used to carried out the fault isolation. The multilayer perceptron (MLP) neural network has been used for different authors [32,33]. In [32] was developed an FPGA-based architecture of a hybrid multilayered perceptron neural network, the activation function was sigmoidal. The authors tested the algorithm by simulation. In [33] was proposed an MLP neural network with modified sigmoid activation functions with application in an intake manifold model, the authors use the LM algorithm to carry out the training. where output a2 is given as follows a2 = ϕ2 (LW (ϕ1 (IW (x) + b1 )) + b2 )
(8)
where ϕ1 is a nonlinear activation function defined as follows
ϕ1 =
2 (1 + exp(−2x) )
−1
and ϕ2 is a linear activation function, x is the input data set to the neural network, IW and LW are the synaptic weights generated in the training, b1 and b2 are the bias.
3. Calculations Below are shown the feature extraction of the pressure signals in time-domain and in the frequency-domain used for training the neural networks shown in Fig. 4. 3.1. Time domain features extraction As mentioned in Section 2.1, the feature extraction of the pressure variable was carried out considering the scenarios of interest; without faults and with faults in each one of the four injectors. Fig. 5 shows the signals for all above-mentioned scenarios during the test duration. To validate the proposed FDD system offline an Electronic Fuel Injection Virtual (EFIV) system was programmed in an FPGA device. The aim of programming the EFIV system is to estimate the fuel rail pressure considering the pressure drop caused by
Fig. 4. Neuronal networks final structure.
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
5
Fig. 5. Pressure signal behavior in the scenarios of interest including faults in each injector.
Fig. 6. Data analysis windows.
the engine cycle change. So, to develop the test, the time domain features extraction is carried out from data sets collected from the EFIV, the data sets are called windows, each window contains 139 data, this because of each injection cycle is 139 ms (1 s/ms) (see Fig. 6). It is worth mentioning that each signal represents a different scenario. Tables 1–4 present the results of the feature extraction applying Eqs. (1)–(4) to the five signals shown in Fig. 6 in the first and fourth window, respectively. 3.2. Frequency domain features extraction To develop the analysis in the frequency domain, the FFT Eq. (7) is applied to the five signals. Figs. 7–11 show the amplitude (db) of the implicit frequencies, for each scenario of the signal of interest (without failure and failure in each one of the injectors).
Tables 5–8 show the result of selecting the amplitudes of the frequencies needed to construct the F vector for the first and fourth windows, respectively. 3.3. Evaluation of the training algorithms The FDD scheme was evaluated comparing the LM algorithm with the BFGS quasi-Newton, GD and ELM algorithms. The evaluation results are shown in Table 9. As can be seen, Both, LM and ELM algorithms reached 100% of the fitting. However, the ELM algorithm has more neurons in the hidden layer of the first and second NN (200 neurons-150 neurons) compared to the solution found with the LM algorithm (10 neurons-8 neurons). Thus, the LM algorithm is a better option to be implemented online because the computational cost is lower. Based on the obtained results, it is concluded that the FDD scheme fulfilled a correct classification
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
6
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx Table 1 Pressure signal characteristic parameters in the five scenarios of interest in the first data window.
|X | Dx Xrms Dsx P
φ1 φ2
Without fault
Fault in injector 1
Fault in injector 2
Fault in injector 3
Fault in injector 4
87.9912 1.1323 × 106 1.5676 × 107 1.0641 × 103 5.6317 × 1010 −0.5538 −0.4462
116.6326 6.3405 × 105 2.1058 × 107 796.2732 5.5764 × 1010 −0.6127 −0.3873
100.4738 7.7547 × 105 1.9046 × 107 880.6091 5.5422 × 1010 −0.4523 −0.5477
101.486 8.0831 × 105 1.8430 × 107 899.0606 5.5935 × 1010 −0.5345 −0.4655
104.9361 7.7111 × 105 1.8821 × 107 878.1299 5.6040 × 1010 −0.3958 −0.6041
Table 2 Pressure signal characteristic parameters in the five scenarios of interest in the second data window.
|X | Dx Xrms Dsx P
φ1 φ2
Without fault
Fault in injector 1
Fault in injector 2
Fault in injector 3
Fault in injector 4
97.2434 8.5701 × 105 1.8265 × 107 0.9257 × 103 5.6008 × 1010 −0.5026 −0.4975
104.6128 7.4755 × 105 1.8926 × 107 864.6107 5.5422 × 1010 −0.5496 −0.4503
99.7409 8.5630 × 105 1.7729 × 107 925.363 5.5459 × 1010 −0.6251 −0.3749
110.2729 7.2794 × 105 2.0163 × 107 853.1923 5.6012 × 1010 −0.5176 −0.4824
102.5576 7.9 × 105 1.8929 × 107 888.8192 5.5856 × 1010 −0.6118 −0.3883
Table 3 Pressure signal characteristic parameters in the five scenarios of interest in the third data window.
|X | Dx Xrms Dsx P
φ1 φ2
Without fault
Fault in injector 1
Fault in injector 2
Fault in injector 3
Fault in injector 4
90.4371 9.6692 × 105 1.6852 × 107 983.3215 5.5907 × 1010 −0.4569 −0.5432
93.4662 8.4299 × 105 1.7699 × 107 918.1475 5.5583 × 1010 −0.5798 −0.4203
106.3345 7.5345 × 105 1.9342 × 107 868.0141 5.5539 × 1010 −0.5334 −0.4666
100.8371 7.9547 × 105 1.8943 × 107 891.8912 5.5997 × 1010 −0.5126 −0.4874
102.3930 8.0260 × 105 1.8723 × 107 895.8779 5.5827 × 1010 −0.5473 −0.4526
Table 4 Pressure signal characteristic parameters in the five scenarios of interest in the fourth data window.
|X | Dx Xrms Dsx P
φ1 φ2
Without fault
Fault in injector 1
Fault in injector 2
Fault in injector 3
Fault in injector 4
88.3935 1.0514 × 106 1.6323 × 107 1025.4 5.5847 × 1010 −0.4969 −0.5030
105.3396 7.8990 × 105 1.9020 × 107 888.7626 5.5657 × 1010 −0.4996 −0.5004
109.2819 7.2899 × 105 1.9779 × 107 853.8078 5.5692 × 1010 −0.5189 −0.4811
77.1288 1.2981 × 106 1.4629 × 107 1139.4 5.5971 × 1010 −0.5296 −0.4704
104.6041 7.9544 × 105 1.8667 × 107 891.8769 5.5799 × 1010 −0.6208 −0.3790
Table 5 Pressure signal characteristic parameters in the frequency domain in the five scenarios of interest in the first data window. f2 f3 f4 f5 f6 f7 f8 f9 f10 f11 f12 f13 f14 f15
Without fault
Fault in injector 1
Fault in injector 2
Fault in injector 3
Fault in injector 4
1.4254 × 104 2.7108 × 104 0.9048 × 104 0.6615 × 104 1.1993 × 104 1.3004 × 104 2.0865 × 104 2.0243 × 104 1.2596 × 104 1.9181 × 104 0.5351 × 104 1.0102 × 104 0.3695 × 104 0.1560 × 104
0.7244 × 104 2.2540 × 104 0.8274 × 104 0.9010 × 104 1.0403 × 104 1.8571 × 104 1.2989 × 104 0.2441 × 104 0.7834 × 104 0.5631 × 104 0.7735 × 104 1.5498 × 104 0.7395 × 104 0.5063 × 104
3.1364 × 104 0.4616 × 104 0.6076 × 104 0.9735 × 104 0.6804 × 104 0.6331 × 104 0.8838 × 104 0.3635 × 104 1.2379 × 104 0.2745 × 104 0.2086 × 104 0.7896 × 104 1.3495 × 104 1.1764 × 104
1.2353 × 104 1.0308 × 104 1.4801 × 104 1.4454 × 104 0.9424 × 104 1.5217 × 104 1.4332 × 104 1.1027 × 104 0.1957 × 104 1.7681 × 104 2.0045 × 104 2.3676 × 104 1.1231 × 104 0.8740 × 104
1.4519 × 104 1.4656 × 104 0.2913 × 104 0.4887 × 104 1.2508 × 104 0.4899 × 104 1.4705 × 104 0.3485 × 104 0.2376 × 104 1.0263 × 104 0.5490 × 104 1.3777 × 104 0.5202 × 104 0.1446 × 104
of the fault using the LM algorithm and implemented in the EFIV system programmed in an FPGA (Fig. 12).
Later, the FDD system was validated online in the real EFI system of a 2014 Classic VW four-cylinder engine CBU 2.5-L.
3.4. Implementation of the FDD scheme using an EFIV system programmed in an FPGA
3.4.1. Artificial neural network fault classifier training In this section, the results corresponding to the implementation of the fault detection scheme in the embedded system are shown. It is worth mentioning that the training of the neural network was carried out using the LM method offline, the ANN
Once the ANN scheme was developed, the FDD system was tested using the EFIV system programmed into the FPGA device.
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
7
Fig. 7. FFT applied to the pressure signal in a scenario without failure.
Fig. 8. FFT applied to the pressure signal in a scenario failure in injector 1.
time training was 3 s, then the synaptic weights and the bias were used in the FPGA device. The first step was obtaining the pressure signal from the EFIV system considering four analysis windows of 139 data, afterward, the time and frequency analysis of this signal were performed. It generates a (m × K ) data matrix, where m is the features number (7 for the time analysis and 14 for the frequency analysis), and K correspond to the analyzed windows number (in this case, four windows).
Basically, the information of the pressure signal was collected, the feature vectors were formed, the three artificial neural networks were trained and once the amount of necessary information was obtained (synaptic weights and bias), it was implemented in the neural network. The proposed neural network structure was applied for each scenario of interest. In addition, for implementing the FDD system, the two first artificial neural networks have 10 neurons in the hidden layer.
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
8
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
Fig. 9. FFT applied to the pressure signal in a scenario failure in injector 2.
Fig. 10. FFT applied to the pressure signal in a scenario failure in injector 3. Table 6 Pressure signal characteristic parameters in the frequency domain in the five scenarios of interest in the second data window. f2 f3 f4 f5 f6 f7 f8 f9 f10 f11 f12 f13 f14 f15
Without fault
Fault in injector 1
Fault in injector 2
Fault in injector 3
Fault in injector 4
2.5005 × 104 0.5856 × 104 1.5816 × 104 1.1095 × 104 1.4754 × 104 0.7485 × 104 2.3151 × 104 1.2415 × 104 1.1313 × 104 1.1131 × 104 0.4625 × 104 1.7828 × 104 1.4111 × 104 1.5522 × 104
1.1142 × 104 1.1450 × 104 0.8542 × 104 0.5365 × 104 1.1260 × 104 1.6494 × 104 0.7504 × 104 0.7298 × 104 0.6611 × 104 0.6190 × 104 1.0388 × 104 1.6626 × 104 1.6597 × 104 0.7596 × 104
0.9619 × 104 0.6028 × 104 1.2740 × 104 2.0334 × 104 1.4059 × 104 1.2223 × 104 2.1232 × 104 1.2963 × 104 1.0387 × 104 0.7361 × 104 0.5536 × 104 0.7946 × 104 0.9117 × 104 0.7757 × 104
0.9624 × 104 0.7238 × 104 1.6276 × 104 0.4882 × 104 0.8182 × 104 0.3093 × 104 0.5030 × 104 0.8104 × 104 1.2080 × 104 1.6112 × 104 0.9081 × 104 2.1330 × 104 0.9624 × 104 1.0297 × 104
1.4611 × 104 0.9530 × 104 0.1407 × 104 1.9007 × 104 0.8282 × 104 0.3347 × 104 0.9646 × 104 0.1191 × 104 1.2789 × 104 0.8843 × 104 0.2939 × 104 0.6547 × 104 0.9301 × 104 1.2869 × 104
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
9
Fig. 11. FFT applied to the pressure signal in a scenario failure in injector 4. Table 7 Pressure signal characteristic parameters in the frequency domain in the five scenarios of interest in the third data window. f2 f3 f4 f5 f6 f7 f8 f9 f10 f11 f12 f13 f14 f15
Without fault
Fault in injector 1
Fault in injector 2
Fault in injector 3
Fault in injector 4
2.2645 × 104 1.4536 × 104 2.9384 × 104 1.0126 × 104 1.0345 × 104 1.7095 × 104 1.5474 × 104 1.2642 × 104 1.2165 × 104 0.9953 × 104 1.5201 × 104 0.7752 × 104 0.5384 × 104 0.4398 × 104
2.6965 × 104 2.1354 × 104 0.3436 × 104 1.6217 × 104 0.8135 × 104 1.0836 × 104 0.3221 × 104 0.8450 × 104 1.5403 × 104 1.9222 × 104 0.7546 × 104 1.9269 × 104 0.3101 × 104 0.8670 × 104
1.2967 × 104 0.8111 × 104 0.9160 × 104 0.6811 × 104 1.4496 × 104 1.7223 × 104 0.5726 × 104 1.4080 × 104 2.0547 × 104 0.3538 × 104 0.4314 × 104 1.4376 × 104 1.2347 × 104 0.9112 × 104
1.6830 × 104 1.9128 × 104 0.5580 × 104 0.9104 × 104 1.1778 × 104 0.6577 × 104 1.3723 × 104 0.8845 × 104 1.2971 × 104 1.1709 × 104 0.9364 × 104 1.7342 × 104 0.8541 × 104 0.3806 × 104
2.9717 × 104 1.8015 × 104 0.7732 × 104 0.7235 × 104 1.4642 × 104 1.0854 × 104 1.5927 × 104 1.3711 × 104 0.3630 × 104 1.0727 × 104 0.7274 × 104 0.6549 × 104 0.5904 × 104 0.5575 × 104
Table 8 Pressure signal characteristic parameters in the frequency domain in the five scenarios of interest in the fourth data window. f2 f3 f4 f5 f6 f7 f8 f9 f10 f11 f12 f13 f14 f15
Without fault
Fault in injector 1
Fault in injector 2
Fault in injector 3
Fault in injector 4
0.5755 × 104 0.3314 × 104 1.5804 × 104 0.5755 × 104 1.8458 × 104 1.5002 × 104 0.9640 × 104 0.0749 × 104 1.0660 × 104 0.7847 × 104 0.9424 × 104 1.4163 × 104 1.4016 × 104 0.6131 × 104
3.2515 × 104 1.5875 × 104 0.3840 × 104 0.8611 × 104 0.1444 × 104 0.7111 × 104 0.9824 × 104 0.6695 × 104 0.2814 × 104 0.5791 × 104 1.1952 × 104 0.5643 × 104 1.8356 × 104 0.8917 × 104
2.7401 × 104 0.6812 × 104 1.5729 × 104 2.5693 × 104 0.6393 × 104 2.4766 × 104 0.9707 × 104 1.5655 × 104 0.9079 × 104 1.3202 × 104 0.6799 × 104 0.9735 × 104 0.6970 × 104 0.5827 × 104
0.8951 × 104 0.9893 × 104 2.4537 × 104 1.2023 × 104 0.5217 × 104 1.2920 × 104 2.8100 × 104 1.0282 × 104 1.9608 × 104 0.4815 × 104 1.5817 × 104 1.5622 × 104 3.1319 × 104 0.9460 × 104
1.1991 × 104 1.5039 × 104 1.4809 × 104 1.4678 × 104 1.2510 × 104 1.5635 × 104 1.2939 × 104 0.7672 × 104 0.5787 × 104 0.9755 × 104 1.5319 × 104 0.2520 × 104 1.2629 × 104 1.5739 × 104
For both ANNs, a (K × K ) square matrix with a diagonal of 1’s was proposed as the target output. For the third neural network, 8 neurons were used in the hidden layer, and the outputs of the previous neural networks were considered, resulting in a (2K × K ) matrix as input data in the third neural network.
To carry out the FDD validation, there were previously recorded different tests inducing failures in the injectors. The results of the isolation of each one of the faulty injector are shown below. Table 10 shows the results of the FDD scheme implementation in the EFIV system. The test was carried out considering
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
10
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx Table 9 FDD evaluation using different training algorithms.
Table 10 FDD scheme results when the signal without failures in the injectors.
no fault in the injection system. Table 10 is divided into five
in injectors, and four possibles scenarios of faults, one for each
sections which represent the five possible scenarios, without fault
injector. The result of the test showed the diagonal of 1’s is in
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
11
Table 11 FDD scheme results with a failure in injector 1.
Table 11 shows the results of the FDD scheme implementation in the EFIV system. The test was carried out considering a fault in the injector 1. Table 11 was divided into five sections which represent the five possible scenarios of operation of the injection system, without fault in injectors, and four possibles scenarios of faults, one for each injector. The result of the test showed the diagonal of 1’s is in Section 2 (Fault in injector 1), confirming that the system condition is with a fault in the injector 1. Furthermore, the other four sections do not satisfy the diagonal of 1’s. Table 12 shows the results of the FDD scheme implementation in the EFIV system. The test was carried out considering a fault in the injector 2. Table 12 was divided into five sections which represent the five possible scenarios of operation of the injection system, without fault in injectors, and four possibles scenarios of faults, one for each injector. The result of the test showed the diagonal of 1’s is in Section 3, confirming that the system condition is with a fault in the injector 2. Furthermore, the other four sections do not satisfy the diagonal of 1’s. Table 13 shows the results of the FDD scheme implementation in the EFIV system. The test was carried out considering a fault in the injector 3. The result of the test showed the diagonal of 1’s is in Section 4, confirming that the system condition is with a fault in the injector 3. Furthermore, the other four sections do not satisfy the diagonal of 1’s. Finally, Table 14 shows the results of the FDD scheme implementation in the EFIV system considering a fault in the injector 4. The result of the test showed the diagonal of 1’s is in Section 5, confirming that the system condition is with a fault in the injector 4. Furthermore, the other four sections do not satisfy the diagonal of 1’s. 4. Results
Fig. 12. Artificial neural networks implemented in FPGA.
4.1. Artificial neural network as an online fault classifier in the real EFI system
the scenario without fault in the injectors, confirming that the system condition is without faults. Furthermore, the other four sections do not satisfy the diagonal of 1’s.
This section presents the results of implementing the designed FDD scheme in a 2014 Classic Volkswagen 2.0 engine fourcylinder CBU 2.5-l, fuel engine. For this purpose, an MPX5700 pressure sensor was located in the injection rail (see Fig. 13).
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
12
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx Table 12 FDD scheme results with a failure in injector 2.
Table 13 FDD scheme results with a failure in injector 3.
In this experiment, 40 analysis windows were considered. The pressure sensor was placed on the injection rail of the vehicle and connected to the FPGA device, where the FDD scheme was programmed. Fig. 14 shows the pressure signal in the scenarios of interest obtained in the VW vehicle. Table 15 shows that the proposed neural network structure (see Fig. 4) can guarantee a 100% classification in any possible scenario. After obtaining the correct classification percentages shown in Table 15, the obtained synaptic weights and bias were used in the FPGA device to carry out the online FDD scheme, the execution time of the FDD algorithm was 1 s.
Table 16 shows the results of the FDD scheme performing five tests and considering only the scenario without failure in the injection rail. As can be seen, the classification percentage is higher in the scenario without failure. The results of five tests inducing a fault in injector 1 are shown in Table 17. The results show that the classification percentage is higher in the scenario with failure in injector 1. In Table 18 are shown the results of five tests inducing a fault in injector 2. The results show that the classification percentage is higher in the scenario with failure in injector 2. In Table 19 are shown the results of five tests inducing a fault in injector 3. The results show that the classification percentage is higher in the scenario with failure in injector 3.
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
13
Table 14 FDD scheme results with a failure in injector 4.
Table 16 Online performance of the FDD scheme without failure. Without fault Fault in injector Fault in injector Fault in injector Fault in injector
1 2 3 4
Test 1
Test 2
Test 3
Test 4
Test 5
85% 20% 22.5% 40% 25%
92.5% 30% 25% 20% 17.5%
90% 30% 20% 37.5% 22.5%
87.5% 30% 27.5% 32.5% 25%
90% 27.5% 20% 25% 30%
Table 17 Online performance of the FDD scheme with a failure in injector 1. Without fault Fault in injector Fault in injector Fault in injector Fault in injector
Fig. 13. FDD scheme implemented in the EFI system for a 2014 Classic VW. Table 15 Neural networks offline training results. Training Without fault Fault in injector Fault in injector Fault in injector Fault in injector
1 2 3 4
ANN1
ANN2
ANN3
87.5% 90% 87.5% 87.5% 90%
87.5% 87.5% 87.5% 87.5% 90%
100% 100% 100% 100% 100%
In Table 20 are shown the results of five tests inducing a fault in injector 4. The results show that the classification percentage is higher in the scenario with failure in injector 4. The FDD implementation in a vehicle was carried out satisfactorily. In this experiment, a 40-window time and frequency analyses were considered. Note that all the experiments were carried out at idle speed (approximately 800 rpm).
1 2 3 4
Test 1
Test 2
Test 3
Test 4
Test 5
20% 85% 25% 30% 27.5%
22.5% 87.5% 17.5% 32.5% 25%
27.5% 95% 20% 40% 27.5%
27.5% 85% 25% 25% 12.5%
17.5% 95% 20% 27.5% 27.5%
Table 18 Online performance of the FDD scheme with a failure in injector 2. Without fault Fault in injector Fault in injector Fault in injector Fault in injector
1 2 3 4
Test 1
Test 2
Test 3
Test 4
Test 5
27.5% 22.5% 87.5% 25% 15%
25% 20% 100% 35% 22.5%
20% 25% 82.5% 15% 15%
35% 17.5% 85% 17.5% 27.5%
27.5% 22.5% 100% 25% 20%
Table 19 Online performance of the FDD scheme with a failure in injector 3. Without fault Fault in injector Fault in injector Fault in injector Fault in injector
1 2 3 4
Test 1
Test 2
Test 3
Test 4
Test 5
17.5% 22.5% 17.5% 87.5% 22.5%
22.5% 17.5% 15% 100% 27.5%
32.5% 20% 27.5% 80% 17.5%
25% 17.5% 30% 87.5% 17.5%
17.5% 17.5% 25% 87.5% 22.5%
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
14
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
Fig. 14. Behavior of the online pressure signal in the scenarios of interest including faults in each injector. Table 20 Online performance of the FDD scheme with a failure in injector 4. Without fault Fault in injector Fault in injector Fault in injector Fault in injector
1 2 3 4
Test 1
Test 2
Test 3
Test 4
Test 5
20% 32.5% 20% 22.5% 95%
25% 25% 27.5% 27.5% 85%
20% 20% 30% 25% 87.5%
10% 15% 27.5% 25% 82.5%
25% 30% 30% 20% 95%
Although the 100% classification was obtained offline, this percentage was not always achieved online. However, the FDD system has shown its effectiveness to isolate the faulty injector.
system is capable of detecting and isolating a damaged injector accurately by the implementation of three ANNs. The ANNs were trained with the LM algorithm and compared versus the BFGS quasi-Newton, GD, and ELM algorithms. The obtained results demonstrate that the LM algorithm is the best option, not only because it reaches a 100% classification offline and near 100% classification online. The FDD scheme was implemented on an FPGA-based and experimentally tested. Future work will focus on proposing a new alternative to isolate failures in the injection system based on the synchrosqueezing transform for the signals features extraction. Declaration of competing interest
5. Conclusions In this paper was presented the online implementation of an FDD ANNs-based system in an EFI system. The proposed FDD
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx Table 21 Abbreviations. AFR ANN BP CI ECU EFI EGR ELM FDD FDI FFT FPGA LM MIL OBD RMS ROC SI
Air Fuel Ratio Artificial Neural Network Back Propagation Compression Ignition Electronic Control Unit Electronic Fuel Injection Exhaust Gas Recirculation Extreme Learning Machine Fault Detection and Diagnosis Fault Detection and Isolation Fast Fourier Transform Field-Programmable Gate Arrays Levenberg–Marquardt Malfunction Indicator Lamp On-Board Diagnostics Root Mean Square Receiver Operating Characteristics Spark Ignition
Acknowledgments This research was funded by Tecnológico Nacional de México grant number 5720.19-P. Alfredo Namigtle Jiménez would like to thank Dirección General Educación Superior Universitaria (PRODEP) for the support given during the development his Master thesis, as well as Dr. Manuel Adam Medina and Dra. Gloria Lilia Osorio for their invaluable comments. The authors would like to thank Tecnológico Nacional de México, Dirección General Educación Superior Universitaria (PRODEP) and CENIDET for the support to develop this work. José Francisco Gómez Aguilar acknowledges the support provided by CONACyT: cátedras CONACyT para jóvenes investigadores 2014. References [1] Wang N, Karimi HR, Li H, Su S. Accurate trajectory tracking of disturbed surface vehicles: A finite-time control approach. IEEE/ASME Trans Mechatronics 2019;24(3):1064–74. [2] Wang N, Deng Q, Xie G, Pan X. Hybrid finite-time trajectory tracking control of a quadrotor. ISA Trans 2019;90:278–86. [3] Cervantes-Bobadilla M, Escobar-Jiménez RF, Gómez-Aguilar J, GarcíaMorales J, Olivares-Peregrino V. Experimental study on the performance of controllers for the hydrogen gas production demanded by an internal combustion engine. ISA Trans 2018;11(8):2157. [4] León PG, García-Morales J, Escobar-Jiménez RF, Gómez-Aguilar JF, LópezLópez G, Torres L. Implementation of a fault tolerant system for the internal combustion engine’s MAF sensor. Measurement 2018;122(1):91–9. [5] Wang N, Pan X, Su SF. Finite-time fault-tolerant trajectory tracking control of an autonomous surface vehicle. J Franklin Inst B 2019. https://doi.org/ 10.1016/j.jfranklin.2019.05.016. [6] Isermann R, Ballé P. Trends in the application of model based fault detection and diagnosis of technical processes. Control Eng Pract 1997;5(5):709–19. [7] Escobar RF, Astorga-Zaragoza CM, Téllez-Anguiano AC, Juárez-Romero D, Hernández JA, Guerrero-Ramírez GV. Sensor fault detection and isolation via high-gain observers: application to a double-pipe heat exchanger. ISA Trans 2011;50(3):480–6. [8] Alegría-Zamudio M, Escobar-Jiménez RF, Gómez-Aguilar JF. Fault tolerant system based on non-integers order observers: application in a heat exchanger. ISA Trans 2018;80(1):286–96. [9] Filipczyk J. The possibilities of early fault detection of engines. J KONES 2013;20(4):1–6. [10] Rastegari A, Archenti A, Mobin M. Condition based maintenance of machine tools: Vibration monitoring of spindle units. Reliab Maintainab Symp (RAMS) 2017;1(1):1–6.
15
[11] Jafarian K, Mobin M, Jafari-Marandi R, Rabiei E. Misfire and valve clearance faults detection in the combustion engines based on a multi-sensor vibration signal monitoring. Measurement 2018;128:527–36. [12] Xi W, Li Z, Tian Z, Duan Z. A feature extraction and visualization method for fault detection of marine diesel engines. Measurement 2018;116(1):429–37. [13] Jafarian K, Darjani M, Honarkar Z. Vibration analysis for fault detection of automobile engine using PCA technique. In: In 2016 4th international conference on control, instrumentation, and automation (ICCIA). 2016, p. 372–6. [14] Flett J, Bone GM. Fault detection and diagnosis of diesel engine valve trains. Mech Syst Signal Process 2016;72–73:316–27. [15] Jafari SM, Mehdigholi H, Behzad M. Valve fault diagnosis in internal combustion engines using acoustic emission and artificial neural network. Shock Vib 2014;2014:1–9. [16] Moshou D, Natsis A, Kateris D, Pantazi X, Kalimanis I, Gravalos I. Fault detection of fuel injectors based on one-class classifiers, Mod Mech Eng 4 (1) 19–27. [17] Namburu SM, Wilcutts M, Chigusa S, Qiao L, Choi K, Pattipati K. Systematic data-driven approach to real-time fault detection and diagnosis in automotive engines. In: I in 2006 IEEE autotestcon. 2006, p. 59–65. [18] Capriglione D, Liguori C, Pietrosanto A. Real-time implementation of IFDIA scheme in automotive systems. IEEE Trans Instrum Meas 2007;56(3):824–30. [19] Vemuri AT. Learning methodology for failure detection and accommodation. IEEE Control Syst 1995;15(3):16–24. [20] Witczak M. Modelling and estimation strategies for fault diagnosis of nonlinear systems: from analytical to soft computing approaches, Vol. 354. Springer Science & Business Media; 2007. [21] Wong P, Wong H, Vong C, Iong T, Wong K, Gao X. Fault tolerance automotive air-ratio control using extreme learning machine model predictive controller. Math Probl Eng 2015;2015:1–10. [22] Zhang B, Yan J, Tian C. Study on fault diagnosis system of diesel engine fuel injection system based on BP neural network. In: 2008 ISECS international colloquium on computing, communication, control, and management, Vol. 2. 2008, p. 108–12. [23] Chen D, Zhao P. Study of the fault diagnosis method based on RBF neural network. In: 2011 2nd international conference on artificial intelligence, management science and electronic commerce (AIMSEC). 2011, p. 4350–3. [24] Ma X. Research on fault diagnosis of diesel engine fuel injector based on LABVIEW. 3rd International Conference on Information Science and Control Engineering 2016;1064–6, (1). [25] Murphy BJ, Lebold MS, Reichard K, Roemer M, Orsagh R, Schoeller M. Diagnostic fault detection & intelligent reconfiguration of fuel delivery systems. In: 2005 IEEE aerospace conference. 2005, p. 2514–3522. [26] He Y, Feng L. Diesel fuel injection system faults diagnosis based on fuzzy injection pressure pattern recognition. In: Fifth world congress on intelligent control and automation (IEEE Cat. No. 04EX788), Vol. 2. 2004, p. 1654–7. [27] Chandroth GO, Sharkey AJC, Sharkey NE. Cylinder pressures and vibration in internal combustion engine condition monitoring, in: Proceedings of Comadem, Vol. 99, 1999, pp. 294–297. [28] Wu J, Liu C. An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network. Expert Syst Appl 2009;36(3):4278–86. [29] Togun NK, Baysec S. Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks. Appl Energy 2010;87(1):349–55. [30] Liu Y, Zhang J, Ma L. A fault diagnosis approach for diesel engines based on self-adaptive wvd, improved FCBF and PECOC-RVM. Neurocomputing 2016;177:600–11. [31] Ahmed R, El Sayed M, Gadsden SA, Tjong J, Habibi S. Automotive internalcombustion-engine fault detection and classification using artificial neural network techniques. IEEE Trans Veh Technol 2015;64(1):21–33. [32] Ann LY, Ehkan P, Mashor MY, Sharun SM. Fpga-based architecture of hybrid multilayered perceptron neural network. Indones J Electr Eng Comput Sci 2019;14(2):949–56. [33] Ebert T, Bänfer O, Nelles O. Multilayer perceptron network with modified sigmoid activation functions. In: International conference on artificial intelligence and computational intelligence. Berlin, Heidelberg: Springer; 2010, p. 414–21.
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
Contents lists available at ScienceDirect
ISA Transactions journal homepage: www.elsevier.com/locate/isatrans
Practice article
Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle ∗
A. Namigtle-Jiménez a , R.F. Escobar-Jiménez b , , J.F. Gómez-Aguilar c , C.D. García-Beltrán b , A.C. Téllez-Anguiano d a
Posgrado del Tecnológico Nacional de México / CENIDET. Int. Internado Palmira S/N, Palmira C.P.62490, Cuernavaca, Morelos, Mexico Tecnológico Nacional de México / CENIDET. Int. Internado Palmira S/N, Palmira C.P.62490, Cuernavaca, Morelos, Mexico c Conacyt-Tecnológico Nacional de México / CENIDET. Int. Internado Palmira S/N, Palmira C.P.62490, Cuernavaca, Morelos, Mexico d Tecnológico Nacional de México / Instituto Tecnológico de Morelia, Av. Tecnológico 1500. Col. Lomas de Santiaguito. C.P. 58120, Morelia, Mich., Mexico b
article
info
Article history: Received 20 December 2018 Received in revised form 30 October 2019 Accepted 4 November 2019 Available online xxxx Keywords: Electronic fuel injection rail system Fault detection and diagnosis scheme Artificial neural networks FPGA
a b s t r a c t In this research, fault detection and diagnosis (FDD) scheme for isolating the damaged injector of an internal combustion engine is formulated and experimentally applied. The FDD scheme is based on a temporal analysis (statistical methods), as well as in a frequency analysis (fast Fourier transform) of the fuel rail pressure. The arrangement of the scheme consists of three coupled artificial neural networks (ANNs) to classify the faulty injector correctly. The ANNs were trained considering five different scenarios, one scenario without fault in the injection system, and the other four scenarios represent a fault per injector (1 to 4). The Levenberg–Marquardt (LM), BFGS quasi-Newton, gradient descent (GD), and extreme learning machine (ELM) algorithms were tested to select the best training algorithm to classify the faults. Experimental results obtained from the implementation in a VW fourcylinder CBU 2.5L vehicle in idle operating conditions (800 rpm) show the effectiveness of the proposed FDD scheme. © 2019 ISA. Published by Elsevier Ltd. All rights reserved.
1. Introduction Nowadays, automatic control area has become a powerful tool to deal with different types of problems, such as tracking trajectories [1,2], regulation [3], as well as fault diagnosis [4] and fault-tolerant control [5]. In literature, fault detection and diagnosis (FDD), fault detection and isolation (FDI), and faulttolerant control (FTC) systems have been studied by different authors [6–8] since the early diagnosis of a fault allows the operator to perform changes in the system or stop it. Over the years, the internal combustion engine (ICE) has been modified and significantly improved for increasing its efficiency and for satisfying the ecological standards. For improving the ICE performance, new mechanical designs and lighter materials, as well as more instrumentation devices and control strategies have been developed. However, adding more electronic devices to the system increases the probability of failures. Fault diagnosis in sensors and actuators is a complex task to carry out because a failure can cause different symptoms in the internal combustion engine. ∗ Corresponding author. E-mail address: [email protected] (R.F. Escobar-Jiménez).
Regarding the ICEs most commons faults, in [9] the author compiled information of 628 engines (including engines with spark ignition (SI) or compression ignition (CI)), the results showed at least 16 common faults. According to the percentage of occurrence, they can be classified as follows, electrical and ignition control system faults, cooling system faults and, fuel injection system faults. To detect the engine faults the author used different diagnosis methods; the on board diagnostics (OBD) system, measurements of electrical values, organoleptic methods including endoscopy and examining the engine during dynamometer stand tests, however, the author did not consider the fault isolation at the injection system. Over the past few years, several authors have chosen to use signal processing techniques to monitor system failures [10], mainly using vibration signals. Regarding fault detection in a combustion engine, in [11], the authors presented a misfire and valve clearance fault detection based on the multi-sensor vibration signal monitoring (4 sensors), and the Fourier transform to extract the signals features, the fault classification was carried out by three different methods (ANN, support vector machines and K-nearest neighbor), reaching 100% accuracy. Other interesting works based on vibration signals or acoustic emission for fault diagnosis can be found in [12–16].
https://doi.org/10.1016/j.isatra.2019.11.003 0019-0578/© 2019 ISA. Published by Elsevier Ltd. All rights reserved.
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
2
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
Fig. 1. Electronic fuel injection system, the black arrow indicates the fuel flow direction.
According to [17,18] the methods based on signals for sensor fault detection, and sensor fault detection and isolation are very efficient in terms of implementation because they only require information of the input and output signals corresponding to the experimental data, taking into account the defects of interest so that a behavior pattern can be generated and then classified. There are statistical and non-statistical fault classification methods in which artificial neural networks are generally used applying different learning algorithms [19,20], however, some of these approaches require physical or analytical sensor redundancy to carried out the fault diagnosis. In [21], the authors used the kernel extreme learning machine (ELM), to build a backup airratio model with the aim of implement a fault tolerance air-ratio control strategy. Other works related to classifying faults in the fuel injection system have been presented mainly in diesel engines. In [22], a fault diagnosis method was designed for the fuel injection system of a diesel engine. The authors proposed a methodology based on vibration signals analysis. First, the signal feature extraction in the time and frequency domains was carried out. After, the backwards propagation neural network was trained to classify 6 different types of faults. The authors reported a high accuracy of the proposed method. In [23], the authors presented a fault diagnosis method in a MATLAB human–machine environment. This method uses the vibration signal and the pressure waveform signal taken by the vibration sensor in the outer wall of the high-pressure oil tube near the injector. The time-domain characteristic values of both signals such as the root mean square (RMS) value, the variance, the average amplitude, are used as data samples. Finally, a radialbased neural network is used to classify the failures. As in the work above mentioned, the authors in [24] work on a human– machine interface, the purpose of the research was to design a virtual diagnosis system for the fuel injectors in diesel engines. The method consisted of measuring the pressure in the high-pressure pipe and the extracting the features of the temporary signal; after, a neural network was trained to carry out the identification of the type of failure. The results showed the identification of two types of failures. The literature review shows different approaches to carried out the fault detection in several subsystems of the internal combustion engines, which are Model-based, vibration or acoustic analysis-based, or threshold-based. Most of the investigations carried out fault detection or classification in the injectors are focused on diesel engines and did not consider the isolation of the damaged injector. Furthermore, only a few research works
were experimentally tested, and they use two or more measured signals for the fault detection. In the present manuscript, the goal is to detect and isolate a faulty injector using only one measured signal (fuel pressure). However, due to the fuel pressure is regulated by a mechanical regulator, the pressure changes caused by a fault in either one of the four injectors are very difficult to detect in the signal. There are different advantages of isolating the damaged injector, for example, the dead-time of the vehicle caused by a bad diagnosis can be avoided, and it may be possible to design an FCT for improving the engine combustion. The main contributions of this work are the following:
• The design of an FDD scheme based on the extraction of •
•
•
•
features of the pressure signal from the injection rail of an ICE. The FDD is capable of extracting the features of the pressure signal from the injection rail even when the pressure regulator compensates the pressure variation caused by an injector fault. The FDD scheme is capable of detecting and isolating accurately the faulty injector in an electronic fuel injection (EFI) system using an arrangement of three ANNs. It was carried out an offline comparison between four different training methods to select the best one, for its implementation in a vehicle. The FDD scheme was validated online in an EFI system of a four-cylinder VW 2014 CBU 2.5-L classic engine using an field-programmable gate arrays (FPGA) device.
2. Materials and methods The EFI system (see Fig. 1) is one of the most important subsystems of a vehicle. The EFI system includes the fuel tank, the injection rail, the fuel pump, the fuel filter, the fuel injectors, etc [25]. The task of the electronic fuel injection system is feeding the fuel into the cylinders to ensure the combustion process the ICE. The injection pressure contains information about the injection system conditions [26]. Fig. 2 shows the FDD scheme to classify faults in the injector of an EFI system. The FDD is carried out in two steps. In the first step, the offline fault classification is carried out. This step consists of 3 stages. Stage (1a) is dedicated to getting the features vector from temporal and frequency pressure signal. Stage (2a) is dedicated to the offline training of the ANNs. Finally, in stage (3a) is carried out the fault classification. In the second step, the
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
3
Fig. 2. FDD scheme to classify faults in the injector of an EFI system.
online fault classification is carried out. This step consists of 3 stages. Stage (1b) is dedicated to obtain the features vector from the online pressure signal. Stage (2b), the ANNs are implemented in the FPG using the obtained weights, and bias from Step 1-stage (2a) to carry out the fault diagnosis. Finally, in stage (3b) the fault classification considering the fault signatures is carried out. To simplify the understanding of the document Table 21 shows the abbreviations used in this document. 2.1. Time domain features extraction
P =
The features extraction is a fundamental part of designing signal-based fault detection and diagnosis systems because these analysis provide particularities of the signal that are related to the system behaviors. For achieving good results in the design of FDD systems, it is recommended to perform the signal analysis not only in the time domain but also in the frequency domain, separately or in collaboration [27–30]. In this investigation, the feature extraction of the pressure variable is carried out considering the scenarios of interest, such as injection rail without failures in injectors and faults in each one of the four injectors, using time statistical methods assuming that the obtained signal, x(t), is a group of discrete data. The parameter of the signal x(t) territory and the probabilistic density function p(x) have a close relationship [22]. Then, methods such as average (X ) value (Eq. (1)), root mean square (Xrms ) value (Eq. (2)), peakto-peak average (|X |) value (Eq. (3)), and variance (Dx ) (Eq. (4)) are calculated. ∞
∫ X =
xp(x) dx
(1)
−∞
√∫
∞
x2 p(x) dx
Xrms =
(2)
−∞
∫
∞
|X | =
|x|p(x) dx
(3)
−∞
Dx =
1 N −1
The time series analysis is included as an analytical method. For this case, the parametric model is used as a base, the aim is to find a systemic mathematical model adaptable to the time series. From an autoregressive (AR) model, the parameters φ1 and φ2 are obtained as characteristic parameters of the fault diagnosis model [22]. Finally, the power (P) contained in the signal of interest was obtained by using Eq. (5). n 1∑
n
x2 [n]
(5)
i=1
Therefore, C is the feature vector that is represented as shown in Eq. (6), which is applied to the pressure signal including all the scenarios of interest. C = {|X |, Dx , Xrms , Dsx , P , φ1 , φ2 }T
(6)
2.2. Frequency domain feature extraction As mentioned, the data acquisition gathers information corresponding to the pressure signal is time dependent. The information and patterns of this signal cannot be directly appreciated, so it is necessary to gather this information and relate it to the characteristic parameters [15,22,28,31]. To develop the analysis in the frequency domain, the FFT is used (see Eq. (7)). F (µ) =
N −1 1 ∑
N
f (x)e−j2πµx/N
(7)
x=0
where F (µ) and f (x) represent the frequency-domain function, and the time domain function, respectively. Once the pressure signal was analyzed using the FFT, a features vector F, using four windows of 139 ms for each scenario of interest is obtained. The feature vector F is composed by the amplitude of the first 14 frequencies of each signal of interest without considering the first or the fundamental frequency, F = fi T for i ∈ [2; 15]. 2.3. Artificial neural network as an offline fault classifier
N
∑
(xi − |X |)2
i=1
(4)
Once the C and F features vectors were obtained, the neural network is trained in order to isolate the faulty injector. Fig. 3
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
4
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
To develop the present work, an arrangement of three ANNs is proposed, each one of them with the structure above mentioned. As is shown in Fig. 4, one of the ANNs is trained with the characteristic parameters obtained by the time-domain feature extraction, while the other is trained with the feature vector F . The ANNs were trained considering five different scenarios, one scenario without fault in the injection system, and the other four scenarios represent a fault per injector (1 to 4). The LM, BFGS quasi-Newton, GD, and ELM algorithms were tested to select the best training algorithm to classify the faults. The confusion matrix and the receiver operating characteristics (ROC) curve are used to validate the result of the classification of the signal of interest in the considered scenarios.
Fig. 3. Multilayer perceptron neural network model.
shows the general structure of the neural network used to carried out the fault isolation. The multilayer perceptron (MLP) neural network has been used for different authors [32,33]. In [32] was developed an FPGA-based architecture of a hybrid multilayered perceptron neural network, the activation function was sigmoidal. The authors tested the algorithm by simulation. In [33] was proposed an MLP neural network with modified sigmoid activation functions with application in an intake manifold model, the authors use the LM algorithm to carry out the training. where output a2 is given as follows a2 = ϕ2 (LW (ϕ1 (IW (x) + b1 )) + b2 )
(8)
where ϕ1 is a nonlinear activation function defined as follows
ϕ1 =
2 (1 + exp(−2x) )
−1
and ϕ2 is a linear activation function, x is the input data set to the neural network, IW and LW are the synaptic weights generated in the training, b1 and b2 are the bias.
3. Calculations Below are shown the feature extraction of the pressure signals in time-domain and in the frequency-domain used for training the neural networks shown in Fig. 4. 3.1. Time domain features extraction As mentioned in Section 2.1, the feature extraction of the pressure variable was carried out considering the scenarios of interest; without faults and with faults in each one of the four injectors. Fig. 5 shows the signals for all above-mentioned scenarios during the test duration. To validate the proposed FDD system offline an Electronic Fuel Injection Virtual (EFIV) system was programmed in an FPGA device. The aim of programming the EFIV system is to estimate the fuel rail pressure considering the pressure drop caused by
Fig. 4. Neuronal networks final structure.
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
5
Fig. 5. Pressure signal behavior in the scenarios of interest including faults in each injector.
Fig. 6. Data analysis windows.
the engine cycle change. So, to develop the test, the time domain features extraction is carried out from data sets collected from the EFIV, the data sets are called windows, each window contains 139 data, this because of each injection cycle is 139 ms (1 s/ms) (see Fig. 6). It is worth mentioning that each signal represents a different scenario. Tables 1–4 present the results of the feature extraction applying Eqs. (1)–(4) to the five signals shown in Fig. 6 in the first and fourth window, respectively. 3.2. Frequency domain features extraction To develop the analysis in the frequency domain, the FFT Eq. (7) is applied to the five signals. Figs. 7–11 show the amplitude (db) of the implicit frequencies, for each scenario of the signal of interest (without failure and failure in each one of the injectors).
Tables 5–8 show the result of selecting the amplitudes of the frequencies needed to construct the F vector for the first and fourth windows, respectively. 3.3. Evaluation of the training algorithms The FDD scheme was evaluated comparing the LM algorithm with the BFGS quasi-Newton, GD and ELM algorithms. The evaluation results are shown in Table 9. As can be seen, Both, LM and ELM algorithms reached 100% of the fitting. However, the ELM algorithm has more neurons in the hidden layer of the first and second NN (200 neurons-150 neurons) compared to the solution found with the LM algorithm (10 neurons-8 neurons). Thus, the LM algorithm is a better option to be implemented online because the computational cost is lower. Based on the obtained results, it is concluded that the FDD scheme fulfilled a correct classification
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
6
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx Table 1 Pressure signal characteristic parameters in the five scenarios of interest in the first data window.
|X | Dx Xrms Dsx P
φ1 φ2
Without fault
Fault in injector 1
Fault in injector 2
Fault in injector 3
Fault in injector 4
87.9912 1.1323 × 106 1.5676 × 107 1.0641 × 103 5.6317 × 1010 −0.5538 −0.4462
116.6326 6.3405 × 105 2.1058 × 107 796.2732 5.5764 × 1010 −0.6127 −0.3873
100.4738 7.7547 × 105 1.9046 × 107 880.6091 5.5422 × 1010 −0.4523 −0.5477
101.486 8.0831 × 105 1.8430 × 107 899.0606 5.5935 × 1010 −0.5345 −0.4655
104.9361 7.7111 × 105 1.8821 × 107 878.1299 5.6040 × 1010 −0.3958 −0.6041
Table 2 Pressure signal characteristic parameters in the five scenarios of interest in the second data window.
|X | Dx Xrms Dsx P
φ1 φ2
Without fault
Fault in injector 1
Fault in injector 2
Fault in injector 3
Fault in injector 4
97.2434 8.5701 × 105 1.8265 × 107 0.9257 × 103 5.6008 × 1010 −0.5026 −0.4975
104.6128 7.4755 × 105 1.8926 × 107 864.6107 5.5422 × 1010 −0.5496 −0.4503
99.7409 8.5630 × 105 1.7729 × 107 925.363 5.5459 × 1010 −0.6251 −0.3749
110.2729 7.2794 × 105 2.0163 × 107 853.1923 5.6012 × 1010 −0.5176 −0.4824
102.5576 7.9 × 105 1.8929 × 107 888.8192 5.5856 × 1010 −0.6118 −0.3883
Table 3 Pressure signal characteristic parameters in the five scenarios of interest in the third data window.
|X | Dx Xrms Dsx P
φ1 φ2
Without fault
Fault in injector 1
Fault in injector 2
Fault in injector 3
Fault in injector 4
90.4371 9.6692 × 105 1.6852 × 107 983.3215 5.5907 × 1010 −0.4569 −0.5432
93.4662 8.4299 × 105 1.7699 × 107 918.1475 5.5583 × 1010 −0.5798 −0.4203
106.3345 7.5345 × 105 1.9342 × 107 868.0141 5.5539 × 1010 −0.5334 −0.4666
100.8371 7.9547 × 105 1.8943 × 107 891.8912 5.5997 × 1010 −0.5126 −0.4874
102.3930 8.0260 × 105 1.8723 × 107 895.8779 5.5827 × 1010 −0.5473 −0.4526
Table 4 Pressure signal characteristic parameters in the five scenarios of interest in the fourth data window.
|X | Dx Xrms Dsx P
φ1 φ2
Without fault
Fault in injector 1
Fault in injector 2
Fault in injector 3
Fault in injector 4
88.3935 1.0514 × 106 1.6323 × 107 1025.4 5.5847 × 1010 −0.4969 −0.5030
105.3396 7.8990 × 105 1.9020 × 107 888.7626 5.5657 × 1010 −0.4996 −0.5004
109.2819 7.2899 × 105 1.9779 × 107 853.8078 5.5692 × 1010 −0.5189 −0.4811
77.1288 1.2981 × 106 1.4629 × 107 1139.4 5.5971 × 1010 −0.5296 −0.4704
104.6041 7.9544 × 105 1.8667 × 107 891.8769 5.5799 × 1010 −0.6208 −0.3790
Table 5 Pressure signal characteristic parameters in the frequency domain in the five scenarios of interest in the first data window. f2 f3 f4 f5 f6 f7 f8 f9 f10 f11 f12 f13 f14 f15
Without fault
Fault in injector 1
Fault in injector 2
Fault in injector 3
Fault in injector 4
1.4254 × 104 2.7108 × 104 0.9048 × 104 0.6615 × 104 1.1993 × 104 1.3004 × 104 2.0865 × 104 2.0243 × 104 1.2596 × 104 1.9181 × 104 0.5351 × 104 1.0102 × 104 0.3695 × 104 0.1560 × 104
0.7244 × 104 2.2540 × 104 0.8274 × 104 0.9010 × 104 1.0403 × 104 1.8571 × 104 1.2989 × 104 0.2441 × 104 0.7834 × 104 0.5631 × 104 0.7735 × 104 1.5498 × 104 0.7395 × 104 0.5063 × 104
3.1364 × 104 0.4616 × 104 0.6076 × 104 0.9735 × 104 0.6804 × 104 0.6331 × 104 0.8838 × 104 0.3635 × 104 1.2379 × 104 0.2745 × 104 0.2086 × 104 0.7896 × 104 1.3495 × 104 1.1764 × 104
1.2353 × 104 1.0308 × 104 1.4801 × 104 1.4454 × 104 0.9424 × 104 1.5217 × 104 1.4332 × 104 1.1027 × 104 0.1957 × 104 1.7681 × 104 2.0045 × 104 2.3676 × 104 1.1231 × 104 0.8740 × 104
1.4519 × 104 1.4656 × 104 0.2913 × 104 0.4887 × 104 1.2508 × 104 0.4899 × 104 1.4705 × 104 0.3485 × 104 0.2376 × 104 1.0263 × 104 0.5490 × 104 1.3777 × 104 0.5202 × 104 0.1446 × 104
of the fault using the LM algorithm and implemented in the EFIV system programmed in an FPGA (Fig. 12).
Later, the FDD system was validated online in the real EFI system of a 2014 Classic VW four-cylinder engine CBU 2.5-L.
3.4. Implementation of the FDD scheme using an EFIV system programmed in an FPGA
3.4.1. Artificial neural network fault classifier training In this section, the results corresponding to the implementation of the fault detection scheme in the embedded system are shown. It is worth mentioning that the training of the neural network was carried out using the LM method offline, the ANN
Once the ANN scheme was developed, the FDD system was tested using the EFIV system programmed into the FPGA device.
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
7
Fig. 7. FFT applied to the pressure signal in a scenario without failure.
Fig. 8. FFT applied to the pressure signal in a scenario failure in injector 1.
time training was 3 s, then the synaptic weights and the bias were used in the FPGA device. The first step was obtaining the pressure signal from the EFIV system considering four analysis windows of 139 data, afterward, the time and frequency analysis of this signal were performed. It generates a (m × K ) data matrix, where m is the features number (7 for the time analysis and 14 for the frequency analysis), and K correspond to the analyzed windows number (in this case, four windows).
Basically, the information of the pressure signal was collected, the feature vectors were formed, the three artificial neural networks were trained and once the amount of necessary information was obtained (synaptic weights and bias), it was implemented in the neural network. The proposed neural network structure was applied for each scenario of interest. In addition, for implementing the FDD system, the two first artificial neural networks have 10 neurons in the hidden layer.
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
8
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
Fig. 9. FFT applied to the pressure signal in a scenario failure in injector 2.
Fig. 10. FFT applied to the pressure signal in a scenario failure in injector 3. Table 6 Pressure signal characteristic parameters in the frequency domain in the five scenarios of interest in the second data window. f2 f3 f4 f5 f6 f7 f8 f9 f10 f11 f12 f13 f14 f15
Without fault
Fault in injector 1
Fault in injector 2
Fault in injector 3
Fault in injector 4
2.5005 × 104 0.5856 × 104 1.5816 × 104 1.1095 × 104 1.4754 × 104 0.7485 × 104 2.3151 × 104 1.2415 × 104 1.1313 × 104 1.1131 × 104 0.4625 × 104 1.7828 × 104 1.4111 × 104 1.5522 × 104
1.1142 × 104 1.1450 × 104 0.8542 × 104 0.5365 × 104 1.1260 × 104 1.6494 × 104 0.7504 × 104 0.7298 × 104 0.6611 × 104 0.6190 × 104 1.0388 × 104 1.6626 × 104 1.6597 × 104 0.7596 × 104
0.9619 × 104 0.6028 × 104 1.2740 × 104 2.0334 × 104 1.4059 × 104 1.2223 × 104 2.1232 × 104 1.2963 × 104 1.0387 × 104 0.7361 × 104 0.5536 × 104 0.7946 × 104 0.9117 × 104 0.7757 × 104
0.9624 × 104 0.7238 × 104 1.6276 × 104 0.4882 × 104 0.8182 × 104 0.3093 × 104 0.5030 × 104 0.8104 × 104 1.2080 × 104 1.6112 × 104 0.9081 × 104 2.1330 × 104 0.9624 × 104 1.0297 × 104
1.4611 × 104 0.9530 × 104 0.1407 × 104 1.9007 × 104 0.8282 × 104 0.3347 × 104 0.9646 × 104 0.1191 × 104 1.2789 × 104 0.8843 × 104 0.2939 × 104 0.6547 × 104 0.9301 × 104 1.2869 × 104
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
9
Fig. 11. FFT applied to the pressure signal in a scenario failure in injector 4. Table 7 Pressure signal characteristic parameters in the frequency domain in the five scenarios of interest in the third data window. f2 f3 f4 f5 f6 f7 f8 f9 f10 f11 f12 f13 f14 f15
Without fault
Fault in injector 1
Fault in injector 2
Fault in injector 3
Fault in injector 4
2.2645 × 104 1.4536 × 104 2.9384 × 104 1.0126 × 104 1.0345 × 104 1.7095 × 104 1.5474 × 104 1.2642 × 104 1.2165 × 104 0.9953 × 104 1.5201 × 104 0.7752 × 104 0.5384 × 104 0.4398 × 104
2.6965 × 104 2.1354 × 104 0.3436 × 104 1.6217 × 104 0.8135 × 104 1.0836 × 104 0.3221 × 104 0.8450 × 104 1.5403 × 104 1.9222 × 104 0.7546 × 104 1.9269 × 104 0.3101 × 104 0.8670 × 104
1.2967 × 104 0.8111 × 104 0.9160 × 104 0.6811 × 104 1.4496 × 104 1.7223 × 104 0.5726 × 104 1.4080 × 104 2.0547 × 104 0.3538 × 104 0.4314 × 104 1.4376 × 104 1.2347 × 104 0.9112 × 104
1.6830 × 104 1.9128 × 104 0.5580 × 104 0.9104 × 104 1.1778 × 104 0.6577 × 104 1.3723 × 104 0.8845 × 104 1.2971 × 104 1.1709 × 104 0.9364 × 104 1.7342 × 104 0.8541 × 104 0.3806 × 104
2.9717 × 104 1.8015 × 104 0.7732 × 104 0.7235 × 104 1.4642 × 104 1.0854 × 104 1.5927 × 104 1.3711 × 104 0.3630 × 104 1.0727 × 104 0.7274 × 104 0.6549 × 104 0.5904 × 104 0.5575 × 104
Table 8 Pressure signal characteristic parameters in the frequency domain in the five scenarios of interest in the fourth data window. f2 f3 f4 f5 f6 f7 f8 f9 f10 f11 f12 f13 f14 f15
Without fault
Fault in injector 1
Fault in injector 2
Fault in injector 3
Fault in injector 4
0.5755 × 104 0.3314 × 104 1.5804 × 104 0.5755 × 104 1.8458 × 104 1.5002 × 104 0.9640 × 104 0.0749 × 104 1.0660 × 104 0.7847 × 104 0.9424 × 104 1.4163 × 104 1.4016 × 104 0.6131 × 104
3.2515 × 104 1.5875 × 104 0.3840 × 104 0.8611 × 104 0.1444 × 104 0.7111 × 104 0.9824 × 104 0.6695 × 104 0.2814 × 104 0.5791 × 104 1.1952 × 104 0.5643 × 104 1.8356 × 104 0.8917 × 104
2.7401 × 104 0.6812 × 104 1.5729 × 104 2.5693 × 104 0.6393 × 104 2.4766 × 104 0.9707 × 104 1.5655 × 104 0.9079 × 104 1.3202 × 104 0.6799 × 104 0.9735 × 104 0.6970 × 104 0.5827 × 104
0.8951 × 104 0.9893 × 104 2.4537 × 104 1.2023 × 104 0.5217 × 104 1.2920 × 104 2.8100 × 104 1.0282 × 104 1.9608 × 104 0.4815 × 104 1.5817 × 104 1.5622 × 104 3.1319 × 104 0.9460 × 104
1.1991 × 104 1.5039 × 104 1.4809 × 104 1.4678 × 104 1.2510 × 104 1.5635 × 104 1.2939 × 104 0.7672 × 104 0.5787 × 104 0.9755 × 104 1.5319 × 104 0.2520 × 104 1.2629 × 104 1.5739 × 104
For both ANNs, a (K × K ) square matrix with a diagonal of 1’s was proposed as the target output. For the third neural network, 8 neurons were used in the hidden layer, and the outputs of the previous neural networks were considered, resulting in a (2K × K ) matrix as input data in the third neural network.
To carry out the FDD validation, there were previously recorded different tests inducing failures in the injectors. The results of the isolation of each one of the faulty injector are shown below. Table 10 shows the results of the FDD scheme implementation in the EFIV system. The test was carried out considering
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
10
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx Table 9 FDD evaluation using different training algorithms.
Table 10 FDD scheme results when the signal without failures in the injectors.
no fault in the injection system. Table 10 is divided into five
in injectors, and four possibles scenarios of faults, one for each
sections which represent the five possible scenarios, without fault
injector. The result of the test showed the diagonal of 1’s is in
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
11
Table 11 FDD scheme results with a failure in injector 1.
Table 11 shows the results of the FDD scheme implementation in the EFIV system. The test was carried out considering a fault in the injector 1. Table 11 was divided into five sections which represent the five possible scenarios of operation of the injection system, without fault in injectors, and four possibles scenarios of faults, one for each injector. The result of the test showed the diagonal of 1’s is in Section 2 (Fault in injector 1), confirming that the system condition is with a fault in the injector 1. Furthermore, the other four sections do not satisfy the diagonal of 1’s. Table 12 shows the results of the FDD scheme implementation in the EFIV system. The test was carried out considering a fault in the injector 2. Table 12 was divided into five sections which represent the five possible scenarios of operation of the injection system, without fault in injectors, and four possibles scenarios of faults, one for each injector. The result of the test showed the diagonal of 1’s is in Section 3, confirming that the system condition is with a fault in the injector 2. Furthermore, the other four sections do not satisfy the diagonal of 1’s. Table 13 shows the results of the FDD scheme implementation in the EFIV system. The test was carried out considering a fault in the injector 3. The result of the test showed the diagonal of 1’s is in Section 4, confirming that the system condition is with a fault in the injector 3. Furthermore, the other four sections do not satisfy the diagonal of 1’s. Finally, Table 14 shows the results of the FDD scheme implementation in the EFIV system considering a fault in the injector 4. The result of the test showed the diagonal of 1’s is in Section 5, confirming that the system condition is with a fault in the injector 4. Furthermore, the other four sections do not satisfy the diagonal of 1’s. 4. Results
Fig. 12. Artificial neural networks implemented in FPGA.
4.1. Artificial neural network as an online fault classifier in the real EFI system
the scenario without fault in the injectors, confirming that the system condition is without faults. Furthermore, the other four sections do not satisfy the diagonal of 1’s.
This section presents the results of implementing the designed FDD scheme in a 2014 Classic Volkswagen 2.0 engine fourcylinder CBU 2.5-l, fuel engine. For this purpose, an MPX5700 pressure sensor was located in the injection rail (see Fig. 13).
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
12
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx Table 12 FDD scheme results with a failure in injector 2.
Table 13 FDD scheme results with a failure in injector 3.
In this experiment, 40 analysis windows were considered. The pressure sensor was placed on the injection rail of the vehicle and connected to the FPGA device, where the FDD scheme was programmed. Fig. 14 shows the pressure signal in the scenarios of interest obtained in the VW vehicle. Table 15 shows that the proposed neural network structure (see Fig. 4) can guarantee a 100% classification in any possible scenario. After obtaining the correct classification percentages shown in Table 15, the obtained synaptic weights and bias were used in the FPGA device to carry out the online FDD scheme, the execution time of the FDD algorithm was 1 s.
Table 16 shows the results of the FDD scheme performing five tests and considering only the scenario without failure in the injection rail. As can be seen, the classification percentage is higher in the scenario without failure. The results of five tests inducing a fault in injector 1 are shown in Table 17. The results show that the classification percentage is higher in the scenario with failure in injector 1. In Table 18 are shown the results of five tests inducing a fault in injector 2. The results show that the classification percentage is higher in the scenario with failure in injector 2. In Table 19 are shown the results of five tests inducing a fault in injector 3. The results show that the classification percentage is higher in the scenario with failure in injector 3.
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
13
Table 14 FDD scheme results with a failure in injector 4.
Table 16 Online performance of the FDD scheme without failure. Without fault Fault in injector Fault in injector Fault in injector Fault in injector
1 2 3 4
Test 1
Test 2
Test 3
Test 4
Test 5
85% 20% 22.5% 40% 25%
92.5% 30% 25% 20% 17.5%
90% 30% 20% 37.5% 22.5%
87.5% 30% 27.5% 32.5% 25%
90% 27.5% 20% 25% 30%
Table 17 Online performance of the FDD scheme with a failure in injector 1. Without fault Fault in injector Fault in injector Fault in injector Fault in injector
Fig. 13. FDD scheme implemented in the EFI system for a 2014 Classic VW. Table 15 Neural networks offline training results. Training Without fault Fault in injector Fault in injector Fault in injector Fault in injector
1 2 3 4
ANN1
ANN2
ANN3
87.5% 90% 87.5% 87.5% 90%
87.5% 87.5% 87.5% 87.5% 90%
100% 100% 100% 100% 100%
In Table 20 are shown the results of five tests inducing a fault in injector 4. The results show that the classification percentage is higher in the scenario with failure in injector 4. The FDD implementation in a vehicle was carried out satisfactorily. In this experiment, a 40-window time and frequency analyses were considered. Note that all the experiments were carried out at idle speed (approximately 800 rpm).
1 2 3 4
Test 1
Test 2
Test 3
Test 4
Test 5
20% 85% 25% 30% 27.5%
22.5% 87.5% 17.5% 32.5% 25%
27.5% 95% 20% 40% 27.5%
27.5% 85% 25% 25% 12.5%
17.5% 95% 20% 27.5% 27.5%
Table 18 Online performance of the FDD scheme with a failure in injector 2. Without fault Fault in injector Fault in injector Fault in injector Fault in injector
1 2 3 4
Test 1
Test 2
Test 3
Test 4
Test 5
27.5% 22.5% 87.5% 25% 15%
25% 20% 100% 35% 22.5%
20% 25% 82.5% 15% 15%
35% 17.5% 85% 17.5% 27.5%
27.5% 22.5% 100% 25% 20%
Table 19 Online performance of the FDD scheme with a failure in injector 3. Without fault Fault in injector Fault in injector Fault in injector Fault in injector
1 2 3 4
Test 1
Test 2
Test 3
Test 4
Test 5
17.5% 22.5% 17.5% 87.5% 22.5%
22.5% 17.5% 15% 100% 27.5%
32.5% 20% 27.5% 80% 17.5%
25% 17.5% 30% 87.5% 17.5%
17.5% 17.5% 25% 87.5% 22.5%
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
14
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx
Fig. 14. Behavior of the online pressure signal in the scenarios of interest including faults in each injector. Table 20 Online performance of the FDD scheme with a failure in injector 4. Without fault Fault in injector Fault in injector Fault in injector Fault in injector
1 2 3 4
Test 1
Test 2
Test 3
Test 4
Test 5
20% 32.5% 20% 22.5% 95%
25% 25% 27.5% 27.5% 85%
20% 20% 30% 25% 87.5%
10% 15% 27.5% 25% 82.5%
25% 30% 30% 20% 95%
Although the 100% classification was obtained offline, this percentage was not always achieved online. However, the FDD system has shown its effectiveness to isolate the faulty injector.
system is capable of detecting and isolating a damaged injector accurately by the implementation of three ANNs. The ANNs were trained with the LM algorithm and compared versus the BFGS quasi-Newton, GD, and ELM algorithms. The obtained results demonstrate that the LM algorithm is the best option, not only because it reaches a 100% classification offline and near 100% classification online. The FDD scheme was implemented on an FPGA-based and experimentally tested. Future work will focus on proposing a new alternative to isolate failures in the injection system based on the synchrosqueezing transform for the signals features extraction. Declaration of competing interest
5. Conclusions In this paper was presented the online implementation of an FDD ANNs-based system in an EFI system. The proposed FDD
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.
A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al. / ISA Transactions xxx (xxxx) xxx Table 21 Abbreviations. AFR ANN BP CI ECU EFI EGR ELM FDD FDI FFT FPGA LM MIL OBD RMS ROC SI
Air Fuel Ratio Artificial Neural Network Back Propagation Compression Ignition Electronic Control Unit Electronic Fuel Injection Exhaust Gas Recirculation Extreme Learning Machine Fault Detection and Diagnosis Fault Detection and Isolation Fast Fourier Transform Field-Programmable Gate Arrays Levenberg–Marquardt Malfunction Indicator Lamp On-Board Diagnostics Root Mean Square Receiver Operating Characteristics Spark Ignition
Acknowledgments This research was funded by Tecnológico Nacional de México grant number 5720.19-P. Alfredo Namigtle Jiménez would like to thank Dirección General Educación Superior Universitaria (PRODEP) for the support given during the development his Master thesis, as well as Dr. Manuel Adam Medina and Dra. Gloria Lilia Osorio for their invaluable comments. The authors would like to thank Tecnológico Nacional de México, Dirección General Educación Superior Universitaria (PRODEP) and CENIDET for the support to develop this work. José Francisco Gómez Aguilar acknowledges the support provided by CONACyT: cátedras CONACyT para jóvenes investigadores 2014. References [1] Wang N, Karimi HR, Li H, Su S. Accurate trajectory tracking of disturbed surface vehicles: A finite-time control approach. IEEE/ASME Trans Mechatronics 2019;24(3):1064–74. [2] Wang N, Deng Q, Xie G, Pan X. Hybrid finite-time trajectory tracking control of a quadrotor. ISA Trans 2019;90:278–86. [3] Cervantes-Bobadilla M, Escobar-Jiménez RF, Gómez-Aguilar J, GarcíaMorales J, Olivares-Peregrino V. Experimental study on the performance of controllers for the hydrogen gas production demanded by an internal combustion engine. ISA Trans 2018;11(8):2157. [4] León PG, García-Morales J, Escobar-Jiménez RF, Gómez-Aguilar JF, LópezLópez G, Torres L. Implementation of a fault tolerant system for the internal combustion engine’s MAF sensor. Measurement 2018;122(1):91–9. [5] Wang N, Pan X, Su SF. Finite-time fault-tolerant trajectory tracking control of an autonomous surface vehicle. J Franklin Inst B 2019. https://doi.org/ 10.1016/j.jfranklin.2019.05.016. [6] Isermann R, Ballé P. Trends in the application of model based fault detection and diagnosis of technical processes. Control Eng Pract 1997;5(5):709–19. [7] Escobar RF, Astorga-Zaragoza CM, Téllez-Anguiano AC, Juárez-Romero D, Hernández JA, Guerrero-Ramírez GV. Sensor fault detection and isolation via high-gain observers: application to a double-pipe heat exchanger. ISA Trans 2011;50(3):480–6. [8] Alegría-Zamudio M, Escobar-Jiménez RF, Gómez-Aguilar JF. Fault tolerant system based on non-integers order observers: application in a heat exchanger. ISA Trans 2018;80(1):286–96. [9] Filipczyk J. The possibilities of early fault detection of engines. J KONES 2013;20(4):1–6. [10] Rastegari A, Archenti A, Mobin M. Condition based maintenance of machine tools: Vibration monitoring of spindle units. Reliab Maintainab Symp (RAMS) 2017;1(1):1–6.
15
[11] Jafarian K, Mobin M, Jafari-Marandi R, Rabiei E. Misfire and valve clearance faults detection in the combustion engines based on a multi-sensor vibration signal monitoring. Measurement 2018;128:527–36. [12] Xi W, Li Z, Tian Z, Duan Z. A feature extraction and visualization method for fault detection of marine diesel engines. Measurement 2018;116(1):429–37. [13] Jafarian K, Darjani M, Honarkar Z. Vibration analysis for fault detection of automobile engine using PCA technique. In: In 2016 4th international conference on control, instrumentation, and automation (ICCIA). 2016, p. 372–6. [14] Flett J, Bone GM. Fault detection and diagnosis of diesel engine valve trains. Mech Syst Signal Process 2016;72–73:316–27. [15] Jafari SM, Mehdigholi H, Behzad M. Valve fault diagnosis in internal combustion engines using acoustic emission and artificial neural network. Shock Vib 2014;2014:1–9. [16] Moshou D, Natsis A, Kateris D, Pantazi X, Kalimanis I, Gravalos I. Fault detection of fuel injectors based on one-class classifiers, Mod Mech Eng 4 (1) 19–27. [17] Namburu SM, Wilcutts M, Chigusa S, Qiao L, Choi K, Pattipati K. Systematic data-driven approach to real-time fault detection and diagnosis in automotive engines. In: I in 2006 IEEE autotestcon. 2006, p. 59–65. [18] Capriglione D, Liguori C, Pietrosanto A. Real-time implementation of IFDIA scheme in automotive systems. IEEE Trans Instrum Meas 2007;56(3):824–30. [19] Vemuri AT. Learning methodology for failure detection and accommodation. IEEE Control Syst 1995;15(3):16–24. [20] Witczak M. Modelling and estimation strategies for fault diagnosis of nonlinear systems: from analytical to soft computing approaches, Vol. 354. Springer Science & Business Media; 2007. [21] Wong P, Wong H, Vong C, Iong T, Wong K, Gao X. Fault tolerance automotive air-ratio control using extreme learning machine model predictive controller. Math Probl Eng 2015;2015:1–10. [22] Zhang B, Yan J, Tian C. Study on fault diagnosis system of diesel engine fuel injection system based on BP neural network. In: 2008 ISECS international colloquium on computing, communication, control, and management, Vol. 2. 2008, p. 108–12. [23] Chen D, Zhao P. Study of the fault diagnosis method based on RBF neural network. In: 2011 2nd international conference on artificial intelligence, management science and electronic commerce (AIMSEC). 2011, p. 4350–3. [24] Ma X. Research on fault diagnosis of diesel engine fuel injector based on LABVIEW. 3rd International Conference on Information Science and Control Engineering 2016;1064–6, (1). [25] Murphy BJ, Lebold MS, Reichard K, Roemer M, Orsagh R, Schoeller M. Diagnostic fault detection & intelligent reconfiguration of fuel delivery systems. In: 2005 IEEE aerospace conference. 2005, p. 2514–3522. [26] He Y, Feng L. Diesel fuel injection system faults diagnosis based on fuzzy injection pressure pattern recognition. In: Fifth world congress on intelligent control and automation (IEEE Cat. No. 04EX788), Vol. 2. 2004, p. 1654–7. [27] Chandroth GO, Sharkey AJC, Sharkey NE. Cylinder pressures and vibration in internal combustion engine condition monitoring, in: Proceedings of Comadem, Vol. 99, 1999, pp. 294–297. [28] Wu J, Liu C. An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network. Expert Syst Appl 2009;36(3):4278–86. [29] Togun NK, Baysec S. Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks. Appl Energy 2010;87(1):349–55. [30] Liu Y, Zhang J, Ma L. A fault diagnosis approach for diesel engines based on self-adaptive wvd, improved FCBF and PECOC-RVM. Neurocomputing 2016;177:600–11. [31] Ahmed R, El Sayed M, Gadsden SA, Tjong J, Habibi S. Automotive internalcombustion-engine fault detection and classification using artificial neural network techniques. IEEE Trans Veh Technol 2015;64(1):21–33. [32] Ann LY, Ehkan P, Mashor MY, Sharun SM. Fpga-based architecture of hybrid multilayered perceptron neural network. Indones J Electr Eng Comput Sci 2019;14(2):949–56. [33] Ebert T, Bänfer O, Nelles O. Multilayer perceptron network with modified sigmoid activation functions. In: International conference on artificial intelligence and computational intelligence. Berlin, Heidelberg: Springer; 2010, p. 414–21.
Please cite this article as: A. Namigtle-Jiménez, R.F. Escobar-Jiménez, J.F. Gómez-Aguilar et al., Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle. ISA Transactions (2019), https://doi.org/10.1016/j.isatra.2019.11.003.