Fault diagnosis for internal combustion engines using intake manifold pressure and artificial neural network

Expert Systems with Applications 37 (2010) 949–958

Contents lists available at ScienceDirect

Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa

Fault diagnosis for internal combustion engines using intake manifold pressure and artificial neural network Jian-Da Wu a,*, Cheng-Kai Huang a, Yo-Wei Chang b, Yao-Jung Shiao b a b

Graduate Institute of Vehicle Engineering, National Changhua University of Education, 1 Jin-De Rd., Changhua City, Changhua 500, Taiwan, ROC Department of Vehicle Engineering, National Taipei University of Technology, Taiwan, ROC

a r t i c l e

i n f o

Keywords: Intake manifold pressure Discrete wavelet transform Artificial neural network Fault diagnosis

a b s t r a c t This paper describes an internal combustion engine fault diagnosis system using the manifold pressure of the intake system. The manifold pressure of the engine intake system always demonstrates the engine condition and affects the volumetric efficiency, fuel consumption and performance of internal combustion engines. Manifold pressure is well known to be detrimental to engine system stability and performance and it must be considered during regular maintenance. Conventional engine diagnostic technology using manifold pressure in intake system already exists through analyzing the differences between signals and depends on the experience of the technician. Obviously, the conventional detection is not a precise approach for manifold pressure detection when the engine in operation condition. In the present study, a system consisted of manifold pressure signal feature extraction using discrete wavelet transform (DWT) and fault recognition using the neural network technique is proposed. To verify the effect of the proposed system for identification, both the radial basis function network (RBFN) and generalized regression neural network (GRNN) are used and compared in this study. The experimental results indicated the proposed system using manifold pressure signal as data input is effective for engine fault diagnosis in the experimental engine platform. Ó 2009 Elsevier Ltd. All rights reserved.

1. Introduction Owing to the great improvement in microprocessor and electrical control technology in recent years, most road vehicle engines are controlled by an engine management system (EMS) with various sensory systems (Nwagboso, 1993). Most of the sensors are used for monitoring and controlling power plant and transmission. One of the most important parameters is the intake manifold absolute pressure which can sense engine speed and control the fuel mixture. Apart from the manifold absolute pressure, the air mass flow into the engine manifold can be sensed using a hot-wire flow meter. The signals of sensors provide input information to the EMS. It then adjusts the air–fuel mixture based on feedback from the oxygen sensor (Crouse & Anglin, 1993). Unfortunately, both the intake manifold pressure and air mass flow often fluctuate when airleakage in the intake manifold obviously occurs, and the engine volumetric efficiency will be decreased. Engine performance and fuel consumption will also be effected. In the above interpretation, there is a relation with intake manifold pressure and various kinds of engine operating conditions. Due to the progress in technology, vehicles have gradually become a popular form of transportation in people’s daily life. The * Corresponding author. E-mail address: [email protected] (J.-D. Wu). 0957-4174/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2009.05.082

stability and performance of vehicles has been the subject of much attention. Before, the fault diagnosis system often used sound emission or vibration signals analysis in the time–frequency domain, but this method was affected by background noise (Wang & McFadden, 1996). Actually, the recognition rate of the diagnosis system would be decreased by the background noise. The conventional method of identifying engine faults was through observing the intake manifold pressure using an experienced technician when the engine was in an operating condition. Obviously, the traditional technique is not a precise approach for engine fault detection, because some fault conditions could be similar to one another. Some faults might be errors as judged by the technician’s subjective opinion. For signal feature extraction in recent years, many useful techniques for signal analysis have been proposed, such as fast Fourier transform (FFT) (Corinthios, 1971), short time Fourier transform (STFT) (Portnoff, 1980) and wavelet transform (WT). Nonetheless, the different analysis techniques did not adapt the signal of intake manifold pressure. The FFT was just analyzed, and inspection of the signal amplitude difference in frequency domain could not determine instantaneous variation. The STFT provides a fixed-size window in the time–frequency domain. The wavelet transform technique was used for both time and frequency resolutions and it could have an auto adjusted scale to adapt to the signal. The wavelet transform could be divided into continuous wavelet

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transform (CWT) and DWT. In 2008, Sen et al. published a wavelet analysis of cycle-to-cycle pressure variations in an internal combustion engine (Sen, Litak, Taccani, & Radu, 2008). The study is a useful example analyzing the pressure in internal combustion engines with WT. Unfortunately, the CWT took a lot of time for the calculation part and the amount of data was huge (Wu & Chen, 2006). Therefore, the DWT was developed to improve on CWT. The original signal was decomposed into several resolutions by the DWT, so it was easy to examine the instantaneous variations of pressure in all frequency bands. The DWT technique is used in feature extraction for the internal combustion engine fault diagnosis system. Further, both the radial basis function network (RBFN) and generalized regression neural network (GRNN) are used for fault identification. In the following sections, the proposed methods and performance of the fault identification are described. 2. Principles of signal processing and multi-resolution analysis

Wavelet transform is a relatively novel technique of signal processing. Similar to a windowed Fourier transform, a wavelet transform can measure the time–frequency variations in spectral components. It provides a more flexible time–frequency resolution

D1

x(t)

2 2

1 CWTða; bÞ ¼ pffiffiffi a

D2

A1

2 2

A3

…

decomposition reconstruction

/j;k ðtÞ ¼ 2

Dj;k ¼ Table 1 Frequency distribution of approximations. Frequency band (Hz) 0–5000 0–2500 0–1250 0–625 0–312.5 0–156.25 0–78.12 0–39.06 0–19.53

1

  tb dt; a

ð1Þ

Z

þ1



xðtÞw

! t  2j k 2j

1

dt;

ð2Þ

j=2

/

t  2j k 2j

!

! t  2j k 2j

j; k 2 I;

ð3Þ

j; k 2 I:

ð4Þ

Here, the time shift k ¼ 1; 2; . . . ; N=2j and the level j ¼ 1; 2; . . . ; J. J is the maximum level of wavelet transform. Thus, the wavelet transform of xðtÞ can be obtained by Eqs . (5) and (6),

Fig. 1. Decomposition and reconstruction of DWT.

Low-pass filters 1 2 3 4 5 6 7 8 9

xðtÞw

where a and b are replaced by 2j and 2j k. The wavelet technique has particular advantages for characterizing signals at different location levels in time as well as frequency domains. The discrete wavelet transform performs two functions (Graps, 1995) that can be shown as high-pass and low-pass filters. The principle of discrete wavelet transform is shown in Fig. 1. The two-filters are called the wavelet function uðtÞ and scaling function /ðtÞ as follows:

uj;k ðtÞ ¼ 2j=2 u

D3

þ1

where a represents the scale parameter, b represents the time shifting (or translation) parameter and w is the complex conjugate of w. The user can dilate or translate the mother wavelet using a and b. During CWT analysis, the wavelet is shifted smoothly over the full domain of the analyzed signal. It thus calculates the wavelet coefficient at every scale, generating a huge amount of data. Due to the huge amount of data generated through CWT, training classifiers based on its coefficients at different scales can often become cumbersome. The DWT is separate from the CWT (Alsberg, Woodward, & Kell, 1997; Seker & Ayaz, 2003). It adopts the dyadic scale and translation to reduce computation time. The DWT can be defined as

A2 2

Level

Z

1 DWTða; bÞ ¼ pffiffiffiffi 2j

2.1. Principle of wavelet transform

2

and multi-resolution representation. The wavelet transform is defined as the inner product of a signal xðtÞ with the mother wavelet wðtÞ:

AJ;k ¼ Samples ðfn ¼ 10 kHzÞ 1

0  fn =2 0  fn =22 0  fn =23 0  fn =24 0  fn =25 0  fn =26 0  fn =27 0  fn =28 0  fn =29

Table 2 Frequency distribution of details. Level

Frequency band (Hz)

Samples ðfn ¼ 10 kHzÞ

High-pass filters 1 2 3 4 5 6 7 8 9

5000–10,000 2500–5000 1250–2500 625–1250 312.5–625 156.25–312.5 78.12–156.25 39.06–78.12 19.53–39.06

fn =21 fn =22 fn =23 fn =24 fn =25 fn =26 fn =27 fn =28 fn =29

 fn  fn =21  fn =22  fn =23  fn =24  fn =25  fn =26  fn =27  fn =28

Z Z

xðtÞuj;k ðtÞdt;

ð5Þ

xðtÞ/J;k ðtÞdt;

ð6Þ

where Dj;k and AJ;k are called the detailed and approximation/ smooth coefficients respectively. Roughly speaking, AJ;k mainly represents the tardy variation of xðtÞ at the low-frequency band and Dj;k represents the detailed part at the high-frequency band. DWT uses the fact it is possible to resolve high frequency components within a small time window, and only low frequency components need large time windows. This because a low frequency component completes

x1

y1

xm

yk d jk

aj Input layer

Hidden layer

Output layer

Fig. 2. Architecture of radial basis function neural network.

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a cycle in a large interval and a high frequency component completes a cycle in a much shorter interval. Therefore, slow varying components can only be identified over long intervals, but fast varying components can be identified over short intervals.

2.2. Multi-resolution analysis Multi-resolution analysis was first developed by Mallat. The mathematical model of MRA (Mallat, 1989; Yang & Leu, 2008) can be defined as follows:

intake manifold pressure

Input

-50 mormal one injector fault two injectors fault intake air-leak intake plugged

-55

Initialization Initialization

Feed Feed forward forward

Gain Gainneuron neuron

Kpa

-60 -65 -70

Check Checkconvergence convergencecondition condition

-75 -80 500

Result

1000

1500

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2500

3000

rpm

Fig. 3. Flow chart of the radial basis function neural network training procedure.

Fig. 6. Intake manifold pressure with various conditions. n

⎡ Di 2 ⎤ 2 ⎥ ⎣ ⎦

∑ exp ⎢ - 2σ =1

i

∑

x1

x2

y( x)

xp−1

∑

xp

n

⎡ Di 2 ⎤ 2 ⎥ ⎣ ⎦

∑ yexp ⎢- 2σ =1

Input layer

Pattern layer

i

Summation layer

Output layer

Fig. 4. Architecture of generalized regression neural network.

Fig. 5. Experimental setup of engine fault diagnosis system.

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V jþ1 ¼ W j  V j ¼ W j  W j1      W jk  V jk ;

ð7Þ

where V j is the approximate version which decomposed the signal with the original signal at scale j; W j is the detailed version showing the transient appearance of the original signal at scale j;  is a summation of two decomposed signals and k is the number of the decomposition level. The multi-resolution analysis can be applied to decompose the signal xðtÞ into various scales of the orthogonal signal section. They are the detailed sub-signal Dj ðtÞ and the

a

Dj ðtÞ ¼ AJ ðtÞ ¼

0.01 0

5 0 -5

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0

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0.01 0 -0.01

0.01 0 -0.01

ð8Þ

AJ;k /J;k ðtÞ J; k 2 I:

ð9Þ

-5

-5

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-3

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D6

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dj;k uj;k ðtÞ j; k 2 I;

and the AJ ðtÞ contents an approximate frequency band of

5 0 1000

k X

h i The Dj ðtÞ contents an approximate frequency band of fS =2jþ1  fS =2j

5 0 0

X

k

normal D1

-0.01

Magnitude

approximation sub-signal AJ ðtÞ, which represent the components of xðtÞ at different resolutions, calculated as follows:

-0.02

0.05 0 -0.05

0.9 0.85 0

1000

2000

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0.8

3000

Sample number

b

5 0 -5

Magnitude

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x 10

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-3

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2000 D7

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D6

2000 A9

3000

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5000

1.2 1.1 1

0

1000

2000

Sample number Fig. 7. Approximate and detailed coefficients which decomposition to nine levels by DWT with various faults in idle: (a) normal, (b) one injector fault, (c) two injectors fault, (d) intake air-leak and (e) intake plugged.

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h

i 0  fS =2Jþ1 , here fS is the sampling frequency. The original signal xðtÞ

can be recovered in terms of these sub-signals with different scales:

xðtÞ ¼ AJ þ

X

Dj ;

3. Principles of artificial neural network models 3.1. Radial basis function network

ð10Þ

j6J

where AJ ðtÞ and Dj ðtÞ express the approximation and the detailed signal of the Jth level. Suppose the sample rate is 10 kHz, the signal in the frequency domain is analyzed by 2n down-sampling. Tables 1 and 2 show the frequency distribution of the details and approximations.

c

two injectors fault D1 0.01 0 -0.01

5 0 -5 Magnitude

The RBFN (Pulido, Ruisanchez, & Rius, 1999; Stubbings & Hunter, 1999) is a feed-forward neural network, which is comprised of three different layers: one input layer, one hidden layer and one output layer. The network architecture is shown in Fig. 2. Here, the RBFN has m input signals composing an input vector that are sent to a hidden layer composed of RBFN neural

0 x 10

1000 -3

0

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D2

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d

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D3 0.02 0 -0.02

D6

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2.3 2.2 2.1

0

Sample number Fig. 7 (continued)

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units. The third layer is the output layer, and the transfer functions of the neurons are the linear units. The transformation from the inputs space to the hidden space is non-linear. Connections between the input and the hidden layers have unit weights. The hidden layer of the RBFN has several forms of nonzero activation functions. The node calculates the Euclidean distance between the center and the network input vector and then passes the result to the radial basis function. The basis function for the jth hidden node is often defined by a Gaussian exponential function shown as follows:

aj ¼ aðv j Þ ¼ exp 

v 2j 2r2j

! ð11Þ

;

where rj is the width of the jth neuron, v j is presented by the Euclidean norm of the distance between the input vector and the neuron center calculated as follows:

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u r uX v j ðxÞ ¼ kx  cj k ¼ t ðxi  cj;i Þ2 ;

i ¼ 1; 2; . . . ; r;

ð12Þ

i¼1

where cj is a center of the jth RBFN unit. And the output value is defined as

yk ¼

s X

djk aj ;

ð13Þ

j¼1

where yk , the kth element of the y, is the output of the kth node in the output layer, djk is the weight from the jth hidden layer neuron to the kth output layer neuron, and aj is the output of the jth node in the hidden layer. The output layer simply consists of linear summation units with a linear activation function. In the training process, the output target of the hidden node associated with the cluster is ‘‘1” for most samples. For the other samples, this target is ‘‘0”. The flow chart of the RBFN training procedure is shown in Fig. 3. The RBFN could automatically determine the number of neurons in the hidden layer during the procedure. If the result does not contain the convergence conditions, the network system will add a neuron to the

e

5 0 -5

Magnitude

5 0 -5

x 10

0 x 10

0

-3

hidden layer and the feed-forward stage is repeated until the convergence conditions are contained. 3.2. Generalized regression neural network In the common artificial neural networks, the back-propagation neural network is the most basic type and the most representation neural network. BPNN has many flaws such as: existence of a local minimum, improper learning rate and requiring a large number of iterations to achieve convergence. The GRNN speed is very quick because it does not require an iterative training for converging to a wanted solution. The GRNN was first proposed by Specht (Specht, 1991). Fig. 4 show the block diagram of the GRNN architecture. It is one-passing learning algorithm, which can be used for estimating continuous variables such as some transient content in intake manifold pressure signals. By definition, the regression of a dependent variable y on an independent variable x computes the most probable value for y, given x and a training set. The f ðx; yÞ represents the known joint continuous probability density function of a vector random variable, x, and a scalar random variable x. The conditional mean of y given X is given by

R1 yf ðX; yÞdy E½yjX ¼ R1 1 f ðX; yÞdy 1

When the density f ðx; yÞ is unknown, it must usually be estimated from the sample of observations of x and y. The probability estimator f ðX; YÞ is based on sample values X i and Y i of the random variables x and y, where n is the number of sample observations and p is the dimension of the vector variable x:

^f ðX; YÞ ¼

intake plugged D1

-3

1000

2000

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5000

1

-2

x 10

0

-3

1000

D2

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D9

0.05 0

0.1 0 0

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0

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D5

2000 A9

0.02 0 -0.02

2000

D8

D4

-0.05

D6

D7

D3 0.02 0 -0.02

1

ð2pÞðpþ1Þ=2 rðp þ 1Þ n " # " # n X ðX  X i ÞT ðX  X i Þ ðY  Y i Þ2  exp  ; exp  2r2 2r 2 i¼1

1.6 1.55 0

1000

2000

3000

4000

5000

ð15Þ

where r is the smoothing parameter, the scalar function D2i defined as

2 0 1000

ð14Þ

1.5

0

Sample number Fig. 7 (continued)

1000

2000

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a

normal D1 0.01 0

0

-0.01

-5

5

Magnitude

5

0 1000 -3 x 10

2000

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D2 5

0

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Fig. 8. Approximate and detailed coefficients which decomposition to nine levels by DWT with various faults in 3000 rpm: (a) normal, (b) one injector fault, (c) two injectors fault, (d) intake air-leak and (e) intake plugged.

D2i ¼ ðX  X i ÞT ðX  X i Þ;

ð16Þ

and performing the indicated integration yields

  D2 exp  2ri 2 ^ ¼ YðXÞ   : Pn D2i i¼1 exp  2r2 Pn

i¼1 Y

i

ð17Þ

When the smoothing parameter r is large, the estimated density is forced to be smooth and the limit becomes a multivariate Gaussian with covariance r2 I. On the other hand, a smaller value of r allows

the estimated density to assume non-Gaussian shapes, but with the hazard wild points may have a significant effect on the estimate. 4. Experimental study and classification of intake manifold pressure signals 4.1. Experimental work In the experimental investigation, the intake manifold pressure signals of engine were recorded and analyzed to prove the

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c

2

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x 10

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Fig. 8 (continued)

proposed fault diagnosis system. The pressure signals were transited using pressure sensors and recorded using a data acquisition system. Afterward, we analyzed and classified the signals using DWT and an artificial neural network. In the signal analysis, the signals can be decomposed into 9 levels by DWT, and can be used to detect faults. The purpose of neural network is to find the same engine fault condition under different operation conditions. The experimental flowchart of the engine fault diagnosis system is shown in Fig. 5. The equipment for the experiment included an internal combustion engine (Ford L type, four-stroke, four cylinders, 1.6-L injection engine), pressure sensor and a data acquisition

system (NI-9215) with 10 kHz sampling frequency. The experiment comprises five engine fault conditions and five engine rotating speeds. The five engine conditions are the normal engine condition, one injector fault, two injectors fault, intake air-leak and intake plugged. The engine is run in an idle condition (800 rpm), 1000 rpm, 1500 rpm, 2000 rpm and 3000 rpm. 4.2. Experimental results and fault classification In the time domain signal, it is very difficult to detect the transient signal in the engine fault condition. The intake manifold pres-

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e

5

x 10

-3

intake plugged D1 5

0

0

-5

-5

5

0 1000 -3 x 10

2000

3000

4000

5000

1000

D6

2000

0 2000

3000

4000

5000

0

1000

2000

D3 Magnitude

4000

5000

3000

4000

5000

3000

4000

5000

3000

4000

5000

3000

4000

5000

0.02 -0.02

D8

0.02

0.05

0

0

-0.02

-0.05

0

1000

2000

3000

4000

5000

D4 0.02

5

0

0

-0.02

-5

5

3000 D7

-5

1000

0

-3

D2

0 0

x 10

0 1000 -3 x 10

2000

3000

4000

5000

0

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1000

D9

A9 1.72 1.7

-5

1.68

1000

2000

D5

0 0

0 1000 -3 x 10

2000

3000

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5000

0

1000

2000

Sample number Fig. 8 (continued)

Table 3 Performance of fault recognition using RBF and GRNN in various faults and operation conditions. RBF

Normal (%) One injector fault (%) Two injectors fault (%) Intake air-leak (%) Intake plugged (%) Averaged recognition rate (%) Training time (s)

GRNN

Idle

1000

1500

2000

3000

Idle

1000

1500

2000

3000

100 100 100 100 100

100 100 100 100 75

100 100 100 100 100

100 100 100 100 100

100 100 100 100 100

100 100 100 100 100

100 100 100 100 100

100 100 100 100 100

100 100 100 100 100

100 100 90 100 100

99 7.4

sures in the time domain of various fault conditions are summarized in Fig. 6. Note the intake manifold pressure of the two injector faults and normal condition are closed. In the proposed system, after recording pressure signal, they could be decomposed into nine levels by the DWT analysis technique. The nine levels were composed of nine high frequency bandwidths and one low frequency bandwidth. For example, Figs. 7 and 8 indicated the approximate and detailed coefficients which decomposed into nine levels by DWT with various faults in the engine operated at idle and 3000 rpm conditions. Unfortunately, there is still no easy way to point out faults from the person’s viewpoint and it is not easy to detect faults from the information. Therefore, the feature vectors of engine faults are summarized as inputs of the neural network and established as the database. However the selection of the feature vector was adapted to the engine speed. For example, for the feature vector of the idle condition 1000 rpm and 1500 rpm, the nine high frequency bandwidths and one low frequency bandwidth were chosen. The feature vector of 2000 rpm and 3000 rpm only chose the nine high level bandwidths. There are 200 data sets for each fault condition. The 120 data sets are used for training and 80 data sets are used to test the recognition rate of the proposed network. The performance of the fault diagnosis system is evaluated by the recognition rate which is defined as

99 0.46

Recognition rate ¼

Correct classification samples  100%: Total testing of samples

ð18Þ

As summarized in Table 3, all the evaluation results had over 95% recognition rates in various engine operation conditions. The experimental results show the DWT fault diagnosis system with GRNN and RBF can be effectively used in engine fault diagnosis of various faults through measuring the engine intake manifold pressure signal. 5. Conclusion A fault diagnosis system of the internal combustion engine based on the intake manifold pressure is proposed. The diagnosis procedure consisted of feature extraction using discrete wavelet transform and classification using artificial neural networks. The approach improves the conventional flaw of too much reliance on the experience of technicians. In this system, the features of intake manifold pressure signal at different resolution levels are extracted by multi-resolution analysis without losing their original properties. The experimental results indicated the proposed fault diagnosis system with two different neural networks can be effectively used in engine fault diagnosis. However, GRNN can

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accomplish the training procedure in a short time and achieve a satisfactory recognition rate in the experimental platform. References Alsberg, B. K., Woodward, A. M., & Kell, D. B. (1997). An introduction to wavelet transforms for chemometricians: A time–frequency approach. Chemometrics and Intelligent Laboratory System, 37, 215–239. Corinthios, M. J. (1971). A fast Fourier transform for high-speed signal processing. IEEE Transactions on Computers, C20, 843–846. Crouse, W. H., & Anglin, D. L. (1993). Automotive mechanics. New York: McGraw-Hill. Graps, A. (1995). An introduction to wavelets. IEEE Computational Science and Engineering, 2, 50–61. Mallat, S. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 674–693. Nwagboso, C. O. (1993). Automotive sensory system. London: Chapman & Hall. Portnoff, M. (1980). Time–frequency representation of digital signal and system based on short-time Fourier analysis. IEEE Transactions on Acoustic, Speech and Signal Processing ASSP, 28, 55–69.

Pulido, A., Ruisanchez, I., & Rius, F. X. (1999). Radial basis functions applied to the classification of UV–visible spectra. Analytica Chimica Acta, 388, 273–281. Seker, S., & Ayaz, E. (2003). Feature extraction related to bearing damage in electric motors by wavelet analysis. Journal of the Franklin Institute, 340, 125–134. Sen, A. K., Litak, G., Taccani, R., & Radu, R. (2008). Wavelet analysis of cycle-to-cycle pressure variations in an internal combustion engine. Chaos, Solitons and Fractals, 38, 886–893. Specht, D. F. (1991). A general regression neural network. IEEE Transactions on Neural Networks, 2, 568–576. Stubbings, T., & Hunter, H. (1999). Classification of analytical images with radial basis function networks and forward selection. Chemometrics and Intelligent Laboratory Systems, 49, 163–172. Wang, W. J., & McFadden, P. D. (1996). Application of the wavelet transform to gearbox vibration signals for fault detection. Journal of Sound and Vibration, 192, 927–939. Wu, J. D., & Chen, J. C. (2006). Continuous wavelet transform technique for fault signal diagnosis of internal combustion engine. NDT & E International, 37, 411–419. Yang, T. Y., & Leu, L. P. (2008). Multi-resolution analysis of wavelet transform on pressure fluctuations in an L-value. International Journal of Multiphase Flow, 34, 567–579.