Misfire and valve clearance faults detection in the combustion engines based on a multi-sensor vibration signal monitoring

Accepted Manuscript Misfire and Valve Clearance Faults Detection in the Combustion Engines Based on a Multi-Sensor Vibration Signal Monitoring Kamal Jafarian, Mohammadsadegh Mobin, Ruholla Jafari-Marandi, Elaheh Rabiei PII: DOI: Reference:

S0263-2241(18)30343-9 https://doi.org/10.1016/j.measurement.2018.04.062 MEASUR 5463

To appear in:

Measurement

Received Date: Revised Date: Accepted Date:

24 November 2017 14 March 2018 16 April 2018

Please cite this article as: K. Jafarian, M. Mobin, R. Jafari-Marandi, E. Rabiei, Misfire and Valve Clearance Faults Detection in the Combustion Engines Based on a Multi-Sensor Vibration Signal Monitoring, Measurement (2018), doi: https://doi.org/10.1016/j.measurement.2018.04.062

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Misfire and Valve Clearance Faults Detection in the Combustion Engines Based on a Multi-Sensor Vibration Signal Monitoring

Kamal Jafarian Petroleum University of Technology, Ahwaz, Iran E-mail: [email protected] Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

Mohammadsadegh Mobin Department of Industrial Engineering and Engineering Management, College of Engineering, Western New England University, Springfield, MA 01119, USA E-mail: [email protected] Ruholla Jafari-Marandi Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA E-mail: [email protected]

Elaheh Rabiei B. John Garrick Institute for the Risk Sciences, University of California, Los Angeles (UCLA), Los Angeles, USA E-mail: [email protected]

Misfire and Valve Clearance Faults Detection in the Combustion Engines Based on a Multi-Sensor Vibration Signal Monitoring

Research Highlights 

Engine misfire and valve clearance faults detection considering signal analysis;



Propose an integrated approach in the signal transformation and feature extraction;



Utilization of the statistical analysis in the selection of significant features;



Application and comparison of machine learning algorithms in the fault diagnosis;



Engine fault diagnosis considering various scenarios and different features.

Abstract Fault diagnosis of the rotary machines is investigated through different kinds of signals. However, the literature shows that the vibration signal analysis is the most commonly used and effective approach. This research investigates the engine faults, including the misfire and valve clearance faults, using the vibration data captured by four sensors placed in different locations of the automobile engine and under different experimental circumstances. The application of the Fast Fourier Transform (FFT) is proposed as a feature extraction methodology which leads to the extraction of 16 features. In addition, four features are extracted using the acquired signals eigenvalues. The statistical approach is proposed to select features for classification of the engine’s state. The Artificial Neural Networks (ANN), Support Vector Machines (SVM), and k Nearest Neighbor (kNN) classification algorithms are employed to predict if the motor works healthily based on the selected features and, if not, what kind of faults is in the engine. The performance of ANN, SVM, and kNN in fault

diagnosis is analyzed considering different scenarios, features, and based on multiple performance metrics. Comparing the results with the similar efforts in the literature proves the validity of the proposed methods and highlights their superiorities.

Keywords: Fault Diagnosis, Fast Fourier Transform (FFT), Artificial Neural Networks (ANN), Support Vector Machines (SVM), k Nearest Neighbor (kNN)

1. Introduction The reliability of rotary engines is essential, as they have become an integral part of many industries and businesses such as transportation and power suppliers. An automated monitoring and control of engines will undoubtedly create opportunity in enhancing the reliability of the engines. An intelligent detection and classification of various faults is an important step toward an online and efficient monitoring and control system. Fault diagnosis and detection in the rotary machines have been extensively investigated in many studies in the past few years. Fault diagnosis of the bearing has been studied by Batista, Badri et al. (2013), Zhao, Jin et al. (2014), and Shi and Liang (2016). Some other efforts investigated the gear box system fault diagnosis: Li, Zhang et al. (2011), Hajnayeb, Ghasemloonia et al. (2011), and Feng, Lin et al. (2016). Moreover, Chen, Matthews et al. (2013), Yin, Wang et al. (2014), Sun, Zi et al. (2014), and Teng, Ding et al. (2017) investigated the fault diagnosis and detection of the wind turbine system. In order to investigate the failure of the rotary machines, researchers have considered and monitored different kinds of signals, such as the vibration signal, acoustic emission signal, current and voltage signals, and performance and condition monitoring, among others. Oh, Shibutani et al. (2012) estimated the health of the cooling fans by considering the acoustic noise estimation, shaft rotational speed, and current consumption. Also, Oh, Azarian et al. (2011) considered the acoustic emission signals to estimate the health condition of fan bearings and predict their life before failures. More recently, Li, Sanchez et al. (2016) employed the deep random forest fusion of acoustics and the vibratory signals to study the fault diagnosis of gear box systems. Niu, Widodo et al. (2008) used transient stator current signal for fault diagnosis of induction motors. Eren, Aşkar et al. (2016) employed a fourchannel FIR filter bank to study a motor current signatures. Wang and Hussin (2009)

proposed an approach which takes the condition of lubrication systems into consideration to estimate the residual time of a marine diesel engine. Among the current approaches in the fault diagnosis of rotary machines, the vibration signals processing is the most popular and effective method for analyzing diagnostic features (Harmouche, J., C. Delpha and D. Diallo (2015), Jack, L. and A. Nandi (2000), and Heng, R. and M. J. M. Nor (1998)). Chow and Hai (2004) developed a wavelet transform-based method to extract features from the vibration faulty signals and diagnose the machine fault when operating at different rotating speeds. Gan, Zhao et al. (2009) developed a fault detection system that extracts feature vectors from the power spectra of machine vibration signals for induction of machine fault detection. Caesarendra, Niu et al. (2010) showed that the increased vibration level in a rotating machine indicates a degradation condition. Randall and Antoni (2011) provided a comprehensive tutorial about the diagnostic analysis of vibration signals obtained from accelerometers with the purpose of diagnosing the rolling element bearing faults. Wang and Hussin (2009) proposed a Taguchi-based approach to fault diagnosis of rolling element bearing. They used time/frequency domain analysis to extract features from the vibration data. Signal processing approaches in terms of different vibrations can be categorized into three main groups. The first group includes approaches with the time-domain analysis, such as works done by Heng and Nor (1998), Martin and Honarvar (1995), Lipovszky, Sólyomvári et al. (1990), and

ag l skis,

rka skas et al. (1989). The second group includes works

which considered the frequency domain analysis, such as Courrech (2000), Miao, Cong et al. (2011), and Miao, Azarian et al. (2011). The third group employs the time-frequency analysis, among others Peng and Yam (2001), and Wang and Hussin (2009). The frequencydomain analysis is the most commonly employed approach and has proved to be effective

and computationally non-demanding in providing essential information that leads to more salient frequency-domain features (Gan, Zhao et al., 2009). Recently, the engine monitoring and fault detection methodologies are significantly improved. Engines need to operate in various conditions, and the usage of online/offline monitoring systems allows improvement of performance. In-cylinder combustion quality is one of the most important parameters for describing engines’ condition, especially for the Internal Combustion (IC) engines. The misfiring happens because of a lack of in-cylinder combustion, and that may occur due to inappropriate ignition and poor/rich air-fuel ratio. The literature includes different approaches for the investigation of faults in the combustion engine such as studying mixture components of in-cylinder or exhaust gas for their types and quantity, engine block vibrations, and engine speed fluctuations. For instance, Devasenapati, Sugumaran et al. (2010) applied the induction learning power of decision trees to identify the misfire fault of the engine in a four-stroke four-cylinder petrol engine. Sharma, Sugumaran et al. (2014) trained the decision trees using vibration signal data to detect misfire in an Internal Combustion (IC) engine. More recently, Moosavian, Khazaee et al. (2015) showed the applicability of sensor data fusion and combination of different classifiers through the theory of belief functions to study the spark plug fault recognition. This paper uses the signal variation data when they are gathered under various circumstances from different sensors in the engine to investigate engine faults. The data obtained in this research are time domain signals; therefore a transformation tool called Fast Fourier transform (FFT), developed and applied by Peng and Chu (2004), is used to convert the time domain data into frequency spectra. The transformation allows further analysis of vibration and better feature extraction. A statistical approach is adopted to select features to be used in the training of three different classifiers: Artificial Neural Networks (ANN), Support Vector Machines (SVM), and k Nearest Neighbor (kNN). The performances of these

different techniques are studied under different scenarios when using multiple sets of features to predict various types of faults. The performance of the proposed diagnosis approach is compared with recent efforts in the literature through common evaluation metrics. The rest of the paper is structured as follows. Section 2 introduces the research framework, which explains different steps throughout the research along with the methods that are employed. Section 3 explains the experimental designs for data collection to study the proposed research goals. Section 4 presents the results on the performance of the techniques. Finally, Section 5 offers concluding remarks and sets a few future research trends.

2. Research Methodology In this research, the fault of the combustion engines is studied through an integrated approach. Figure 1 summarizes the proposed approach, which is comprised of seven steps. First, the experimental design and setting are done, and the required data are gathered. At the level of pre-processing, the high frequency noise data are identified and rejected. In the next step, using the spectral and eigenvalue analysis, twenty features are extracted from each acquired data. Then, the essential features are selected using statistical analysis. Three classification techniques including ANN, SVM, and kNN are trained using the selected features and class labels to classify fault diagnosis in the combustion engine. Three classification performance metrics, including accuracy, sensitivity, and specificity metrics, are measured to evaluate the performance of these classifiers. Insert Figure 1 here.

2.1. Feature Extraction

Feature extraction is an essential part of any intelligent classification system. While many use different approaches to deal with the challenge of the appropriate feature extraction, there has not yet been a valid, reliable, and methodical approach to extract features for the vibration signals analyses. Among others, this could be due to the difference of data types in various studies. The paper adopts frequency analysis, which is also called spectral analysis, as it has been employed by many researchers in the area of fault diagnoses including Courrech (2000), Miao, Azarian et al. (2011), Miao, Cong et al. (2011), Harmouche, Delpha et al. (2015), and Jafarian et al. (2016). The Fourier Transformation is an essential part of the frequency analysis. The Fourier Transform (FT) converts a signal from its time domain into the frequency domain using a set of mathematical functions. Transformed data reveals information such as contained frequencies and the amplitude of each frequency for a raw signal. For proper usage of the frequency analysis, it is important to note that the main application of Fourier Transform is when the signal has the stationary behavior. In case a signal is non-stationary, the behavior of the signal changes over the time. That is to say, the Fourier Transform of a signal from t0 to t1 can be different from its Fourier Transform from to

. In fact, although the Fourier Transform performs effectively when the signal is

stationary, it cannot provide the information about the emergence of each frequency when a given signal is non-stationary. The base of Fourier Transform is that each periodic function with

period can be presented in a set of the sine and cosine terms. Equations 1 to 3 present the Fourier Transform and its coefficients in which t

represents time and

and

are the k-th coefficients.

(1)

(2)

(3)

Equation 4 shows Fourier Transform that is defined using imaginary numbers as a conveyor between the time and frequency domains. Note that

in Equation 4 presents the

angular velocity value.

(4)

Equations 1 to 4 represent the continuous format of Fourier Transform. Similar to almost all recent experiments in the literature, the experiment conducted in this research contains discrete data because of provided digital instrumentation systems. Fast Fourier Transform computes the Discrete Fourier Transform (DFT) of a digital signal. Considering the signal points as

, the DFT of the signal can be defined 5 to 7.

(5)

(6)

(7)

In this research, the recorded vibration signals in the time domain are converted into the frequency domain using FFT. Next is the identification of two dominant frequencies in each signal, which are the corresponding peaks of the frequency domain with the highest amplitude. Therefore, the amplitude and frequency of each dominant peak can be considered as the frequency-based extracted features of the signal. In total, this process introduces four features, including two frequencies and two amplitudes, from each spectrum of the vibration signal. Since there are four sensors in our investigations, there will be 16 features. In addition, the eigenvalue of four vibration sensors is calculated and considered as additional features. Eigenvalues have summarizing quality as they find the direction of change with the highest variance (Shlens 2014). The eigenvalue can be calculated using and the transpose of matrix

, where

is a 4*4 matrix, which can be obtained by multiplying

by matrix , which includes four vibration signals in its columns.

By solving the determinant equations, four eigenvalues of the matrix

can be obtained,

which are considered as additional features in fault diagnosis in our case study. In effect, each of the four eigenvalues is the summarized variable of four feature data from each sensor that has the highest value of variance of those features that could be presented in one variable. 2.2. Feature Selection Feature selection, unlike feature extraction, deals with eliminating some features to make sure that the features with a high level of similarity will not be used at the same time (Guyon and Elisseeff, 2003). Failure to do so may lead to unnecessary computational complexity and training biases due to excess of the same pattern in different features. In the literature, there are different approaches for feature selection. Examples include the decision tree approach by Sugumaran, Muralidharan et al. (2007), principal component analysis by Malhi and Gao (2004), regression-based methods by Breheny and Huang (2011), Self-Organizing Map

(SOM) by Jafari-Marandi, Khanzadeh et al. (2017), and optimization-based approaches by Jack and Nandi (2000). Moreover, the statistical analysis, utilized by Chaves, Ramírez et al. (2009) and Zhou and Wang (2007), is realized to be the most commonly used approach in this area of research. To this end, the features obtained from the feature extraction step are used to conduct several statistical-based pair-wise comparisons among the fault classes to see whether classes have the same value per each feature. If, statistically, there is no significant difference between two classes for a feature, it means that the feature will be eliminated since it cannot contribute to the training of classifiers. Consequently, only the significant features that will uniquely help classifier to find patterns for predictions will be selected. Paired t-test in MINITAB software is used in this research for the statistical pair-wise comparison analysis. 2.3. Fault Classification Fault classification in this research is the process of creating an intelligent system that will be able to predict if an engine is working healthily or faultily based on the extracted and selected features of the working engines. Three most commonly used classification approaches are employed and compared for the fault diagnoses of the internal combustion engine. A well-known method for dividing the data into train and test sets, i.e., the K-fold cross validation method (Devasenapati, Sugumaran et al., 2010, Moosavian, Khazaee et al., 2015 ), is applied to train all three classification techniques applied in this research. In the Kfold cross validation method, the original samples are randomly partitioned into k subsamples with equal sizes. As it is suggested in the related machine learning literature, the number of folds is considered as 5 (K = 5), which means each subsample consists of 20% of the data. One single subsample is retained for testing while the remaining 4 subsamples (80% of the data) is considered as the training set. This process is repeated 5 times and each of the subsamples is used exactly once as the validation set.

2.3.1. Artificial Neural Networks Artificial neural networks method, which is inspired by the brain nervous system, is a powerful tool to map a set of input variables to a set of output variables. The Multi-Layer Perceptron (MLP), also known as feedforward ANN, is one of the most effective artificial neural network structures since it can solve the non-linear separable classification problems and approximate the continuous function. A network of neurons with three defined and heavily connected layers constructs the structure of a Feedforward ANN. ANN consists of input and output layers as well as one or several hidden layers. There are multiple neurons in each layer and each neuron in one layer is connected to all the neurons in the immediate next layers (thus, feedforward). Input layers neuron represents the predicting features of a classification task, while the output layer represents the class label(s) (Jafari-Marandi, Khanzadeh et al., 2017). Backpropagation is the learning power of ANN that changes the randomly initiated weights for each connection on the structure of ANN to bring the network’s expectation for the class of each instance and the actual class of the instance. This process is called the neural network training or backpropagation. One of the most widely used backpropagation algorithms in the training process of a neural network is the Levenberg-Marquardt algorithm which is an improvement to the Newton’s method (Hagan and Menhaj, 1994). Equation 8 presents the formula to calculate the weight changes that will lead the expectation of ANN to move nearer to actual class labels. These changes are calculated based on the partial derivative value and the neurons weight change in the previous epoch. Here, , , , and

,

are, respectively, the average of all the squared errors (popularly used for

ANN to calculate the general difference between the expectation of the network and the actual value of the classes), the weight of the connection between th neuron and th neuron,

the learning rate, the momentum rate, and the epoch number. Learning rate determines the allowed level of change at each epoch of training. On the other hand, momentum rate ensures to include the past weight changes in the direction that the current change will take.

(8)

After some initial experimentations, we observed neural networks with one hidden layer that has 30 neurons is the most successful in the classification of faults. The Tangent Sigmoid transfer function, Levenberg-Marquardt algorithm and gradient less than lower than 10-6 are set, respectively, for the activation functions for the hidden layer neurons, the network training backpropagation algorithm, and network stoppage criteria. 2.3.2. Support Vector Machines Support vector machine, introduced in 1963 by Cortes and Vapnik (1995), is a supervised classification method originally designed for the binary classification. The key element in the SVM method is to obtain a hyperplane which divides the d-dimensional data into two classes (Faziludeen and Sabiq, 2013). SVM finds optimal distinct hyperplanes by mapping the input vectors nonlinearly into a high-dimensional feature space. The method uses the boundaries of nonlinear classes to construct linear models to estimate the decision function. If classes are linearly separable, SVM creates the optimal hyperplane with the maximum distance between the hyperplane and the data points closest to the hyperplane. Support vectors are the data points with the minimum distance to the hyperplane (Keramati, Jafari-Marandi et al., 2014). Assume a set of known objects, i.e., training set, in which each object has a feature vector and a corresponding class value. The learning algorithm utilizes the training data and provides decision function to classify the unknown input data (Suykens and Vandewalle, 1999). There are training data points represented as ( , data point has

inputs denoted by

), when is data point index. Each

, and a class label with two possible values,

. Therefore, all hyperplanes in the hyperplane, noted as

, and a constant value, noted as , where these two parameters can

be presented as:

. Considering this hyperplane

the function

which separates the data,

can correctly classify the training data. This given

hyperplane, which is represented by for

can be parameterized by a vector orthogonal to

, can be also similarly presented by all pairs

. The canonical hyperplane is defined in a way that separates the data

from the hyperplane by a distance, which should be at least 1, i.e., those that satisfy for all data points. This geometric distance between the hyperplane and a data point can be calculated by Equation 9, where

is used to normalize the distance

(Faziludeen and Sabiq, 2013).

(9)

Finally, the hyperplane that maximizes the distance is obtained by minimizing ||W|| subject to the distance constraints. Details of the described calculations are presented by (Suykens and Vandewalle, 1999). Although the SVM tools are mostly used for binary classifications, they can also be modified, using the One Against One (OAO) approach (Hsu and Lin, 2002), to be used in a case with more than two classes. The OAO approach considers one SVM for each pair of classes. Therefore, for a classification with

classes, there will be

trained

SVMs. In this research, the OAO algorithm was applied to the RBF kernel and the best classification rate was reached with the optimal parameter

and

.

2.3.3. k-Nearest Neighbor k-Nearest Neighbors is one the most applied non-parametric learning algorithms. kNN is known as a lazy algorithm; that is, all training data are used at testing phase. There is no

training phase for kNN, and all data points are used directly in testing. kNN method works locally by considering the majority vote of its neighboring data points (Keramati, JafariMarandi et al., 2014). kNN assigns new unclassified data points into a class in which the majority of the data point’s k nearest neighbors belong to, where k represents the number of neighbors. Provided a test data, denoted as , kNN finds the

nearest neighbors of , which are among

the training data and scores the candidate categories based upon the category of considering the similarity of

neighbors,

and each neighboring data point as the score of the category.

When several data points from the

nearest neighbor belong to the same category, kNN

considers the summation of the score of that category as the similarity score of the category. Finally, the candidate category with the highest score will be assigned to the test data x (Lei and Zuo, 2009). kNN is proven as an effective method of classification when the number of samples is large; however, its performance is highly depended on the number of the nearest neighbor k. It is a challenge to find the optimal value of k. Trial and error approach is normally used to tune the method for this parameter. It is experienced that the parameter may assume different values for better performance in the case of different studies. However, as it is mentioned in the literature, the value of

should be close to the square root of , when

represents the

number of training data in a class. The detailed steps of the kNN algorithm from (Jiang, Cai et al., 2007) is listed as follows: 1) choose , 2) calculate the distance, 3) sort the distance in the ascending order, 4) find the research, after trial and error,

class values, and 5) find the dominant class. In this

is adjusted to be 3. Also, the linear nearest neighbor, which is

based on the Euclidean distance function, is used in kNN for the search algorithm.

3. Experimental setup and data acquisition

Three faults related to the misfire in the Overhead Valve (OHV) automobile engine are analyzed. An automobile manufacturing company is considered that manufactures the OHV engine, which is a 1600 cc, linear-four-cylinder and four-stroke engine with eight valves. Three investigated faults have negative effects on the combustion performance in an automobile engine: the slight misfire fault, the severe misfire fault, and abnormal valve clearance, i.e., excessive clearance of valve train. The slight misfire fault happens when there is the spark plug misfire in one cylinder among four cylinders, and therefore, there is no combustion in the cylinder. When there is the spark plug misfire in two cylinders, the severe misfire fault happens. In order to simulate these two faults in cylinders, we cut the wires in the first and second cylinders. The cutting of only one will cause the first fault and their simultaneous cutting will lead to the second. The valve clearance is defined as a gap between the rocker arm and the valve seat (also known as valve cap). In order to create the abnormal valve clearance fault, there are two options: the tight clearance and excessive clearance, which happens, when the clearance is smaller or larger than that specified by the manufacturer, respectively. These faults are due to the incorrect positioning of the valve’s rocker arm or valve tappet adjusting screw, or the worn or spalled cam or roller in the engine. In this research, the excessive clearance is only investigated. In order to create the excessive clearance, as shown in Figure 2, the exhaust (smoke) valve clearance is set 0.6 mm, using the feeler gauge, which is larger than its normal clearance (0.3 mm).

Insert Figure 2 here In order to monitor the vibration signals (data acquisition), four one-direction, piezoelectric, CTC accelerometers are used. Throughout this paper, they are referred to as sensors. The sensors are the products of Sweden, designed to work appropriately between -

500 to 1210 C and each weighs 90 grams. There is one sensor installed under each plug in each cylinder by a magnet. The resonant frequency for each sensor, reported by its manufacturer, is 35 kHz; therefore, the upper limit of operation frequency can be about 20 kHz. The ADASH4400 is used to record data. This instrument has four AC channels, four DC channels, as well as an action channel with the capabilities such as presenting the wave and frequency domain of vibration, advanced analysis of vibration, and saving the data in the internal memory. Because of that, we are able to export saved files to comma separated value (CSV) format for further analysis. It is noteworthy that some efforts in the literature focus on a high sample rate for the data acquisition. For example, Flett and Bone (2016) considered 48 kHz, which can be converted to the encoder resolution equal to 1440 pulses/revolution using Equations 10 and 11, where N is rotational speed of engine’s crankshaft in PM.

(10)

(11) However, in most of the research papers in the related literature, the sampling rate is not as high as what is considered in Flett and Bone (2016); therefore, the encoder resolution normally has higher numbers. For instance, Kiencke (1999) considers the encoder pulse as 6 degrees; Osburn, Kostek et al. (2006) and Jung, Eriksson et al. (2015) use 30 degrees; and Naik (2004) applies 90 degrees. In this research, we consider 2000 RPM, and the sample rate is adjusted at 2 kHz to have 60 pulses/revolution using Equations 10 and 11. This corresponds to an encoder pulse every 6° of crank angle. The adjusted encoder resolution is a moderate number, i.e., not very low (Naik 2004) nor very high (Flett and Bone 2016) according to the setting used in the literature.

Using the described data collection settings, five different situations are studied in total. Table 1 introduces these situations and the abbreviation used for them throughout this paper.

Insert Table 1 here 4. Results and Discussion Collected data go through pre-processing, feature extraction, and feature selection before classification is possible. As an example for the nature of the collected data, Figure 3 shows the recorded signal for 0.2 seconds with the four accelerometers (sensor) in two situations: H and M2. In this research, 30 automobile engines are investigated and sensor signals are recorded for only 1 minute since longer recording could cause permanent damage to the engine. The data recorded during the first and the last 5 seconds of the experiment are eliminated to handle the noise generated by the data acquisition system. The rest of 50 seconds are divided into five 10-second parts. Insert Figure 3 here One can see in Figure 3 that the obtained raw signals have some high-frequency artifacts. In order to reject these high-frequency noises, a fourth-order low-pass filter is used. The cutting frequency of the designed filter is adjusted to be 100 Hz. Figure 4 illustrates the denoised signals of Figure 3. Insert Figure 4 here Figure 5 depicts the described feature extraction for the 16 features extracted from all the sensors. Features are presented by a cross (×) in Figure 5. Table 2 presents all extracted features values and their mean and standard deviation for each class. In addition, the last four features in Table 2 are the calculated eigenvalues for the acquired multi-channel signal. In

total, there are 16 features directly extracted from sensor data and 4 processed features obtained based on the multi-channel eigenvalue approach. Table 2 also shows the mean and standard deviation of all the 5 situations presented in Table 1. In Table 2, the descriptions of features are presented in the second column where DF denotes the dominant frequencies, S denotes the sensor, and (Amp.) represents the corresponding amplitude of the dominant frequencies. For instance, 2nd DF S1 (Amp.) indicates the second dominant frequency of the first sensor. Insert Figure 5 and Table 2 here As explained in section 2.2, the statistical analysis approach is employed to find the significant features to be used for classification training. A pair-wise comparison for all the possible combinations of the five situations is conducted to find which features have more distinguishing information. Table 3 presents the results. Each 0 means the feature cannot be significantly distinguished between the selected situations, and each 1 indicates the opposite. For instance, in Table 3, the bolded and underlined number one shows that feature 2 can significantly distinguish between H and M12. All the statistical tests shown in Table 3 are performed under type one error tolerance of 5 percent (

). When P-value is less than

, we reject the null hypothesis. It means that two classes are significantly different. Based on the result of statistical analysis, features number 2, 4, 9, 10, 11, 12, 13, 14, 15, and 16 are found to be significant features in the category of frequency domain features. Not surprisingly, all eigenvalue features, i.e., features number 17 to 20, are found to be significant as well. Insert Table 3 here Three different classification techniques are used for fault diagnosis analysis of engine under different scenarios and considering all features or selected features.

Six scenarios are developed to compare the performance of classification techniques for different fault diagnosis, considering different versions of features. Table 4 lists these six scenarios in detail. For instance, the first scenario is when only misfire faults are considered and classifiers use all the 20 features for training. Insert Table 4 here All the classification tasks in this paper are evaluated with three performance metrics designed for binary classification. This is the case as the only matter of interest is to study the distinguishability power of the proposed approach for different cases such as, if the engine working healthily or not. Table 5 displays the value of performance metrics for the three classification techniques after being applied in all presented scenarios (Table 4). Three different performance metrics are employed: Accuracy, Sensitivity, and Specificity. The reason is that in most of the binary classification, there is a difference between the two types of misclassifications, and researchers are interested in knowing the preference of method for allowing the type of misclassifications (Keramati, Jafari-Marandi et al., 2014).

In binary classification, each prediction may lead to four difference cases: True Positive (TP), True Negative (TN), False Positive (FP) and False Negative (FN). True and false represent if the class prediction has been correct or not, while positive and negative represent whether prediction has detected fault (positive) or not (negative). Equations 12 to 14 present the equations of three performance metrics. Accuracy is the ratio of truly classified data points. Accuracy does not prefer any type of error. However, sensitivity is only concerned with the number of fault detections, i.e. it calculates how many of the existing faults data instances are predicted correctly. On the other hand, specificity is only concerned with the healthy data instances.

(12)

(13)

(14)

Insert Table 5 here Table 6 shows the average of classifiers performance when all the features are used versus when only selected features are employed. P-value of t-test hypothesizing equal mean for feature-selected experiments and all-features experiments without assumption of equal variance is 0.27. At the confidence level of 5 percent, this hypothesis is rejected, leading to prove the improving impact of the proposed feature selection in this paper. Moreover, on average, as presented in Table 5 and Table 6, ANN, kNN, and SVM have performed 3 percent better when evaluated by specificity over when evaluated by sensitivity. This shows that these methods predict healthy engine condition more successfully than they detect faults. Insert Table 6 here The classification results obtained by the classification algorithms considering all scenarios show that SVM outperforms kNN and ANN in terms of all performance metrics, i.e., the average accuracy, sensitivity, and specificity. In general, in the scenarios that only significant features are considered in the classification of data, i.e., scenarios 2, 4, and 6, all three algorithms provide better classification results considering all performance metrics. It proves the significant contribution of feature selection in the proposed methodology of this research. Table 7 provides a comparison for the results of this paper and similar research efforts in the literature. Different research efforts, as shown in the literature review, have used different types of data for classification tasks. Since the accuracy reported in all similar work

in the literature is rounded with no decimal points, the results represented from this paper are also rounded. In addition, different faults can be investigated individually and simultaneously. Insert Table 7 here The accuracy values provided in Table 7 are the best values for the accuracy metric presented at the reviewed papers and obtained in our research. In this paper, SVM showed the best performance among all three experimented techniques for scenarios 1 and 2, so the reported 98% accuracy in Table 7 refers to obtained accuracy of SVM. It should be mentioned that SVM algorithm outperforms other algorithms for all the scenarios. In addition, all the best values of accuracy are the ones that selected features are used. This validates that feature selection approach used in this research can improve the accuracy of fault classification. Comparing our results with the results from Flett and Bone (2016) reveals our paper excels in accuracy prediction by almost 1 percent, from 99% to 99.83%. This is particularly significant because Flett and Bone (2016) used high sampling frequency for data acquisition which is 24 times greater than what we have considered. In addition, the feature selection process in our paper is computationally less expensive. It should be noted that when only one-cylinder data is investigated in this research, the obtained accuracy is 100% in the case of all three classification techniques.

5. Conclusion In this paper, an integrated approach is proposed to diagnose three possible faults in the combustion procedure of the automobile engine. Four sensors placed in different parts of automobile engine capture raw vibrational data. FFT, feature extraction, and feature selection approaches prepared the data for different fault diagnosis experiments of the combustion engines. The experiments are designed to investigate the distinguishability power of the

proposed method for three types of faults: slight misfire fault, the severe misfire fault, and abnormal valve clearance. ANN, the SVM, and the kNN algorithms are utilized in the automatic fault diagnosis process. The performance of ANN, SVM, and kNN is analyzed in the fault diagnosis considering various scenarios, features, and based on multiple performance metrics. This paper is the first research effort in the related literature to consider both single and double-cylinder misfire and abnormal valve clearance in fault diagnoses of the combustion engine while vibration signals are captured and analyzed. It also is the first to investigate different scenarios in fault diagnosis of the combustion engine, while individual and simultaneous faults are considered in one-cylinder and two-cylinders. Moreover, a computationally inexpensive spectral-based feature extraction and a feature selection using statistical approach are proposed and applied in this paper. While normally discrete wavelet transformation is applied in the related literature, we showed the applicability of FFT in the feature extraction process. A comprehensive comparison analysis of results obtained from this paper and similar works in the literature is provided. It is shown that the proposed approach is more accurate since the calculated accuracy in different scenarios is higher that the accuracy values provided in the literature. The performance of above 94 percent accuracy for all of the experiments in this paper proves the validity and reliability of the proposed approach. The proposed approach not only proves its capability in detection of the healthy engine working, but also reveals its fault type detection power. It also proves to be successful when compared against all-feature-included experiments, which shows the benefit of using the proposed feature selection approach. In addition, it confirms the applicability of vibrational analysis for fault detection of internal combustion engines. Furthermore, it validates the finding in the literature and shows directions for improvements of predictions.

The proposed research methodology encourages development of vibrational fault detection systems for automatic monitoring and control of engine. Both high-risk and low risk industries may be able to use the proposed fault diagnosis. High-risk industries are probably more interested in successful detection of various faults (higher sensitivity); lowrisk industries will seek the detection of healthy working engine (higher specificity). While our presented method shows higher specify, success of fault classification is apparent, as the smallest sensitivity is 95%. In addition, with decision-making criteria driven from the expectation of industries form engines, the study has created the foundation for development of a cost-sensitive classification approach where the right balance of sensitivity and specificity for each engine can be adjusted. The research framework and the presented results may lead to more research in the future. First, we only showed the distinguishability power of the proposed framework for three types of faults. The successful proposed framework can be used for the detection of more and other faults. Second, in this research we only applied three cost-insensitive classification methods. The application of cost-sensitive classification methods adds to the literature of misfire fault detection. As we experience a 3 percent average gap between specificity and sensitivity, a cost-sensitive classification can help construct a balance between the two or lead to better sensitivity if desired. Also, we suspect that false positives (detect faulty by mistake) are less expensive as they would only result in further inspections. However, false negatives (detect healthy by mistake) should be much more expensive since they may lead to higher cost such as permanent damage to the engine and unforeseen consequence of working with a faulty engine. Using the extracted and selected features from this paper, one can study the advantages of applying cost sensitivity in the classification of misfire faults.

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Tables

Table 1: The description of the studied situations N. 1 2 3 4 5

Situation when the engine works healthy without any faults when there is misfire fault is cylinder number 1 when there is misfire fault is cylinder number 2 when there is simultaneous misfire fault in cylinders 1 and 2 when there is abnormal valve clearance

Abb. H M1 M2 M12 VC

Table 2: Features values for each class N. 1

Description 1st DF S1

H 43.52±4.16

M1 40.54±4.13

M2 40.38±4.26

M12 23.78±8.01

VC 52.52±5.04

2

2nd DF S1

23.27±2.67

26.74±14.87

34.99±17.12

22.82±7.56

19.55±17.14

3

1st DF S2

43.8±4.51

40.56±4.05

40.16±3.8

16.14±1.56

52.06±4.78

4

2nd DF S2

24.18±2.2

11.84±4.19

21.25±12.88

37.46±8.38

32.41±26.29

5

1st DF S3

43.4±4.33

40.61±4.06

40.43±4.16

17.99±7.82

52.55±4.78

6

2nd DF S3

23.78±2.16

20.08±2.25

20.22±1.92

29.89±8.57

23.79±2.38

7

1st DF S4

23.7±2.45

39.84±4.21

40.25±3.67

22.43±7.56

40.76±12.29

8

2nd DF S4

43.65±4.18

19.86±2.07

20.04±1.79

23.82±7.81

31.87±12.82

9

1st DF S1 (Amp.)

1245.57±136.37

1228.47±127.26

1267.92±136.48

643.6±71.33

1243.78±137.08

10

2nd DF S1 (Amp.)

260.6±73.43

145.87±43.62

226.23±45.56

581.51±75.98

229.82±73.34

11

1st DF S2 (Amp.)

1063.64±114.06

1117.05±113.27

1144.23±113.42

554.7±91.54

915.27±95.13

12

2nd DF S2 (Amp.)

369.59±65.59

235.4±34.12

208.51±30.96

212.98±40.8

109.72±32.55

13

1st DF S3 (Amp.)

953.77±118.3

1287.96±123.11

1300.56±128.41

312.93±50.19

880.49±89.65

14

2nd DF S3 (Amp.)

381.37±61.04

374.53±58.43

360.52±54.71

254.76±41.8

420.06±55.49

15

1st DF S4 (Amp.)

926.03±104.87

1209.57±127.83

1159.97±120.75

398.66±58.24

641.76±81.68

16

2nd DF S4 (Amp.)

741.61±76.79

598.11±77.14

595.42±73.37

364.37±50.63

599.9±74.6

17

Eigenvalue #1

13295.23±1300.56

13375.58±1395.61

15928.05±1705.75

9813.93±1104.01

11882.95±1285.39

18

Eigenvalue #2

14669.94±1782.64

17041.7±1676.61

16816.43±1807.47

14499.42±1729.86

15120.01±3324.7

19

Eigenvalue #3

35872.96±3977.4

24759.86±2407.77

33955.46±5072.44

23971.4±2700.15

27141.94±6280.7

20

Eigenvalue #4

58607.14±5439.2

57634.61±5640.51

53783.01±5540.28

31276.18±3522.05

52479.56±6153.77

Table 3: Pair-wise comparison for feature selection Classes H vs. M1 H vs. M2 H vs. M12 H vs. VC M1 vs. M2 M1 vs. M12 M1 vs. VC M2 vs. M12 M2 vs. VC M12 vs. VC Sum of 1s

1 0 0 0 0 0 0 0 0 0 0 0

2 0 0 1 1 0 1 0 1 1 0 5

3 0 0 0 0 0 0 0 0 0 0 0

4 0 0 0 0 1 1 1 1 1 1 6

5 0 0 0 0 0 0 0 0 0 0 0

6 0 0 0 0 0 0 0 0 0 0 0

7 0 0 0 0 0 0 0 0 1 0 1

8 0 0 0 0 0 0 0 0 1 0 1

9 0 1 1 0 1 1 1 1 1 1 8

10 1 0 1 1 1 1 1 1 1 0 8

Features 11 12 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 10 9

13 1 1 1 1 1 0 1 1 1 1 9

14 0 1 0 1 1 0 1 1 1 1 7

15 1 1 1 1 1 1 1 1 1 1 10

16 1 1 1 1 1 0 0 1 1 0 7

17

18

19

20

1 1 1 1 1 1 0 1 1 1 9

1 1 1 1 0 1 1 1 1 1 9

1 1 1 1 1 1 1 1 0 1 9

0 1 1 1 1 1 1 1 1 1 9

* Note: 1 denotes that two classes are different based on the investigated feature; 0 otherwise.

Table 4: Developed scenarios to investigate classification techniques

Scenarios 1 2 3 4 5 6

Misfire fault * * * *

Faults Abnormal clearance

Features All

Significant

* * * * * *

* * * *

Table 5: Performance metrics for the classification techniques considering 6 scenarios

Scenario 1 2 3 4 5 6 Average

Accuracy (%)

ANN Sensitivity (%)

Specificity (%)

Accuracy (%)

KNN Sensitivity (%)

Specificity (%)

Accuracy (%)

SVM Sensitivity (%)

Specificity (%)

96.46 96.77 98.79 98.98 94.67 95.80 96.91

94.56 95.02 97.99 98.87 91.11 93.00 95.09

98.82 98.92 99.60 99.66 98.22 98.60 98.97

96.65 96.94 98.27 99.15 95.42 95.69 97.02

95.00 95.31 97.15 98.59 92.36 92.81 95.20

98.88 98.98 99.42 99.72 98.47 98.56 99.01

97.51 98.04 99.73 99.83 96.79 97.34 98.21

96.38 97.03 99.61 99.72 94.65 95.57 97.16

99.17 99.35 99.91 99.94 98.93 99.11 99.40

Table 6: Average results of classification with selected features vs. all features

Scenario All Features Selected Features

Accuracy (%)

ANN Sensitivity (%)

Specificity (%)

Accuracy (%)

kNN Sensitivity (%)

Specificity (%)

Accuracy (%)

SVM Sensitivity (%)

Specificity (%)

96.64 97.18

94.55 95.63

98.88 99.06

96.78 97.26

94.84 95.57

98.92 99.09

98.01 98.40

96.88 97.44

99.34 99.47

Table 7: Comparison of our results with the similar works in the literature Similar works

Signals

Faults One cylinder misfiring Two cylinders misfiring

Accuracy % 92 97

(Wu and Liu 2009)

Sound Emission Signal

(Devasenapati, Sugumaran et al. 2010)

Vibration Signal

One cylinder misfiring

95

(Boudaghi, Shahbakhti et al. 2015)

ECU Information

One cylinder misfiring Two cylinders misfiring

94 97

(Li, Mi et al. 2013)

Vibration Signal

(Shatnawi and AlKhassaweneh 2014)

Sound Emission Signal

Abnormal Valve Clearance One cylinder misfiring Two cylinders misfiring

98 100 90

(Sharma, Sugumaran et al. 2014)

Vibration Signal

One cylinder misfiring

89

(Moosavian, Khazaee et al. 2015)

Both Vibration And Sound Signal

One cylinder misfiring

98

(Flett and Bone 2016)

Vibration Signal

Abnormal Valve Clearance

99

This paper

Vibration Signal

One cylinder misfiring

100

This paper

Vibration Signal

Two cylinder misfiring

100

This paper (Scenarios 1 and 2)

Vibration Signal

One cylinder misfiring Two cylinders misfiring

98

This paper (Scenarios 3 and 4)

Vibration Signal

Abnormal Valve Clearance

100

This paper (Scenarios 5 and 6)

Vibration Signal

One cylinder misfiring Two cylinders misfiring Abnormal Valve Clearance

97

Figures

Figure 1: A schematic summary of research methodology

Figure 2: Implementing excessive clearance using the feeler gauge

(3.a) Raw signal for the normal engine

(3.b) Raw signal for the engine with M2 fault Figure 3: Raw signal

(4.a) Denoised signal for the normal engine

(4.b) Denoised signal for the engine with M2 fault Figure 4: Denoised signals

(5.a) Extracted features for the normal engine

(5.b) Extracted features for the engine with M2 fault Figure 5: Extracted features