Research Article Research on Fault Diagnosis System of a Diesel Engine Based on Wavelet Analysis and LabVIEW Software

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1 Research Journa of Appied Sciences, Engineering and Technoogy 7(18): , 214 DOI:1.1926/rjaset ISSN: ; e-issn: Maxwe Scientific Pubication Corp. Submitted: November 4, 213 Accepted: November 13, 213 Pubished: May 1, 214 Research Artice Research on Faut Diagnosis System of a Diese Engine Based on Waveet Anaysis and LabVIEW Software 1,2 Eidam Ahmed Hebie, 1 Zhu Zhou, 1 Dong Sheng Wang, 1 Liu Jie, 1 Mohamed Ahmed Ebashier, 1 Wen Dongdong, 1 Li Peng and 1 Li Xiaoyu 1 Department of Agricutura Engineering Automation and Measurement Technoogy Research Section, Coege of Engineering, Huazhong Agricutura University, Wuhan, China 2 Department of Agricutura Engineering, Facuty of Natura Resources and Environmenta Studies, University of Western Kordofan, Enahoud, Sudan Abstract: Experiment presented in this study, used vibration data obtained from a four-stroke, 295 diese engine. Faut of the interna-combustion engine was detected by using the vibration signas of the cyinder head. The faut diagnosis system was designed and constructed for inspecting the status and faut diagnosis of a diese engine based on discrete waveet anaysis and LabVIEW software. The cyinder-head vibration signas were captured through a piezoeectric acceeration sensor, that was attached to a surface of the cyinder head of the engine, whie the engine was running at two speeds (62 and 13 rpm) and two oads (15 and 45 N m). Data was gathered from five different conditions, associated with the cyinder head such as singe cyinder shortage, doube cyinders shortage, intake manifod obstruction, exhaust manifod obstruction and norma condition. After decomposing the vibration signas into some of the detais and approximations coefficients with db5 mother waveet and decomposition eve 5, the energies were extracted from each frequency sub-band of heathy and unheathy conditions as a feature of engine faut diagnosis. By doing so, norma and abnorma conditions behavior coud be effectivey distinguished by comparing the energy accumuations of each sub-band. The resuts showed that detection of faut by discrete waveet anaysis is practicabe. Finay, two techniques, Back-Propagation Neura Network (BPNN) and Support Victor Machine (SVM) were appied to the signa that was coected from the diese engine head. The experimenta resuts showed that BPNN was more effective in faut diagnosis of the interna-combustion engine, with various faut conditions, than SVM. Keywords: Diese engine, faut detection, interna-combustion engine, LabVIEW, vibration signa, waveet anaysis INTRODUCTION Diese engine is a main component in machinery and represents the heart of any vehices. Therefore, it has gained great attention in the fied of condition monitoring. For many decades, the ony way of diagnosing and ocating fauts was by using bioogica senses. At that time, everything was based on observing, istening, smeing and touching different parts of the system. Later, a greater fow of accurate faut information was enabed by introducing measuring equipment and nowadays computers made possibe of a dramatic progress in faut detection and identification. Faut detection in interna-combustion engines often requires the understanding of the dynamic and chemica processes that taking pace inside an engine, which are difficut to mode accuratey (Chandroth and Staszewski, 1999). Most recenty, diese engines have expected to meet both fue-economy requirements and poutant emissions reguations, which are continuousy becoming more rigorous. Good fue and air mixture preparation is a key factor in meeting these requirements. Among the common interna-combustion engine fauts, are the fauts that affect the engine efficiency and may increase the emissions of unburned hydrocarbon, which caused by poor air to fue ratios (Parak et a., 25) Diese engine was used widey in many fieds of appications. When the engine produced an abnorma vibration, it woud not ony make the vibration of the engine body itsef increase, but aso motivate the vibration inside machine parts and a kinds of accessories, as a resut, it coud cause a variety of shock and vibration damage. Features from vibration signas may be extracted in the time-domain (Ftoutou et a., 211), frequency-domain (Carucci et a., 26) and time-frequency domain (Wang et a., 28). The frequency-domain anaysis is generay considered the most adequate signa-processing too for the nonstationary diese engine vibration. The method for deveoping anaysis process is studied in this study in Corresponding Author: Li Xiaoyu, Department of Agricutura Engineering Automation and Measurement Technoogy Research Section, Coege of Engineering, Huazhong Agricutura University, Wuhan, China This work is icensed under a Creative Commons Attribution 4. Internationa License (URL:

2 order to monitor four-stroke, 2 cyinders 295 diese engine, using vibration signa. Vibration signa was acquired through piezoeectric acceeration sensors attached to a surface of the cyinder head of the engine using LabVIEW program. This signa was anayzed by using frequency-domain techniques to determine various parameters. Then the engine states coud be predicted. Moreover, vibration signa detected on the diese engine is composed of various events that associated with some mechanica processes in the engine. A events were combined to create a compicated vibration signa. This signa coud be mapped onto various processes associated with a cyinder head such as one cyinder shortage, doube cyinders shortage, intake manifod obstruction, exhaust manifod obstruction and norma condition, by using discrete waveet transform method based on LabVIEW software. To achieve the above objectives, two sets of tests wi be conducted: Setting up of the diese engine to aow observations of the vibration signa characteristics when the diese engine is running at a heathy condition. Setting up of the diese engine in order to aow observations of the vibration signa characteristics when the diese engine is suffering from abnorma conditions. Tabe 1: Main design features of the test 295 diese engine Items Parameters Items Parameters Type Vertica, fourstroke Rated speed 15 rpm Number of cyinders 2 Dimensions mm Bore stroke mm Cooing system Water cooing Rated power 13.5 kw weight 345 kg Inet Naturay aspirated Fig. 1: Diese engine faut diagnosis system, physica map The signas wi be anayzed by using the frequency-domain techniques. The resuts or information extracting from the anayzing of these signas reated to engine processes can be used to identify or diagnosis fauts by comparing detected deviations between norma and abnorma conditions in the engine (Girdhar et a., 24). MATERIALS AND HARDWARE DESIGN OF THE FAULT DETECTION Diese engine test of experimenta study: The diese engine used in this experiment was a 295 diese engine. The engine has an exceent technica performance and operating stabiity. The specifications of the 295 diese engine were shown in Tabe 1. The diese engine faut diagnosis system, Data Acquisition (DAQ) of monitoring and toos of signa coected, based on LabVIEW virtua instrument was shown in Fig. 1. The dynamometer CW4, manufactured by Xiangjiang Power Testing Instrument Co. Ltd., was used in this experiment to measure and contro the engine torque and speed. Nationa Instrument PCI 64 E card and LabVIEW software were used for data acquisition. Data was acquired in the card that paced directy inside the computer and used as an interface in order to contro the pant in rea time environment. Two Piezoeectric acceeration sensors-type CA-YD-16A were used; these sensors are sma, highy sensitive and ight in weight. Since the votage signa from the sensor 3822 Fig. 2: The position of sensors on the diese engine head is very weak, the muti-channe charge ampifier YE5853A was used to faciitate ater observation, acquisition and signa anaysis and processing. A SCB- 68 shieded connection box with 68 screw terminas, for easy connection to the Nationa Instruments 68-pin product, was used in this experiment. A Lenovo computer Pentium 4 with 3. GHz processor, 512 MB RAM and 8 GB HD was used. Feature extraction and data anaysis were performed in a MATLAB 21b program. Sensors instaation on the diese engine head: Vibration signas have been measured using the most sensitive piezoeectric acceeration sensors, which were paced on the cyinders head bots. The bots tighten the cyinder head on the engine body and engine fundamenta parts. These bots have a direct impact on engine vibration. The position of the sensors on the interna-combustion diese engine was shown in Fig. 2.

3 Fig. 3: On-ine condition monitoring Setting up of the experiment: The vibration data obtained from the tested diese engine was used in this experiment. Data was gathered from five different conditions, which are associated with the cyinder head such as singe cyinder shortage, doube cyinders shortage, intake manifod obstruction, exhaust manifod obstruction and heathy condition. Singe and doube cyinders shortages were designed by artificiay cutting diese fue route, whie intake and exhaust manifods obstruction were designed by pacing a gasket in the opening between air inet/exhaust outet manifod pipe, respectivey and engine body in order to decrease the norma opening of the air inet and exhaust outet. The engine speeds in this experiment were 62 and 13 rpm; whie the oads torque were 15 and 45 N m. The On-ine condition monitoring can be shown in Fig. 3. The seection of samping frequency and points: In the diese engine, the frequency of the excited vibration was changed according to the ater of its source, speed and oad. Some trias have shown that, the diese engine's head vibration frequency wi be about 1 khz (Liu et a., 1999). For each of the above-mentioned five operating conditions, two speeds and two oads were used, then for each treatment; a signa was coected from the engine s head by using of the sensor. The signa was transferred into the computer through the data-acquisition units. One hundred and one ampitudes were randomy seected for each of the above mentioned treatments. The rate of the sampe taken in this experiment was 2 khz or 2 k sampes per second as a foating-point data. The time duration of each vibration signa was 4 sec; and 5 points were captured from each second. Since this rate of the samping is too difficut to anayze; therefore, ony 496 sampes were randomy chosen in order to faciitate the anaysis process. Vibration signa method: This study was focused on the use of vibration measurements as one of the main methods of condition monitoring of diese engines. Today, many methods are being used to diagnose the 3823 diese engine fauts that can be shown as foows: Diagnosing the faut by making use of ionic current. That is, identifying the faut by the change of it between the eectrodes, when the cyinder is working (Wang and Zhou, 23). Detect the faut by the waveform of ignition votage. That is, to diagnose the faut on the based on the difference of ignition votage waveforms in various working conditions (Chao et a., 28). Diagnose the faiure by the engine cyinder pressure. That is, to cacuate the indicated mean effective pressure by measuring the pressure in engine cyinders and to compare it with that of the norma condition (Viarino and Bohme, 24), then the faut can be distinguished from the resut. Detecting the faut by the oxygen was contained in the exhaust gas. That is, to measure the oxygen density at the end of the exhaust pipe in the engine. A higher oxygen density than that of the norma working conditions indicates the faut in the engine (Chung et a., 2). Diagnose the faut by the rotation speed of the crankshaft. That is, to estabish the inear or non-inear dynamics mode of an engine. Then identify the working conditions by the deviation of transient rotation speed of the engine principa shaft (Jiang et a., 27). Faut detection by vibration signa of the cyinder head. That is, to insta the sensors on the cyinder head bots. Compared to the above methods; the vibration signa of the cyinder head is a very easy to measure it has no effect on the working of the engine of the whoe progress in measurement. Therefore, this method shows its obvious advantages in the arge number of repeatabe and non-damaging experiments, which is based on the design of cyinder shape and the configuration of a caibration program. So, in this experiment, vibration signa method was estabished as the way of recording required data set because of the ease of measurement and the rich contents of the information. Vibration measurement is one of the most common faut diagnosis methods. Vibration signa from the machine surface as the mutistimuation response, not ony contains stimuation information in detai, but aso contains the transferring characteristics and reative faut information. Therefore,

4 it is an effective method to carry out performance of monitoring and faut diagnosis without disassembing the engine (Zhen et a., 24). Severa researchers such as Modgi et a. (24), for exampe, noted that in many cases, there is a direct one-to-one reationship between the cause of the defect and the frequency content and ampitude of the vibration signa. In recent years, these vibration anaysis appications have been used to compicated devices that incorporate a arge number of moving parts such as reciprocating machines, gear boxes transmissions (Biqing et a., 24) and aso in a sma number of appications invoving internacombustion engines as done by Mingzan et a. (23). To keep vibrations under contro, it is essentia to understand the basics and main sources of engine vibrations. WAVELET TRANSFORMS METHOD AND MULTI-RESOLUTION ANALYSIS Waveet transforms method: Waveet anaysis is a method signa process, which decomposes the signa with muti-scae waveet function and eiminates the strong noise from the origina signa. In addition, it is widey used in the processing of compicated signas; therefore, it is significant and practica for the detection of engine faut, i.e., to introduce the waveet anaysis into the anaysis of cyinder-head vibration signas. One of the most important issues in inteigent monitoring is that which reated to featuring extraction. This research mainy focuses on finding appicabe features for diese engine faut detection and diagnosis. Waveet Transforms (WT), which is capabe of simutaneousy processing stationary and non-stationary signas in time and frequency domains, was used for feature extraction (Daubechies, 1992). Despite the amount of previous research on waveet transform; the seection of the mother waveet function as a significant topic in signa anaysis, has been investigated. WT can mainy be divided into discrete and continuous forms. Continuous waveet transforms: Continuous waveet transform the signa x (t) is defined as Addison (22): x w t 1 ( τ ) ( ) Res. J. App. Sci. Eng. Techno., 7(18): , 214 *, s = x t d t. s t τ ψ s (1) The signa-transformed coefficient X wt (τ, s) is a function of the transation parameter (time parameter τ) i.e., the mother waveet and the scae parameter s i.e., father waveet (s>). The mother waveet is denoted by ψ (t), the * indicates that the compex conjugate is used in case of a compound waveet. The signa energy is normaized at every scae by dividing the waveet coefficient by 1/ (Addison, 22). This ensures that the waveets have the same energy at every scae or 3824 the reason for choosing the factor 1/ in Eq. (1) is to keep the energy of the waveets constant. Given ψ (t) is the mother waveet the other waveets can be cacuated as foows: ψ s, τ ( t) 1 t τ = ψ d t s s (2) where, s and τ are the diation and transation + s R, τ R, (Amaris parameters, respectivey, { } et a., 28). The Discrete Waveet Transform (DWT), instead of (CWT), was used in this study (Misiti et a., 1996). Cacuations were made for a chosen subset of scaes and positions. Recenty, Daubechies buit orthogona waveet that was a simpe, but it is very skifu, in addition; he has done many studies on waveet frameworks that aow more freedom regarding the choice of the foundation waveet functions at ess expense of some abundance. Daubechies, aong with Maat, therefore, is entrusted for the improvement of the waveet from continuous to discrete signa anaysis. In the DWT formaism, the scae s and time τ are discretized as foows: s = τ = n s > And m Z (3) where, m and n are integers. Therefore, the continuous waveet function ψ (t) becomes discrete waveets and given by: m / 2 m ( ) = ( ) ψ m n t s ψ s t nτ,. (4) The discretization of the scae parameter and time parameter eads to the discrete transform, which defined as: m / 2 * m ( ψ ) = ( ) ψ ( τ ) X wt m, n; s x t s t n dt. (5) Different from the STFT, the waveet transform can be used for muti-scae anaysis of a signa through diation and transation, so it can effectivey extract time-frequency features of that signa. Therefore, the waveet transform is more suitabe for the anaysis of non-stationary signas (Francois and Patrick, 1995). Nowadays, the waveet obtains great success in machine faut diagnosis for its many distinct advantages, i.e., not ony for its abiity in the anaysis of non stationary signas; but aso the waveet coefficients in the different frequency bands of the DWT can be processed in severa ways. By adjusting the waveet coefficients the reconstructed signa of the synthesis

5 fiter bank can be changed in comparison to the origina signa; this gives the DWT some more attractive properties over inear fitering. Compared to the CWT, DWT is simpy to compute and its coefficients are easier to interpret since no conversion from scae to frequency has to be made. This scheme was conducted by computing the socoefficients. The caed approximations and detais approximations coefficients are high-scae and ow frequency components, whie the detais ones are owof the signa. The scae and high frequency components DWT coefficients were computed using the equation: [ ] [ ] x x x n g n = = s, τ j, k j, k n Z where, 2 j j s=, τ = k2, j N, k Z The waveet fiter g pays the roe of ψ (t). The decomposition (fitering) process can be iterated, so that one signa can be broken down into many ower-resoution components; this is caed the waveet decomposition tree. For detection of transients, a muti-resoution anaysis tree based on waveets has been appied (Maat, 1998). Figure 4 showed the tree structure of 5 eve muti-resoution decomposition. Every one of the waveet transforms sub-band was independenty reconstructed from each other, so as to get k+1 separated components of the signa x [n]. The MATLAB mutires function (Sanchez et a., 1996) cacuates approximation coefficients to the 2 k scae and detai coefficients signas from the 2 1 to the 2 k scae for a given input signa. It uses the anaysis ow-pass and high-pass fiters to synthesize those fiters again to get the origina signa. The decomposition can be hated at any scae with the fina smoothed output containing the information of a the remaining scaes. Muti-resoution anaysis: The discrete waveet anaysis is based on the concept of Muti-Resoution Anaysis (MRA) introduced by Maat (1998). With the (MRA), the signa was decomposed recursivey into a sum of detais and approximations coefficients at different eves of resoution. From the ow-pass fiter, a vector of Approximation Coefficients (CA1) was found that represents an estimation of origina signa with haf resoution. From the high-pass fiter, a vector of Detai Coefficients (CD1) was obtained that contains the detais of the signa. The vector (CA1) can be further decomposed to form of a new vector of Approximation Coefficients (CA2) and a vector of Detai Coefficients (CD2). With the increase of the decomposition eve, Fig. 4: Decomposition tree of DWT signa S. CD and CA are (6) detais and approximations coefficients at eves Less information wi be incuded in the approximation coefficients. The ost information between approximation coefficients of too successive decompositions is encoded into the detai coefficients; this process can be iterated to eve N. In this study; iteration was made to eve 5 and db order 5. As a resut, a vector of approximation coefficients and a series of vectors of detai coefficients were accompished that forms the DWT coefficients. For instance, if F s is the samping frequency, then the approximation of a eve-n of the DWT decomposition corresponds to the frequency band,, whereas the detai covers the frequency range ;. The reation of 5-eve muti-resoution decomposition at origina signa S is noted mathematicay as: S = CA5 + CD5 + CD4 + CD3 + CD2 + CD1 where, D1 to D5 denotes the first to five muti-resoved higher frequency signa (detais coefficients), whie, A5 denotes the owest frequency signa (approximation coefficient). The signa can be convenienty reconstructed by Inversing Discretee Waveet Transform (IDWT). As shown in Fig. 5 at each stage of the MRA, the signa was passed through a high-pass fiter (scaing fiter) denoted as H and ow-pass fiter (waveet fiter) denoted as G. The H and G fiters are the decomposition fiters. The H and G fiters are the reconstruction fiters; the process of reconstructing the approximations CA i (t) and detais CD i (t) was presented in Fig. 6. The waveet coefficients thus obtained can then be used for the purposes of signa denoising and compression. SIGNAL DENOISING When a signa was coected from the diese engine head, it was frequenty contaminated by noise; the term

6 Fig. 5: The decomposition process of approximations (CAi) and detais (CDi), at eve 5. A symbo 2 represent dyadic down samping Fig. 6: The reconstruction process of approximations (CAi) and detais (CDi), at eve 5. A symbo 2 represent dyadic up samping noise refers to any undesired change that atered the vaues of the origina signa. The simpest mode for acquisition of noise by a signa is the additive noise, which has the foows form: contaminated signa = origina signa + noise. Reduction of noise as an integra part of signa estimation has been studied for many years in diverse fieds such as vibration signas. The principe of denoising method which used DWT is that the waveet coefficients beonging to the noise at each scae must be removed, whie keeping the vauabe signa. Finay, the reconstruction of decomposed and 3826 de-noised signa can be processed by property of reciprocity. Method of threshod denoising: From signa denoising, it has been shown that threshoding the waveet coefficients of a noisy signa aow to restore the smoothness of the origina signa. Since the beginning of using waveet transforms in signa processing, it has been noticed that waveet threshod was gained considerabe interest for removing noise from signas. The method consists of decomposing the

7 data into an orthogona waveet basis, to suppress the waveet coefficients smaer than given ampitude; and therefore, to transform the data back into the origina domain. In this research, the use of discrete waveet transforms for denoising diese enginee head vibration signas have been investigated. Because of the arge bandwidth of the diese engine head systems, the vibration signa is usuay contaminated by noise coming from various sources such as the crank mechanism, pressure puses from gas, fue or air fow, vaves, gearwhees, an unbaanced turbocharger and so on. Denoising the diese engine head vibration signas before performing any data anaysis is very important in order to enhance the showing of detection of the diese engine fauts and to get correct depth resuts (Lahouar, 23). Energy is mosty concentrated in a sma number of waveet dimensions; the coefficients of these dimensions are reativey arge compared to the other dimensions or to noise, which has its energy spread over a arge number of coefficients. Hence, by setting smaer coefficients to zero, the noise can neary optimay be eiminated, whie preserving the important information of the origina signa (Donoho, 1995). Fig. 7: Hard threshod denoising Types of threshoding: Two standard threshod techniques exist; these are soft threshod (shrink or ki) and hard threshod (keep or ki). These two threshoding methods are used to estimate waveet coefficients in waveet threshod de-noising (Merry, 25). The simpest threshod technique is the hard threshod, where the new vaues of the detais cd t were found according to the coefficients, ^ ( ) foowing equation: cd ^ ( ) if cd( t) if cd( t) cd t > θ = θ where, cd (t) = The detais coefficients θ = The threshod eve In the soft threshod, the new detais coefficients were given by the foowing equation: ( ) ( ( )) ( ) sign cd t cd t if ^ θ cd t > θ cd = if cd t θ where, sign () is the sign function. The threshod θ can be estimated as foows: θ = σ 2ong ( N) ( ) ( ) (7) (8) (9) 3827 Fig. 8: Soft threshod denoising where, N is the ength of threshod coefficients and σ characterizes the noise eve. This method of threshod seection and others were described in Donoho and Johnstone (1995). In order to get the denoised signa, the new detais coefficients, c (t) are used in the signa reconstruction process instead of the origina coefficients CD (t). Figure 7 and 8 iustrated the difference between the hard and soft threshods denoising methods respectivey; these methods have been widey used in data processing. The procedure of signa denoising based on DWT consisted of three steps: decomposition, threshoding and reconstruction signa. The denoising procedure is summarized in Fig. 9. The contrast, Signa to Noise Ratio (SNR) and Root Mean Square Error (RMSE) were used to stimuate the noise canceation effect. When SNR is greater than RMSE, the signa is coser to the origina signa and the noise was reduced or removed. When the x (n) was chosen for the origina signa and (n) for the signa after denoising; then SNR can be defined as foows: 2 x ( n) n (1) SNR= 1 og ^ 2 x( n) x ( n) n

8 Fig. 9: Discrete waveet transforms denoising procedure SNR/RMSE Mean of SNR Mean of RMSE Vaue of θ Fig. 1: Comparison of SNR and RMSE under different vaues of θ Whie the RMSE can be defined by the foowing equation: RMSE = 1 x n xˆ n N [ ( ) ( )] 2 n (11) important roe in the impact of noise canceation (Hu and Kw, 21). The seection of mother waveet is one of the main factors to maintain the data in the waveet domain. According to Peretto et a. (25), the effectiveness of the waveet anaysis is argey infuenced by the choice of the mother waveet, decomposition eve and the noise canceation. The choice of the appropriate mother waveet depends on the nature of the signa and the type of the information to be extracted from the signa. In this study, in order to seect the best mother waveet and decomposition eve of the waveet function; many severa tests have been done in many types of waveet functions such as Daubechies, Symet, Haar and Coifet waveet famiies. The seected mother waveet type shoud be one that has the greatest vaue of the SNR. The `db5' mother waveet and waveet decomposition eve 5 have the best performance than others. Accordingy, they were appied in this research studied. BACK PROPAGATION NEURAL NETWORK Seection of the optimum threshod eve and mother waveet decomposition for the signa denoising: The seection of the threshod eve θ that ranges from.- 1. pays an significant roe in the denoising process, which is manifested by the canceation of the noise. Therefore, seection of the appropriate vaue of θ is very important. In order to use a suitabe mother waveet and decomposition eve N for denoising signa, severa tests on the seected sampes (496 sampes) were done. From those tests, it was ceared that the best mother waveet and eve N are sym5 and eve 5, respectivey. The average of SNR and RMSE were compared. The resuts were potted in histograms form Fig. 1. Knowing that the difference between SNR and RMSE is very big, so to compare SNR and RMSE, the RMSE was mutipied by 1 (RMSE 1), as shown in Fig. 1. From Fig. 1, it is cear that, when θ =.1 the average of SNR was , which is the biggest vaue and greater than that of the RMSE. This means that the signa is cosest to the origina signa and the noise was reduced or removed. Therefore, this was considered as the best hard threshod eve to use for denoising process. Seection of mother waveet and waveet decomposition eve for signa anaysis: There are many different mothers waveets that paying an 3828 Artificia Neura Network (ANN) is non-inear mapping structures based on the function of the human brain. It is a powerfu too for modeing, especiay when the underying data reationship is unknown. ANN can identify and earn correated patterns between input data sets and corresponding target vaues. After training, ANN can be used to predict the outcome of new independent input data. ANN has a great capacity in predictive modeing, i.e., a the characters describing the unknown situation can be presented to the trained ANN and then predicts the resuts. In recent decades, ANN has been used for a wide variety of appications such as chemica processes, engineering, digita circuitry and contro systems. It shoud be noted that, choosing a suitabe ANN architecture is very important for its successfu appication. A neura network consists of a set of connected ces, i.e., the neurons. The neurons receive impuses from either input ces or other neurons, perform some kinds of transformation of the input and transmit the outcome to other neurons or to output ces. The neura networks are buit from ayers of connected neurons; one ayer receives input from the preceding ayer of neurons and passes the output on to the subsequent ayer. To date, the most popuar ANN architecture is the backward propagation neura network BPNN (Ying et a., 21). Furthermore, neura networks are capabe of performing faut cassification at hierarchica eves. Based on earning strategies, ANN fas into two categories namey

9 Fig. 11: Mode of back-propagation network supervised and unsupervised. The BPNN is a supervised network and a mode of it was shown in Fig. 11. Each input node is connected to a hidden ayer node and each of it is connected to an output node in a simiar way. This makes the BPNN a fuy connected network. The outputs are compared to the desired target vaues and an error was produced. In addition, the weights are adapted to minimize the error. Finay, the desired target vaue was known; then this is a supervised earning process. The input vaues (input data) are fed to the neurons in the so-caed input ayer in the eft part of Fig. 11. Then these vaues are processed within the individua neurons of the input ayer. After that, the output vaues of these neurons are forwarded to the neurons in the hidden ayer. The arrows indicate the connections from the input nodes to hidden nodes; aong which the output vaues of the input nodes are passed on to the hidden nodes. Either these vaues obtained as inputs by the hidden nodes, which processed within them and passed on to the output ayer or to the next midde ayer, i.e., there are more than one hidden ayer. Each connection has an associated parameter indicating the strength of it; the so-caed weight. By changing the weights in a specific manner, the network can earn to map patterns presented at the input ayer to targeted vaues of the output ayer. The description of the procedure by which this weight adaptation is performed is caed earning or training agorithm. Sometimes, the so-caed bias units are aso present in the neura network; these are neurons with the property of aways producing a +1 at the output (Hurvich and Tsai, 1989). The back propagation agorithm is the most commony used ANN earning technique. For a three-ayer network with n input, h number of hidden neurons and m output neurons, the number of neurons in hidden ayer woud be obtained as foows: h = n* m+ c where, c : A constant, usuay take from 1-2 (12) 3829 The hidden node output y i is cacuated as foows: yi f = wij x j θ j j where, x j = The input to the input node w ij = The input ayer to hidden ayer connection weight = The threshod for the hidden ayer nodes θ j The output node of the outputt o i can be found by the foowing formua: oi f = Tij yi θi i where, T ij = The hidden ayer to outpu ayer connection weights = Threshod for the output ayer nodes θ i The foowing equation is used to cacuate the error contro: p E= e < ε k k k 1 L ( ) ( ) 2 k k 1 1 e = t o where, E = The error for a sampes e k = The error for one sampe p = The number of sampes o i = The output nodes = The desired output of output node t (13) (14) (15) (16)

10 Cacuation of error vaue δ is performed as foows: δ = ( t O ) O (1 O ) (17) 1 1 Weight correction can be obtained as foows: T ( k+ 1) = T ( k) +ηδ y i i i Threshod correction can be found as foows: 1 (18) θ ( k+ 1) = θ ( k) + nδ (19) Hidden ayer node is revised as foows: Error can be obtained as foows: δ = y (1 y ) δ T (2) ' i i i i Weight correction can be cacuated as foows: w ( k+ 1) = w ( k) + ηδ x (21) ' ' ij ij i j Threshod correction can be obtained as foow: Therefore, feature extraction and seection are needed to produce good features for cassification routines (Barziay and Braiovsky, 1999). Construction of SVM agorithm: Support vector machine is a usefu cassification method Boser et a. (1992). Given the data input x i (i = 1, 2, 3 L), L is the number of sampes; the sampes are assumed to have two casses namey positive cass and negative one. Each of these casses is associated with abes be y i = 1 for positive cass and y i = - 1 for negative cass. In the case of inear data, it is possibe to determine the hyper-pane f (x) = that separates the given data or ineary separate the training set of x, y, x R n, y = + 1, 1 i= 1,2,..., ; there are rea i i i i { }( ) even number sequences (w, b) that satisfy the conditions: ( i ) ( ) w. xi+ b 1 y =+ 1 w. xi+ b 1 yi = 1 (23) Cassification function from the above conditions is as foows: θ ( k + 1) = θ ( k) + ηδ (22) ' ' i i i SUPPORT VECTOR MACHINE AND SIGNAL PATTERN RECOGNITION Support Vector Machine (SVM) is a usefu technique for data cassification. According to Vapnik et a. (1996), there are three main probems in machine earning, which are: cassification, regression and density estimation. In a the cases, the goa is to suppose a function from a sampe data. In the supervised earning mode, the training data consists of pairs of input/output points (x i, y i ), 1 i n, with x i beonging to some space X and y i R (regression) or y i {-1, 1} (binary cassification). In machine condition monitoring and faut diagnosis probem, SVM is empoyed for recognizing specia patterns from acquired signa and then these patterns are cassified according to the faut occurrence in the machine. After signa acquisition, a feature representation method can be performed to define the features, e.g., statistica feature of signa for cassification purposes. These features can be considered as patterns that shoud be recognized using SVM. Usuay, huge features wi be obtained in feature representation. Unfortunatey, not a features are meaningfu and contain high information about machine condition; some of them are useess and irreative features. These features shoud be removed to increase the accuracy of cassifier. 383 ( ) = ( + ) f x sign w x b. i (24) The separating hyper-pane that creates the maximum distance between the pane and the nearest data, i.e., the maximum margin, is caed the optima separating hyper-pane. Taking into account the noise with sack variabes g i and the error penaty c, the optimum hyper-pane that separating the data can be obtained as a soution to the foowing optimization probem: 2 Minimize 1 Dmin = w, 1 2 Dmin = w + C gi 2 (25) 2 i= 1 Subjected to y ( w. x + b) 1 ( i= 1,2,.. ) i i The cacuation can be simpified by converting the probem with Kuhn-Tucker condition into the equivaent Lagrangian dua probem, which wi be as foows: 1 2 L( w, b, a) = w ai yi( w. xi+ b) 1 (26) 2 i= 1 In which the Lagrange mutipier. i a The task is to minimize Eq. (26) with respect to w and b, whie requiring the derivatives of L to a to

11 vanish. At an optima point, the foowing sadde-point equations can be obtained: L L =, =, w b This was repaced into the foowing form: (27) i i i, i i ( i ) (28) w= a y x a y = a o i= 1 i= 1 From Eq. (28), w was found to be contained in the subspace spanned by the x i. By substituting Eq. (28) into (26), the dua quadratic optimization probem was obtained as foows: Maximize ( ) 1 L a = a a a y y x. x (29) i i j i j i j i= 1 2 ij= Subjected to ai o, i= 1,...,, ai yi =. (3) Thus, by soving the dua optimization probem the coefficient a i, can be obtained, which was required to express the w to sove Eq. (25). This eads to the foowing non-inear decision function: i= 1 When appying a kerne function, the earning in the feature space does not require expicit evauation of ϕ and the decision function wi be as foows: f ( x) = sign ai yik( xi, y j) + b (33) ij= 1 Any function that satisfies Mercer s theorem Chebi et a. (29) can be used as a kerne function to compute a dot product in the feature space. There are different kerne functions used in SVM, such as inear, poynomia and Gaussian Radia Basis Function (RBF). The seection of the appropriate kerne function is a very important, since the kerne defines the feature space in which the training set exampes wi be cassified. The definition of egitimate kerne function is given by Mercer s theorem. In this study, inear, poynomia and RBF were evauated and formuated as foows: Linear kerne: K ( x, x ) = x x (34) i j i j Poynomia kerne: K( x, x ) ( x x 1) 3 i j = i j+ (35) f ( x) = sign ai yixi. x j+ b (31) ij= 1 SVM can aso be used in non-inear cassification tasks with appication of kerne functions. The data to be cassified is mapped onto a high-dimensiona feature space, where the inear-cassification is possibe. Using the non-inear vector function φ (x) = (ϕ 1 (x),, ϕ (x)) to map the n-dimensiona input vector x onto dimensiona feature space, the inear decision function in a dua form was given by: f ( x) = sign ai yi( φ( xi). φ( x j) ) + b (32) ij= 1 Working in the high-dimensiona feature space enabes the expression of compex functions, but it aso generates a probem. Computationa probem occurred due to the arge vectors and the over-fitting exists due to the high-dimensionaity. The atter probem can be soved by using the kerne function K (x i, x j ). Kerne is a function that returns a dot product of the feature space mappings of the origina data points and stated as: ( i, j) = ( φ( i). φ( j) ) K x x x x 3831 RBF kerne: x x K( xi, x j ) exp i j = 2 where, δ is kerne parameter. 2 2δ RESULTS AND DISCUSSION (36) Waveet anaysis on cyinder head vibration signa: Origina cyinder head vibration signa not ony incuded ow-frequency signa, but aso incuded compicated high-frequency signa. Consequenty, it is difficut to discriminate directy between heathy and fauty behaviors from the origina signas, especiay in high-speed segment. Thereby, the faut can be ceary distinguished after decomposing the signas. When a defect occurs, the engine vibration acceerates and the signa frequencies and ampitudes wi be changed. Therefore, the output signa energy distribution wi change. For exampe, some frequency-domain signas are suppressed, whie others are enhanced, which resut in a corresponding decrease or increase in an energy. In this study, the DWT decomposition was appied to the vibration data for norma and abnorma condition, using mother waveet db5 and decomposition eve 5. The

12 Tabe 2: Frequency sub-bands for the six eves of DWT decomposition Frequency bands Frequency range (Hz) CA5 CD5 CD4 CD3 CD2 CD1 14 Norma 7 One cyinder shortage Energy per frequency band Energy per frequency band Frequency bands Doube cyinder shortage Frequency bands Intake obstruction Energy per frequency band Energy per frequency band Frequency bands Frequency bands 14 Exhaust obstruction Energy per frequency band Frequency bands Fig. 12: Average energy contained in the frequency sub-bands of 1, 2, 3, 4, 5 and 6 for vibration data captured on a surface of the cyinder head of the 295 diese engine origina Signa (S) was decomposed into six components: an approximation coefficient CA5 and five detais coefficients from CD1 to CD5. The frequency sub-bands corresponding to each component of the signa were shown in Tabe 2. Characteristics of the signa energy extraction and faut detection: The anaysis of the energy characteristics of the signa can be used for the diese engine faut detection. Therefore, the energy was extracted from each frequency sub-band of norma and abnorma conditions as a feature of engine faut 3832 diagnosis. Any method deveoped for this purpose shoud be highy accurate. There is an approach, which based on the fact that, fauts are transated into ow and high frequencies phenomena in the vibration signa. Consequenty, the faiure behavior can be detected by anayzing the energy contained in those frequencies. When this approach was appied to the sub-band frequency, the energy in the sixth band was aways higher if the condition is norma, whie it was arger in the first band if the condition is abnorma. This resut agreed with (Boser et a., 1992), who appied discrete waveet decomposition for the detection and diagnosis

13 of fauts in roing eement bearings. By doing so norma and abnorma conditions, behavior coud be efficienty distinguished by comparing the energy accumuation of each sub-band. Figure 12 showed the resuts of the energy contained in the frequency subbands of 1, 2, 3, 4, 5 and 6 for vibration data of heathy and fauty conditions. Design of the Back-Propagation (BP) network: The BP mode used in this study was incuded in MATLAB Neura Network Toobox. In this appication, the number of inputs X i is associated with the number of detais coefficients extracted from discrete waveet anayzed. The number of hidden ayers and the number of neurons in the hidden ayer are generay appication dependent. For this study, one hidden ayer was used for the whoe test, whie the number of neurons in this ayer was 19; that is to observe the change in cassification accuracy. There were five outputs y i, which were associated with the number of diese engine faut conditions. The output from the network is binary i.e., each output node is either or 1. On the creation of the BP network function newff was used; the input ayer and midde ayer of the transfer function was tansig, the midde ayer and output ayer transfer function was purein whie, training function was trainm. Weights and threshod vaue of the BP earning agorithm that used the defaut gradient and descent momentum earning function was earngdm. The mean square error for the defaut performance function is the "mse" and the network training error of.1 was seected. Five working conditions namey exhaust obstruction, intake obstruction, norma, singe cyinder shortage and doube cyinders shortage that corresponded to the desired output of 1, 1, 1, 1 and 1, respectivey were seected. For exampe, when the output was 1 recognition resuts were of the exhaust obstruction. Features extraction using BPNN and SVM: Figure 13 iustrated a comparison of the diagnostic accuracies rates extracted as percentages, using SVM and BPNN on the five diese engine faut conditions. They were appied to the signa captured from the test diese engine at two speeds, two oads and five faut conditions. The experimenta resuts showed that, diagnosis accuracy of BPNN in the faut detected has higher rates than that of SVM for a cases. These resuts agreed with Wu et a. (28), who appied artificia neura network and SVM to diagnose the earning disabiities probem for students. His resuts showed that neura network performs better than SVM. In addition from Fig. 13, it can aso be seen that the norma condition has the highest accuracies rate for a conditions, i.e., amost about 1% when BPNN was appied, but it has got different percentages when SVM was appied. Therefore, it can be observed that BPNN is most consistent in a the conditions than SVM. Finay, according to the above resuts, it can be concuded that the BPNN is the exceent method than SVM in faut diagnosis for a diese engine in the cases studied. Design of the support vector machine training: Support vector machine on different training faut cassifications has been used in this study. Just as BPNN was modeed, SVM Toobox was aso used in MATLAB software. In this appication, the number of inputs X i was associated with the number of detais coefficients that were extracted from discrete waveet anaysis. The number of outputs y i was associated with above-mentioned five diese engine faut conditions Norma Singe cyinder shortage Doube cyinder shortage Intake obstructions Exhaust obstructions 12 1 Norma Singe cyinder shortage Doube cyinder shortage Intake obstructions Exhaust obstructions 8 8 % 6 % SVM BP SVM BP 62 r/min 13 r/min SVM BP SVM BP 62 r/min 13 r/min Fig. 13: Cassification accuracy rates as (percentages) extracted from SVM and BPNN on the data, at two speeds, two oads and under five faut conditions, (a) oad 15 N m, (b) oad 45 N m 3833

14 (a) (b) Fig. 14: Optimization of c and g of engine speeds at 62 rpm (a) (b) Fig. 15: Optimization of c and g of engine speed at 13 rpm There are two parameters in the RBF kerne. Parameter c is the error penaty function and the parameter g is the kerne functions. In the function "svmtrain", c and g were usuay given empiricay or by any cacuation to obtain the Cross-Vaidation (CV). CV is a standard technique for adjusting hyper-parameters of predictive modes. The goa was to identify good c and g; therefore, the cassifier can accuratey predict unknown data i.e., testing data. So, usuay the function "svmtrainforcass" for cross-vaidation was used to determine the vaues of c and g, it is noted that, it may not be usefu to achieve high training accuracy i.e., a cassifier that correcty predicts training data whose cass abes are indeed known. The prediction accuracy obtained from the unknown set more precisey refects the performance on cassifying an independent data set The cross-vaidation procedure can prevent the overfitting probem. The current experiment appied the cross-vaidation data set for the two speeds, two oads and five-engine faut conditions. Figure 14 and 15 beow, iustrated cross-vaidation test resuts by deveoping SVM and radia basis function RBF kerne, in order to optimize parameters c and g, in 2D and 3D at two different speeds. From Fig. 14 and 15, it can be summarized that the cross-vaidation test accuracy rates were shown as foows: When the speed was 62 rpm, best C = 256, g = with cross-vaidation rate = 77.33%. When the speed was 13 rpm, best C = 256, g =.1882 with cross-vaidation rate = 78.33%.

15 CONCLUSION In this experiment, the attention was focused on the investigation of the vibration signa of the 295 diese engine. Diese engine faut diagnosis depends on the feature extraction of the signas. The faut diagnosis system was constructed and designed for inspecting the status and faut diagnosis of the diese engine based on the discrete waveet anaysis and LabVIEW software. The cyinder-head vibration signas were captured through a piezoeectric acceeration sensor, whie the engine was running at two speeds of 62 and 13 rpm and two oads 15 and 45 N m. Data was gathered from five different conditions such as singe cyinder shortage, doube cyinders shortage, intake manifod obstruction, exhaust manifod obstruction and norma condition. The signa captured was anayzed by deveoping DWT technique. Hard threshod (db5) mother waveet and decomposition eve 5 were used. The resuts showed that, the energy extracted from discrete waveet anaysis techniques coud be successfuy used in condition monitoring and faut diagnosis of diese engine fauts. In addition, two techniques, BPNN and SVM were appied on signa capturing to determine cassification accuracy rate. The resuts showed that, BPNN was effectiveness in faut diagnosis for interna-combustion engine with various faut conditions than SVM. Further research on the diese engine to choose an appropriate mother-waveet and decomposition-eve can increase the accuracy rate of fauts diagnosis. REFERENCES Addison, P.S., 22. The Iustrated Waveet Transform Handbook. IOP Pubishing Ltd., ISBN: Amaris, A.H., C. Aonso, M.D. Forez, T.J.P. Lobos, J. Rezmer and Z. Wacawek, 28. Appication of advanced signa processing methods for accurate detection of votage dips. Proceeding of 13th Internationa Conference on Harmonics and Quaity of Power, pp: 1-6. Barziay, O. and V.L. Braiovsky, On domain knowedge and feature seection using a support vector machine. Pattern Recog. Lett., 2(5): Biqing, W., A. Saxena, T.S. Khawaja and R. Patrick, 24. An approach to faut diagnosis of heicopter panetary gears. Proceeding of IEEE AUTOTESTCON, pp: Boser, B., I. Guyon and V.N. Vapnik, A training agorithm for optima margin cassifiers. Proceedings of the 5th Annua Workshop on Computationa Learning Theory, New York. Carucci, A.P., F. Chiara and D. Laforgia, 26. Anaysis of the reation between injection parameter variation and bock vibration of an interna combustion diese engine. J. Sound Vib., 295: Res. J. App. Sci. Eng. Techno., 7(18): , Chandroth, G.O. and W.J. Staszewski, Faut detection in interna combustion engines using waveet anaysis. Proceedings Comadem 99. Sunderand, UK, 3: 8. Chao, H., J. Xu and C. Ji, 28. Experimenta study on the knock contro of HCCI engine by methano and ethano additives at high oad. T. Chinese Soc. Agric. Mach., 3: Chebi, J., G. Noe, M. Mesbah and M. Deriche, 29. Waveet decomposition for the detection and diagnosis of fauts in roing eement bearings. Jordan J. Mech. Ind. Eng., 3(4): Chung, Y., H. Kim and S. Choi, 2. Fow characteristics of misfired gas in the exhaust manifod of a spark ignition engine. J. Autom. Eng., 4: Daubechies, I., Ten ectures on waveets. Proceeding of CBMS-NSF Regiona Conference Series in Appied Mathematics. Society for Industria and Appied Mathematics (SIAM). Phiadephia, PA, Vo. 61. Donoho, D., Denoising by soft threshoding. IEEE T. Inform. Theory, 4: Donoho, D. and I. Johnstone, Adapting to unknown smoothness via waveet shrinkage. J. Am. Stat. Assoc., 9: Francois, A. and F. Patrick, Improving the readabiity of time frequency and time scae representations by the reassignment method. IEEE T. Signa. Process., 43: Ftoutou, E., M. Chouchane and N. Besbès, 211. Interna combustion engine vave cearance faut cassification using mutivariate anaysis of variance and discriminate anaysis. T. I. Meas. Contro, DOI: / Girdhar, P., C. Scheffer and S. Mackay, 24. Practica machinery vibration anaysis and predictive maintenance. Newness, Esevier, Oxford. Hu, J.W. and C. Kw, 21. Waveet anaysis of vibration signa denoising. J. Mech. Eng. Autom., 1: Hurvich, C.M. and C. Tsai, Regression and time series mode seection in sma sampes, Biometrika, 76: Jiang, A., X. Li, W. Wang, X. Huang, Z. Zhang and H. Hua, 27. Misfire faiure diagnosis of engine based on waveet anaysis. T. Chinese Soc. Agric. Eng., 4: Lahouar, S., 23. Deveopment of data anaysis agorithms for interpretation of ground-penetrating radar data. Ph.D. Thesis, Department of Eect. Eng. Virginia Tech. Backsburg, VA. Liu, Y., R. Du and Y. Shu-Zi, Interna combustion engine cyinder head vibration acceeration signa of the features and diagnostic appications. Huazhong Univ., Techno., 1: