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    • ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE) Volume 1, Issue 6, August 2012 Novel Technique for Multiple Fault Detection in an Automobile Engine Using Sound Signal 1 Mr. S. N. Dandare, 2 Dr. S. V .Dudul 1 Associate Professor, Electronics Department. B.N.C.O.E., Pusad, India 2 Professor & Head, Dept of Applied Electronics, SGBAU, Maharashtra, India.Abstract Car technology is advancing at amazing speed so its 1. Introductionno surprise that at least more than hundreds of car models Now-a-days technology is advancing very fast andare coming up in each year with newer technology and because of that everyone in and around feels the change ininnovations. The new technologies are necessary to meet their life style. As a result in the recent years of advancedincreased transport demands in future and satisfy the need technology, the cars are no less because almost every otherfor the safer, faster and more sustainable mobility of day the automobile engineers are trying out more and morepersons and goods. But day by day the maintenance of the new technologies in the car. The car users will simply bevehicle is difficult because of the scarcity of skilled surprised when they get to know more about the emergingmechanic in all over the world [1, 2]. Automobile engine is car technologies. The emerging technology has madea complex system and sometimes the problems can be a bit possible the economic use of cars. The technology assurestricky to diagnose. To diagnose the problem correctly, lots that the car can be used for a longer period of time withoutof knowledge and experience is required. Engine problems bringing about any harm in the environment. It is possible,are caused primarily by improper maintenance or fatigue when the engine of the car working in healthy conditions.caused by normal wear and tear and also worn out or Though the engines differ in their specifications, they faceclogged car parts. Worn out parts may cause overheating of the common problems and troubles [7]. Car enginethe engine, engine surging and other problems. When the problems need special attention and care to maintain theproblem arises and it‟s not properly diagnosed and repaired engine performance and effectiveness. If the problemin time then it may create some other severe problems and arises, it can be informed automatically when it is at initialultimately the engine may bring to a halt the working [3,4]. stage [8].This paper proposes the innovative idea to detectLooking to this aspect it is very necessary to diagnose the the multiple faults using the single sensor.fault in initial stage and for that automatic fault detectionsystem is necessary. Many researchers have suggested the 2. Engine used for Experimentation.fault detection techniques by implementing a separatesensor for separate fault but that makes the system very According to news published by Maruti Suzuki New Delhi,complex. At the same time maintenance of sensor will be on June 15th, 2012: “Maruti Suzuki Alto has been thean additional job than the maintenance of vehicle. highest selling car, in the domestic market for the past 7Therefore, it is proposed a simple fault detection technique years. It has also been rated as the highest selling small carto detect the multiple faults using single sensor [5, 6]. A in the world, for the past two years.” Looking to themicrophone is used as a sensor to collect the dynamic popularity of Maruti Alto Car, Engine of this car has beeninformation of an automobile engine in normal and faulty used for experimentation. The Maruti Alto Car Engine iscondition. The features are extracted using MALAB with 800 cc, four cylinders and five speed gears. The detailsoftware then the detailed analysis is carried out using specifications are given in Table 1A.Artificial Neural Networks (ANN). Comparison of all typesof ANN has been done on the basis of Average Table 1A: Engine Specifications.Classification Accuracy. Finally optimal neural networkhas been designed for optimal performance of the engine on Maruti Alto LXi Enginethe basis of Classification Accuracy. Engine Type In-Line EngineKey Words: Artificial Neural Networks, Fault Detection, Engine Description 0.8L 47bhp FC engineClassification Accuracy, Support Vector Machine, Engine Displacement(cc) 796Knocking Fault, Cylinder Head Fault and Insufficient No. of Cylinders 3lubricant Fault. Maximum Power 46bhp@6200rpmAbbreviations: ACA-Average Classification Accuracy %, KF- Knocking Fault, ISL – Insufficient Lubricant Fault , HOL – High oil Level Fault, IFS – Maximum Torque 62Nm@3000rpmInsufficient Lubricant Fault, PR – Piston Ring Fault, N – Normal Signal, MSE- Valves Per Cylinder 4Mean square error, NMSE - Normalize mean square error, MAE – MeanAbsolute Error, r- Correlation Coefficient, ANN – Artificial Neural Networks Valve Configuration SOHCand FDIE- fault detection, isolation and estimation. Fuel Supply System MPFI 122 All Rights Reserved © 2012 IJARCSEE
    • ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE) Volume 1, Issue 6, August 2012 Bore x Stroke 68.5 x 72.0 mm but it may also indicate an oil flow problem that will Maruti Alto LXi Transmission eventually cause damage to at least one valve. Transmission Type Manual Gear box 5 Speed II) Insufficient lubricant Fault Drive Type FWD To protect moving parts and reduce friction, Synchronizers All Gears automotive engine oil provides a barrier between the Maruti Alto LXi Performance rotating or moving engine components. Ideally, a film of oil Top Speed 137km/hr should exist between moving components. This is called Acceleration (0-100 kmph) 17.7 seconds full film lubrication. In order to achieve full film lubrication, a constant supply of clean oil is required. The Table 1B Microphone Specification engine oil system constantly filters and circulates engine oil to ensure that all components are protected. Features Type Over the Ear III) Excessive Lubricants Fault Specification If the crankcase is over-filled by oil more than a little then Frequency 20Hz-20KHz excess is drain out. The excess oil pressure resulting from this Drive Unit 40 mm Impedance 32 Ohm blockage can cause the engine oil system to by pass the oil Sensitivity 110dB3dB filter. The normal circulation of motor oil through the engine Microphone- Sensitivity 58db2db lubrication system, results in the accumulation of particles in Connector 3.5 mm the motor oil. These particles can consist of dirt, rust and material generated from engine wear. During engine operation, the normal path for the circulation of oil is from the Table 1C Sound Recorder Specification oil pump to the oil filter, then on to the crankshaft bearings. If particles were to become embedded in the soft material usedSignal ProcessingAD/DA conversion 24 bits, 44.1 kHz in the crankshaft bearings, a barrier to lubrication can occurData Type and cause excessive wear in the bearing material or theFormat MPEG 1, Audio Layer 3 (MP3) crankshaft surface.Sampling Rate 44.1 kHzBit Rates 64/96/128/160/192/256/320 kbps IV) Insufficient Fuel Supply FaultFormat WAVFrequency 20 Hz to 20 kHz Excessively rich or excessively lean mixtures decreaseResponse temperatures and combustion speed. Excess fuel, as in richUSB Interface Mini-B type connector (support USB mixture, cools the engine somewhat, but the effect of 1.1/2.0 mass storage device class) unburned fuel as a coolant is generally overrated. The cooling is mainly due to other effects, like lower 3. Fault considered for Analysis combustion speed. These are two very different conditions,There are more than two hundreds of faults occurred in an as a lean mixture burns relatively slowly, and a rich mixtureautomobile engine and the level of the faults may be burns faster. It is indeed a key factor in ignition timing.different at different speed and different gear positions. But Indication of Bad Fuel Pressure is listed belowit is an endeavor to find the simple solution for some of the  The vehicle engine runs rough and often stumbles andfaults. Therefore five different types of faults are sputters.considered for analysis. The information and causes of the  The engine will not start after ignition. Usually, thisfaults are given as under. problem is encountered in the morning when you start I) Knocking Fault your vehicle. Many times, the engine starts, but halts An engine can ping (or knock) due to an improper shortly after starting.combustion process. A "spark knock" is the result of  The fuel pump in the vehicle becomes noisy at times.combustion occurring too early. Early combustion can  A bad fuel pressure regulator can also result in fouledoccur from carbon buildup inside the combustion chamber, spark plugs.a lean air/fuel mixture, and advanced ignition timing (spark  The fuel mileage of the engine decreases noticeably.plug firing too soon). In a properly-firing cylinder, thespark plug ignites the air/fuel mixture and a flame front V) Cylinder Head (2 piston working and 1 not working)starts on one side of the piston and burns across the top to Cylinder head is a crucial part of the combustionthe other side, which creates a rapid and evenly expanding engine. It consists of intake valve & exhaust valve.gas that pushes down on the top of the piston. When the Cylinder head fault occurs due to hotspots, overheating,air/fuel mixture is ignited prior to the spark plug firing, the components installed incorrectly. Due to this fault,two flame fronts collide, causing the pinging/knocking cylinders may lose compression & misfire and crackingnoise. That "tappet" noise may be only one sticking lifter 123 All Rights Reserved © 2012 IJARCSEE
    • ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE) Volume 1, Issue 6, August 2012occurs in cylinder head. The functions of piston in IC and the process of dividing of frames has been continued upengine are as follows. to 256 numbers of frames. The parameter extraction has  To transmit the force of explosion to the crank shaft. been carried out for normal as well as faulty signals of each  To form a seal so that the high pressure gases in the frame. Fig 1 shows the scatter plot for normal and faulty combustion chamber do not escape into the crank case. signal parameters for 256 frames. It is found that the most  To serve as a guide and a bearing for small end of the of the parameters of normal and faulty signals are connecting rod. overlapped but from Fig 1(f) and Fig 1(l), it is observed thatThis fault occurs due to excessive heat because of which at some places parameters are slightly separated but notthe piston expands and becomes tight in the cylinder. As a everywhere and hence the decision boundaries sepearitingresult some piston does not work. The main reasons for this different faults is highly complex and nonlinear [9,10].fault are Therefore neural network has been employed to separate  Insufficient lubrication of cylinder walls. out the faults on the basis of classification accuracy.  Overloading the engine.  Insufficient cooling system 6. Analysis using Artificial Neural NetworkLeakage of cooling water in the cylinders causes lubricantfilm breakdown. The extracted parameters have been applied as input to all ANN‟s and classification accuracy has been observed for 4. Data Acquisition each and every frame of the signals. Ten different types ofThe faults are occurring due to long usage of vehicle or lack ANNs have been employed for analysis such as Multilayerof maintenance of the vehicle. The data acquisition has Perceptrons (MLPs), Generalized Feed Forward (GFF),been done through a single sensor for five different types of Modular Neural Networks ( MNN), Jordan and Elmanfaults. The engine has been started in normal condition and Networks (JEN), Principal Component Analysis (PCA),then in different faulty condition by creating one by one Radial Basis Function (RBF), Self-Organizing Featurefaults into it. The signals have been recorded for normal Maps (SOFM), Time Lagged Recurrent Networks (and each faulty condition at different speed and different TLRN), Recurrent Networks (RN), and Support Vectorgear positions. The MP3 sound recorder has been used to Machine (SVM). Except SVM, in all nine ANN‟s, Tanh-record all types of normal and faulty acoustic signals. The Axon and Momentum are used as a Transfer Function &simple carbon microphone is used as a sensor. The Learning Rule respectively.specifications of microphone and MP3 sound recordershave been shown in Table 1B and Table 1C respectively. Table 1 to Table 9 shows the ACA for frames 1, 2, 4, 8, 16,The signals were recorded at 1000 rpm, 1200 rpm, 1400 32, 64, 128 and 256 respectively. The performance ofrpm, 1600 rpm, 1800 rpm and 2000 rpm for the neutral and ANN‟s can also be observed from bar chart shown in Tabledifferent gear positions I, II, III and IV. The recorded signal 1 to Table 9 for the respective frame numbers.for normal and faulty conditions has been plotted as shownin Fig 1. It has been observed from the Fig 1 that the From Table 1 it is found that the maximum three ACAnormal and faulty signals are overlapped. It has been also obtained for SVM, GFF and MLP are 88.383, 61.791 andobserved that the amplitude of the faulty signals is very 59.152, respectively for 1 frame of the signal.much greater than the normal signals. The FrequencyComponent of the recorded signal has been obtained using From Table 2 it is found that the maximum three ACAFFT transform. The FFT response of normal and faulty obtained for SVM, RBF and PCA are 87.161, 63.134 andsignal has been shown in Fig 1. It is noticed from the plot 62.077, respectively for 2 frames of the signal.that magnitude of faulty frequency component is greaterthan the normal frequency component. From Table 3 it is found that the maximum three ACA obtained for SVM, GFF and MLP are 97.323, 59.954 and 5. Feature Extraction 59.619, respectively for 4 frames of the signals.The parameters of the signals have been extracted using From Table 4 it is found that the maximum three ACAMATLAB software. The extracted parameters are Mean, obtained for SVM, SOFM and JEN are 99.576, 61.942 andMode, Energy, Maximum Value, Minimum Value, 58.805, respectively for 8 frames of the signals.Standard Deviation and Variance. These parameters havebeen extracted for normal and faulty signal separately after From Table 5 it is found that the maximum three ACAdividing the signal into different frames. The first frame is obtained for SVM, MLP and SOFM are 99.395, 61.950 andconsisting of 200000 of samples in it. Then this frame is 61.187, respectively for 16 frames of the signals.divided into two frames consisting of 100000 samples ineach frame. Later on each frame is divided into two frames 124 All Rights Reserved © 2012 IJARCSEE
    • ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE) Volume 1, Issue 6, August 2012From Table 6 it is found that the maximum three ACA 59.578, respectively for 32 frames of the signals.obtained for SVM, MLP and MNN are 99.428, 60.672 and 1 Normal & Faulty Signal Plot 2 Normal & Faulty FFT Plot 3 Scatter Plot a b c d e f g h i j k l m n o Fig 1: Signal Plot, FFT Plot and Scatter Plot.From Table 7 it is found that the maximum three ACAobtained for SVM, MNN and RBF are 98.642, 61.570 and All ten types of neural networks have been60.50, respectively for 64 frames of the signals. tested for optimal performance. From the analysis it is obvious that SVM performance is always superiorFrom Table 8 it is found that the maximum three ACA to all other nine neural networks.obtained for SVM, MNN and RBF are 98.642, 61.570 and60.550, respectively for 128 frames of the signals. The Support Vector Machine is implemented using the Kernel Adatron algorithm.From Table 9 it is found that the maximum three ACA The Kernel Adatron maps inputs to a highobtained for SVM, MNN and RBF are 99.241, 67.017 dimensional feature space, and then optimallyand 65.567, respectively for 256 frames of the signals. separates data into their respective classes by 125 All Rights Reserved © 2012 IJARCSEE
    • ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE) Volume 1, Issue 6, August 2012 𝒑 isolating those inputs which fall close to the data with the constraints 𝒊=𝟏 𝜶𝒊 𝒅𝒊 = 0 , 0 ≤ 𝜶𝒊 ≤ boundaries. Therefore, the Kernel Adatron is C, especially effective in separating sets of data which share complex boundaries. [ 11]. where C is a user defined constant and p is the number of learning data pairs (xi, di )[14,15]. C Classification in SVM is an example of Supervised represents the regularizing parameter andLearning. Known labels help indicate whether the determines the balance between the complexity ofsystem is performing in a right way or not. This the network, characterized by the weight vector winformation points to a desired response, validating theaccuracy of the system, or be used to help the system and the error of classification of data. For thelearn to act correctly. In the classification mode the normalized input signals the value of C is usuallyequation of the hyper plane separating two different much higher than 1 and adjusted by cross validation.classes is given by the relation The solution of (2) with respect to the Lagrangey(x) = 𝒘 𝑻 ø(x) = 𝒌 𝐰 𝐣 ∅ 𝐣 𝐱 + 𝐰 𝟎 = 0, …(1) multipliers produces the optimal weight vector 𝑤 𝑜𝑝𝑡 𝒋=𝟏 𝑁𝑠 as 𝑤 𝑜𝑝𝑡 = 𝑖=1 𝛼 𝑜𝑖 𝑑 𝑜𝑖 ø(𝑥 𝑜𝑖 ). In this equation Nswhere the vector ø(x) = [∅ 𝟎 𝒙 , .. , ∅ 𝒌 (𝒙)]T is composed of denotes the number of support vectors, i.e. theactivation functions of hidden units with ø(x) = 1, and learning vectors xi, for which the relations 𝒅 𝒊 [ 𝒘 𝒋w = [ w0,w1,………wk ] T is the weight vector of the ∅ 𝒋 ( 𝒙 𝒊 ) + 𝒘 𝟎 ] ≥ 1 - 𝛏 𝒊 (𝛏 𝒊 ≥ 0 , the nonnegative slacknetwork. variables of the smallest possible values) are The most distinctive fact about SVM is that the fulfilled with the equality sign. The output signallearning task is reduced to quadratic programming by y(x) of the SVM network in the retrieval mode (afterintroducing the so called Lagrange multipliers. All learning) is determined as the function of Kernels.operations in learning and testing modes are done in 𝑵𝒔SVM using Kernel functions. Kernel Adatron is y(x) = 𝒊=𝟏 𝜶 𝒐𝒊 𝒅 𝒊 K (𝒙 𝒐𝒊 , 𝒙 ) + 𝒘 𝟎 and thespecially used for the SVM [12]. explicit form of the nonlinear function ø (x) need The final problem of learning SVM, formulated as the not be known. The value of y(x) greater than 0 istask of separating learning vectors xi into two classes of associated with „ 1‟ membership of the particularthe destination values either di=1 or di= -1, with class and the negative one with „–1‟ membership ofmaximum separation margin, is reduced to the dual the opposite class. Although SVM separates themaximization problem of the quadratic function data only into two classes, the recognition of moreMax Q(α) = 𝒑 𝜶 𝒊- 𝟏 𝒑 𝒑 𝜶 𝒊 𝜶 𝒋 𝒅 𝒊 𝒅 𝒋 [𝒙 𝒊 , 𝒙 𝒋 ] - (2) classes is straightforward by applying either “one 𝒊=𝟏 𝟐 𝒊=𝟏 𝒋=𝟏 against one” or “one against all” methods. ANN Test CV Training Table 1. % ACA For Single Frame MLP 47.61304 49.76852 59.152 GFF 55.10221 51.85185 61.79152 MNN 52.77898 52.08333 58.50092 JEN 42.65392 57.87037 55.81364 PCA 38.10847 47.85053 51.25356 RBF 42.19096 45.53571 58.12133 SOFM 47.68639 51.09127 53.98358 TLRN 42.92328 41.60053 48.75817 RN 43.7987 47.22222 52.49539 SVM 36.52958 32.44048 88.38361 ANN Test CV Training Table 2. % ACA For Two Frames MLP 52.63952 59.00288 58.84305 GFF 54.6003 58.27701 57.69722 MNN 54.68943 51.34491 58.25636 JEN 56.28639 57.82403 55.71805 PCA 47.98391 55.81836 62.07718 RBF 48.3747 54.66793 63.13441 SOFM 48.1416 50.41777 57.02809 TLRN 32.92107 35.88944 41.37418 RN 49.69834 45.24764 52.9389 126 All Rights Reserved © 2012 IJARCSEE
    • ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE) Volume 1, Issue 6, August 2012 ANN Test CV Training Table 3. % ACA For Four FramesSVMMLP 47.94232 54.83748 50.72701 53.60667 87.16116 59.61907GFF 59.05996 54.31297 59.95468MNN 54.52436 56.49932 55.03668JEN 55.76993 56.70175 55.42383PCA 46.20854 48.82208 52.58299RBF 53.96523 49.98303 58.77953SOFM 52.68503 52.06978 59.38763TLRN 42.98195 50.63129 56.24709RN 47.48965 50.16106 52.29983SVM 61.32723 61.92324 97.32323 ANN Test CV Training Table 4. % ACA For Eight FramesMLP 57.8571 53.67521 55.68214GFF 55.56114 56.3335 58.15665MNN 56.18556 56.71232 58.56729JEN 53.91441 53.95399 58.80586PCA 47.90472 50.34316 45.92377RBF 58.30047 58.55369 58.1966SOFM 61.34095 58.86049 61.94225TLRN 53.97997 54.42487 55.87348RN 55.1552 54.98037 57.07625SVM 73.2304 71.58783 99.57627ANN Test CV Training Table 5. % ACA For Sixteen FramesMLP 59.98275 60.4395 61.95093GFF 57.04214 57.21383 58.39424MNN 58.49723 58.78499 57.90275JEN 56.82847 56.94362 57.19459PCA 50.7373 49.75545 50.57835RBF 57.69083 56.81055 58.71466SOFM 61.16253 60.14298 61.18753TLRN 51.78985 54.02329 52.75403RN 54.21101 54.27626 55.79178SVM 71.7676 75.67274 99.3951 ANN Test CV Training Table 6. % ACA For Thirty Two FramesMLP 58.32146 59.76383 60.67264GFF 56.36951 58.34367 58.17349MNN 57.21508 59.49828 59.5787JEN 56.78466 57.0798 59.19924PCA 53.01912 53.03101 53.01008RBF 55.49657 56.12123 58.23226SOFM 57.92605 59.52665 58.89398TLRN 51.69913 54.41298 54.31975RN 54.80937 55.68052 55.91651SVM 83.78459 84.85433 99.42813 ANN Test CV Training Table 7. % ACA For Sixty Four FramesMLP 55.46415 55.73544 58.40767GFF 55.77485 56.19408 57.90048MNN 58.7721 59.437 61.57078JEN 57.13754 58.5818 60.07232PCA 52.70591 51.68064 53.05507RBF 58.30167 57.91153 60.55084SOFM 56.66088 57.33402 58.38295TLRN 51.39181 52.08884 53.54042RN 53.97779 53.86846 55.88928SVM 84.99111 85.51761 98.6426 127 All Rights Reserved © 2012 IJARCSEE
    • ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE) Volume 1, Issue 6, August 2012 ANN Test CV Training Table 8. % ACA For One Twenty Eight FramesMLP 56.41622 56.05128 56.68612GFF 57.11301 57.93254 58.05246MNN 60.86075 61.5505 61.71411JEN 49.70916 49.9036 50.21574PCA 51.29568 51.26666 51.58578RBF 57.78853 57.93291 58.49105SOFM 52.89971 53.01305 53.28091TLRN 48.23438 49.08543 49.39634RN 53.07562 54.12797 54.77476SVM 87.02902 87.78235 97.92152 ANN Test CV Training Table 9. % ACA For Two Fifty Six FramesMLP 61.74299 61.20276 61.24997GFF 64.32415 63.83152 64.13582MNN 66.78093 66.7573 67.01756JEN 62.04285 62.05304 62.34698PCA 58.61241 58.03369 58.07959RBF 65.56578 65.47882 65.56725SOFM 60.78371 60.48339 60.16104TLRN 47.96672 48.02868 47.48147RN 47.48147 58.18068 58.22299SVM 94.25598 94.46717 99.24151 analysis for combined five faults. It has been 7. Optimal Solution observed that for 256 frame and for Epoch no equal From the above analysis it has been illustrated to 70, the maximum ACA observed is 96.299978that the performance of SVM is found to be the % for test data, 95.467171% for cross validationbest amongst all neural networks. Kernel Adatron data and 99.871512 %for training data as shown inis specifically used for SVM. From detailed Table10. SVM Test CV Training Table 10. ACA of SVMEp-2 92.409594 92.499301 97.964746Ep-4 93.211087 93.314933 98.155714Ep-6 93.507478 93.550603 98.312209Ep-8 93.707705 93.706554 98.403726Ep-15 93.847361 93.897755 98.459222Ep-20 93.849956 93.897875 98.479546Ep-25 93.849891 93.899895 98.469149Ep-25 93.857361 94.197755 98.466549Ep-30 93.847369 94.656745 98.469877Ep-40 93.857341 94.898595 98.469149Ep-50 93.867160 93.897755 98.469949Ep-60 93.887361 93.897755 98.769229Ep-70 96.299978 95.467171 99.871512Ep-80 95.255978 94.46899 99.501512Ep-90 94.258978 94.467971 99.564151Ep-100 94.273819 94.50035 99.634657 8. Conclusion faults. Faulty and normal signal have beenIn this work, a technique for Multiple Fault decomposed into a number of frames from one toDetection in Maruti Alto car engine using 256. The 256 frames yield the best Classificationacoustic signal has been proposed. It is not a Accuracy for SVM classifier, amongst ten neuralcomplete automotic fault detection system. But network classifiers used for the analysis. Kernelthe main advantage of this system is its simplicity Adatron is specifically used for the SVM and it isand compactness requiring a single sensor system. recommended for multiple fault detection. It isThe comparative analysis of Artificial Neural found that SVM can be used as a reasonableNetworks depicts that the reduction in frame size classifier for fault detection in an automobilewould increase the classification Accuracy of the engine. 128 All Rights Reserved © 2012 IJARCSEE
    • ISSN: 2277 – 9043 International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE) Volume 1, Issue 6, August 2012References IMechE Vol. 221 Part D.J. Automobile[1] S.N.Dandare and S.V.Dudul “Neural Network Engineering.based Air Filter Fault Detection in an Automobile [7] Kadarsah Suryadi & Eri Ricardo Nurzal, “ AEngine from Sound Signal” In First International Decision Support System for Car Fault DiagnosisConference on Sunrise Technologies SSVPS Using Expert System” . International Journals ofBSDCollege of Engneering, Dhule, on 14-&15th Information Science for Decision Making N02-Jan 2011. April 1998[2] S.N.Dandare and S.V.Dudul “Consistency of [8] Shubhalxmi Kher, P.K.Chand, & P.C.Sharma.MLP & SVM for Air Filter Fault Detection in an “Automobile Engine Fault Diagnosis UsingAutomobile Engine from Sound Signal” Neural Network” IEEE Intelligent TransportationInternational Journal of Computer Information Systems Conference Processing- Oakland (CA),Systems, Vol. 2, No. 2, 2011 USA – August 25-29 2001[3] Dandare S.N. & Dudul S.V. “Support Vector [9] Jain-Da Wu, Chiu – Hong Liu, “InvestigationMachine Based Multiple Fault Detection in an of engine fault diagnosis using discrete waveletAutomobile Engine Using Sound Signal” Journal transform and neural network”. Expert Systemof Electronic and Electrical Engineering, ISSN: with Applications 35(2008) 1200-1213.0976-8106 & E-ISSN: 0976-8114, Volume 3, [10]Robert.J.Howlett,Simon.D.Walters, Peter. A.Issue 1, 2012, pp.-59-63. Howson, Ian. A. Park, “Air Fuel Ratio [4] Matthew A. Franchek, Patrick J. Buehler & Measurement in an Internal Combustion EngineImad Makki, “ Intake Air Path Diagnostics for using a Neural Network.Internal Combustion Engine” Journal of Dynamic [11] Platt, J., Fast training of SVM usingSystems, Measurement , and Control, Janury sequential optimization (1998) Advances in ... pp.2007, Vol, 129/33. 185-208. , B. Scholkopf, C. Burges, and A. [5] E. Albas, T. Arikan, C. Kuzkaya ,“ In – Smola, Eds. CambridgeProcess Motor Testing Results Using Model [12] C. W. Hsu and C. J. Lin. “A comparison ofBased Fault Detection Approach.” methods for multiclass support vector machines.”[6] M.B.Celik and R.Bayir, “Fault detection in IEEE Trans. Neural Networks, vol. 13, pp. 415–internal combustion engines using fuzzy logic.” 425, Mar. 2002. 129 All Rights Reserved © 2012 IJARCSEE