INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY (IJEET)

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1 INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY (IJEET) International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 ISSN (Print) ISSN (Online) Volume 3, Issue 2, July September (2012), pp IAEME: Journal Impact Factor (2012): (Calculated by GISI) IJEET I A E M E APPLICATION OF TIME-DOMAIN FEATURES WITH NEURAL NETWORK FOR BEARING FAULT DETECTION Amit Shrivastava 1 and Dr. S. Wadhwani 2 1 Research Scholar, 2 Reader, Department of Electrical Engineering, Madhav Institute of Technology & Science Gwalior, Madhya Pradesh , India Mobile No amit_12399@rediff.com, 2 sulochana_wadhwani1@rediffmail.com ABSTRACT The normal functioning of induction motors depend largely on ball bearing. Proper functioning and operation of ball bearing is the result of its maintenance by condition monitoring. Out of the various measures of condition monitoring vibration monitoring is the most extensively used and economical technique to detect, identify and distinguish fault in induction motors. In this research paper induction motor faults have been detected using time domain features of vibration signal. For the automated diagnosis of faults neural network has been designed using values of nine statistical features as input parameters which are: peak value, root mean square value, crest factor, kurtosis, skewness, clearance factor, impulse factor, shape factor and standard deviation. The artificial neural network designed was applied for three conditions of bearing i.e. for healthy bearing, bearing with inner raceway defect and bearing with outer raceway defect. The experimental observation shows that the proposed method is effective and is able to detect the faulty condition with high accuracy. KEYWORDS: Vibration monitoring, fault diagnosis, artificial neural network 1 INTRODUCTION The detection and diagnosis of incipient faults in induction motor is desirable for product quality assurance and improved operational efficiency. [1] On line condition based maintenance is the most widely used maintenance theme which detect the changes in the sensory signal and thereby indicate the presence and severity of machine faults and help in deciding the maintenance steps to be taken before 151

2 breakdown occur.[2] Fault in a rotating machine produces vibrations with distinctive characteristics frequency, which can be measured and compared with the reference ones, in order to perform the fault detection and diagnosis. [3-4] In the present work focus is on detection of bearing faults using vibration signal analysis. In the traditional model based approach of fault diagnosis a detailed mathematical model of the system under observation is constructed using varies variables and system parameters are compared with real parameters of working motor. Often the proposed models fail in practice and cannot cope with real working conditions due to inability to cope with real parameters of working motor. Thus a new automatic, economical and less time consuming online conditioning monitoring system is needed using computer. Artificial neural network is one such technique. For the development of the artificial neural network, features have been extracted from nine statistical parameters which are summarized in table 1 : [5] An ANN is composed of nodes which are the artificial neurons arranged in input, hidden and output layers. The number of nodes in input layer is decided by the number of input features and the nodes in each layer are interconnected with the nodes in the succeeding layer. The output layer is able to determine and display the nature of the exact fault and provide a solution for the fault to be overcome [6-7] There are two mean phases in the ANN s application: the training phase and the testing phase. The training phase is based on the type of future task to be solved by ANN. Training phase is followed by testing phase in which the representative features of the inputs are processed. After calculated the weights of the network, the values of the last layer neurons are compared with the expected output to verify the suitability of the design. This type of ANN is called a multilayer perceptron, and usually a popular back-propagation algorithm is used to train the network. [8] Table 1: Statistical parameters for ANN feature extraction. 1) Standard deviation 2) Root mean square 3) Crest factor value = ( ) = ( ) = 4) Kurtosis = 7) Impulse factor = 5) Skewness = 8) Shape factor h= 6) Clearance factor = 9) Peak value = ( ) ( ) 2. EXPERIMENTAL SETUP In the present experiment, the vibration signals are recorded by running the motor with healthy bearing to establish the base-line data. Then data are collected for different fault conditions. The defect was introduced intentionally in the form of a small hole firstly in the inner raceway and then in the outer raceway of the bearing. During the experiment, the vibration signals of a 3.75 kw, 50Hz, 440 V, 1440 rpm, 152

3 4-pole induction motor were recorded with the accelerometer having sensitivity 100mV/g. The bearings used in experiment were 9 balls, 10mm ball diameter, 62 mm outer ring and 30 mm inner ring diameter with 0 rad contact angle.in order to ensure the consistency in signal recording, the data was recorded repeated number of times. The data acquisition card collects and converts the analog signal into digital signal in order to save the acquired signal into the computer memory. The software for collection of data was developed in LabView. 3. RESULT AND DISCUSSION 3.1 Time-Domain Analysis: Experimental analysis was carried out by collection of time domain vibration signals for three different conditions of the bearing: (i) normal, (iii) outer race fault, and (iv) inner race fault. The vibration signature for a normal bearing is shown in Fig.2(a). Vibration fault signals for bearings with faults located in outer race and inner race are shown in Figs.2(b) and (c), respectively. Fig 2 Vibration signal of (a)healthy bearing (b) bearing with inner raceway defect (c) bearing with outer raceway defect. 3.2 Feature extraction Healthy Defective outer raceway Defective inner raceway Fig 3: Time domain features of vibration signals The time-domain features were extracted from the raw vibration signals for bearing fault diagnosis. The features extracted from the vibration signals are shown graphically in figure 3 for normal, defective inner race and defective outer race bearings. The feature extracted are peak value(pv), root mean square value(rms), 153

4 crest factor(crf), kurtosis(kv), skewness, clearance factor(clf), impulse factor(imf), shape factor(shf) and standard deviation(sd). As per the displayed results following inference can be drawn: the separations between normal and defective cases are maximum in the plots of shape factor and standard deviation. The plots of crest factor, skewness, and kurtosis show a narrow separation between normal and raceway defective cases. In the plots of the other four normalized features, there is sufficient separation between normal and raceway defective cases. 3.3 Application of ANN During the experiment multilayer feed forward neural network with back propagation training algorithms was used. The input features were normalized before application to neural network. In the present work the data was normalized between 0.1 and 0.9 to avoid sigmoid function. The time domain signal comprised of samples. The signals were split into 30 non-overlapping segments each. The segments were designed such that maximum features can be extracted in minimum computation time. Thus 30 sets of nine normalized features are obtained for each of the three conditions of the bearing. Out of these 30 sets randomly selected 20 sets were used to train the network and rest 10 sets were used for testing. During training, the first, second and third outputs are used for normal, defective outer race and defective inner race respectively of the bearing. Difference in normalized data at input and output layers were compared, and the result as compared to output layer is summarized in table 1. Mean square error is minimized upto < 0.1 by back propagation by adjusting synaptic weight between layers. S.No. Table 1 : Target vector for output layer node Bearing defect Target Node 1 Node 2 Node 3 1. Healthy DIR DOR Using the designed three layer ANN system the result obtained are quite satisfactory. Both testing as well as training results are 100% for healthy bearing and for defective bearing the training accuracy varies from 94-98% and testing accuracy varies from 92-96%. 5. CONCLUSION An automated fault diagnosis technique using vibration data had been proposed in this paper. Nine time domain features were used as input parameters for designing 154

5 of ANN. Algorithm using vibration data showed 96% success rate in the recognition of different bearing conditions for fault diagnosis and is fast. The experimental observation shows that the proposed method is effective and is able to detect the faulty condition with high accuracy and can be used in industries. 6. REFERENCES 1. Motor Reliability Working Group (1985), Report of large motor reliability survey of industrial and commercial installations, part 2, IEEE Trans. on Ind, Applications, vol. IA-21, no. 4, pp Carter D.L. (1966), A new method of processing rolling element bearing signals, Proceeding 20th Annual Meeting of the Vibration Institute 3. Igarishi T. and Hiroyoshi. (1980), Studies on Vibration and Sound of Defective Rolling Bearing Bulletin JSME, Vol 25, no. 204, pp: Wadhwani S., Gupta S.P., Kumar V. (2005), Wavelet Based Vibration Monitoring for Detection of Faults in Ball Bearing of Rotating Machines, Vol.86. IE(I) Journal-EL. 5. Yadav M. and Wadhwani S. (2011), Vibration analysis of bearing for fault detection using time domain features and neural network, International Journal of Applied Research in Mechanical Engineering,Vol1(1). 6. Ogbonnaya E. (1998), A. Condition monitoring of a diesel engine for electricity generation, M.Tech. Thesis, Department of Marine Engineering, Rivers State University of Science and Technology, Port Harcourt, Nigeria, pp. 68, 7. Bishop C. (1995), Neural networks for pattern recognition, Oxford University Press, New York, 8. Ogbonnaya E.A. (2004), Modelling vibration-basic faults in rotor shafts of a gas turbine, PhD Thesis, Department of Marine Engineering, Rivers State University of Science and Technology, Port Harcourt, Nigeria, pp