翻译部分英文原文Fault Diagnosis of Three Phase Induction Motor Using Neural Network TechniquesAbstract：Fault diagnosis of induction motor is gaining importance in industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown.Due to environmental stress and many others reasons different faults occur in induction motor. Many researchers proposed different techniques for fault detection and diagnosis.However,many techniques available presently require a good deal of expertise to apply them successfully.Simpler approaches are needed which allow relatively unskilled operators to make reliable decisions without a diagnosis specialist to examine data and diagnose problems.In this paper simple,reliable and economical Neural Network(NN)based fault classifier is proposed,in which stator current is used as input signal from motor.Thirteen statistical parameters are extracted from the stator current and PCA is used to select proper input.Data is generated from the experimentation on specially designed 2 Hp,4 pole 50 Hz.three phase induction motor.For classification,NNs like MLP,SVM and statistical classifiers based on CART and Discriminant Analysis are verified.Robustness of classifier to noise is also verified on unseen data by introducing controlled Gaussian and Uniform noise in input and output.Index Terms: Induction motor, Fault diagnosis, MLP, SVM,CART, Discriminant Analysis, PCA I.INTRODUCTIONINDUCTION motors play an important role as prime movers in manufacturing,process industry and transportation due to their reliability and simplicity in construction.In spite of their robustness and reliability,they do occasionally fail,and unpredicted downtime is obviously costly hence they required constant attention.The faults of induction motors may not only cause the interruption of product operation but also increase costs,decrease product quality and affect the safety of operators.If the lifetime of induction machines was extended, and efficiency of manufacturing lines was improved,it would lead to smaller production expenses and lower prices for the end user.In order to keep machines in good condition, some techniques i.e.,fault monitoring, fault detection, and fault diagnosis have become increasingly essential.The most common faults of induction motors are bearing failures, stator phase winding failures ,broken rotor bar or cracked rotor end-rings and air-gap irregularities.The objective of this research is to develop an alternative neural network based incipient fault-detection scheme that overcome the limitations of the present schemes in the sense that,they are costly, applicable for large motors, furthermore many design parameters are requested and especially concerning to long time operating machines, these parameters cannot be available easily.As compared to existing schemes, proposed scheme is simple, accurate, reliable and economical. This research work is based on real time data and so proposed neural network based classifier demonstrates the actual feasibility in a real industrial situation. Four different neural network structures are presented in this paper with all kinds of performances and about 100%classification accuracy is achieved.II.FAULT CLASSIFICATION USING NNThe proposed fault detection and diagnosis scheme consists of four procedures as shown in Fig.1:1. Data collection & acquisition2. Feature extraction3. Feature selection4. Fault classificationA. Data Collection and Data acquisitionIn this paper the most common faults namely stator winding interturn short(I),rotor dynamic eccentricity(E)and both of them(B)are considered.Fig.1.General Block Diagram of proposed classifierFor experimentation and data generation the specially designed 2 HP, three phase,4 pole,415V,50 Hz induction motor is selected. Experimental set up is as shown in Fig.2.Fig.2.Experimental SetupThe load of the motor was changed by adjusting the spring balance and belt.Three AC current probes were used to measure the stator current signals for testing the fault diagnosis system. The maximum frequency of used signal was 5 kHz and the number of sampled data was 2500.From the time waveforms of stator currents as shown in Fig.3,no conspicuous difference exists among the different conditions.Fig.3.Experimental Waveforms of Stator currentB. Feature ExtractionThere is a need to come up with a feature extraction method to classify faults.In order to classify the different faults,the statistical parameters are used.To be precise, sample statistics will be calculated for current data.Overall thirteen parameters are calculated as input feature space.Minimum set of statistics to be examined includes the root mean square (RMS)of the zero mean signal(which is the standard deviation),the maximum, and minimum values the skew ness coefficient and kurtosis coefficient. Pearsons coefficient of skew ness,defined by: