Fault diagnosis of induction motors using signal processing based methods and optimal feature selection
Fault detection and diagnosis in rotating machines have been used widely in commercial systems over the past few decades. Numerous works on machine conditions have been implemented with the aid of the MCSA (Motor Current Signature Analysis) method, the vibration-based methods, etc. The purpose of these methods is to detect and diagnose faults in an early stage and therefore allow contingency plans to be put into place before the problems worsen. The dynamic and vibratory behaviours of the machine, such as vibration, sound, and temperature… are affected if the running condition is changed. The behaviours can be useful indicators to detect problems within the machine as they vary abnormally from a standard when a fault occurs. Of the many signals which can be measured, the vibration signal has been the most useful to monitor the machine condition as in many cases the time domain vibration signals are sufficient to diagnose and can be easily measured with accelerometers. The signal processing methods for induction motor fault detection have recently received great attention because they do not need a typical mathematical model. Many signal processing diagnostic procedures have been studied in this work to identify faults of the machines. The decision tree, support vector machine (SVM), artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS), and k-nearest neighbour (K-NN) have been applied to diagnose the condition of machines with rather high accuracy. These methods have used vibration data as an indicator for monitoring the fault conditions. In this work, the vibration data are measured in three dimensions to collect as much information as possible. Then an optimal feature selection is proposed in this work for improving the classification performance of the diagnostics system. The classification results have proved the efficiency of the proposed optimal feature selection and the suitability of vibration data as an indicator for induction motor fault diagnosis.