Fault classification performance of induction motor bearing using AI methods

This paper presents an approach of intelligent fault classification of induction motor bearing (IMB) using several artificial intelligent (AI) methods. The most common AI methods are FeedForward Neural Network (FFNN), Elman Network (EN), Radial Basis Function Network (RBFN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The data of IMB fault is obtained from Case Western Reserve University website in form of vibration signal. For further analysis these datas are converted from time domain into frequency domain through Fast Fourier Transform (FFT) in order to acquire more fault signs during pre-processing stage. Then, during features extraction stage, a set of 16 features from vibration and pre-processing signal are extracted. Subsequently, a distance evaluation technique is used as features selection, in order to select only salient features. Lastly, during fault classification several AI methods are examined, where results are compared and the optimum AI method is selected.