Fault diagnosis of rolling element bearing based on artificial neural network

• Published in: Journal of mechanical science and technology Vol. 33; no. 2; pp. 505 - 511
• Main Authors: Gunerkar, Rohit S., Jalan, Arun Kumar, Belgamwar, Sachin U
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Mechanical Engineers 01.02.2019; Springer Nature B.V; 대한기계학회
• Subjects: Adaptive algorithms; Artificial intelligence; Artificial neural networks; Back propagation networks; Classification; Control; Dynamical Systems; Engineering; Expert systems; Fault detection; Fault diagnosis; Feature extraction; Industrial and Production Engineering; Mechanical Engineering; Multilayer perceptrons; Neural networks; Roller bearings; Signal processing; Vibration; Wavelet transforms; 기계공학; Wavelet transform; Artificial neural network; Fault detection; K-nearest neighbor
• ISSN: 1738-494X; 1976-3824
• DOI: 10.1007/s12206-019-0103-x

This paper proposes the expert system for accurate fault detection of bearing. The study is based upon advanced signal processing method as wavelet transform and artificial intelligence technique as artificial neural network (ANN) and K-nearest neighbor (KNN), for fault classification of bearing. An adaptive algorithm based on wavelet transform is used to extract the fault classifying features of the bearing from time domain signal. These features have been used as inputs to proposed ANN models and the same features have also been used for KNN. Dedicated experimental setup was used to perform the test upon the bearing. Single data set for four fault conditions of bearing is collected to train ANN and KNN. The processed and normalized data was trained by using backpropagation multilayer perceptron neural network. The results obtained from ANN are compared with KNN, ANN results proved to be highly effective for classification of multiple faults.