Bearing Fault Classification Based on Wavelet Transform and Artificial Neural Network

Abstract

In the present paper, hybrid bearing faults classification scheme based on wavelet transformation and neural network is proposed. Basically, the proposed methodology identifies four different types of bearing faults. For classification of the faults, vibration signals have been utilized. The vibration signals are first decomposed into components in different sub-bands using discrete wavelet transformation. Subsequently, variance and variance of autocorrelation value extracted from decomposed signal have been used as input features for the neural network. The time interval between the impacts of original signal is also exploited to characterize the bearing vibration signals. A neural network follows to classify the extracted feature vector. Trained neural networks are able to classify different states of the bearing with 100% accuracy. The proposed procedure requires only a few input features, resulting in simple preprocessing and faster training. Effectiveness of the proposed method is illustrated using the bearing vibration data obtained experimentally.

Keywords:

• Autocorrelation
• Bearing
• Discrete wavelet transform
• Variance
• Vibration

Additional information

Notes on contributors

Ashwani Kumar Chandel

Ashwani Kumar Chandel graduated in Electrical Engineering from Kerala University. He post graduated from Punjab Engineering College (PEC) Chandigarh. He was awarded Ph.D. degree from Indian Institute of Technology (IIT), Roorkee, India in 2005. Dr. Kumar joined Department of Electrical Engineering, National Institute of Technology, Hamirpur, HP India, as faculty in 1991, where presently he is working as an Associate Professor and DPGC convener of the EED. His research work has been published in various International Journals of repute including IEEE, IEE, and Elsevier Science, Taylor & Francis and others. He has worked extensively in the area of harmonic estimation & elimination and currently his interest continues in this field. He has a CSIR project to his credit. He has guided 20 M.Tech dissertations, 2 PhD thesis and a number of B.Tech. He is a life member of ISTE (I). E-mail: [email protected]

Raj Kumar Patel

Raj Kumar Patel graduated in Electrical & Electronics Engineering from Uttar Pradesh Technical University, 2006. He is at present an M. Tech. student of Electrical Engineering at NIT Hamirpur (H.P.). His area of interest is fault diagnosis of power apparatus using signal processing techniques E-mail: [email protected]