The condition monitoring and fault diagnosis of rolling element bearing is a very important research content in the field of gas turbine health management. In this paper, a hybrid fault diagnosis approach combining S-transform with artificial neural network (ANN) is developed to achieve the accurate feature extraction and effective fault diagnosis of rolling element bearing health status. Considering the nonlinear and non-stationary vibration characteristics of rolling element bearing under stable loading and rotational speeds, S-transform and singular value decomposition (SVD) theory are firstly used to process the vibration signal and extract its time-frequency information features. Then, radical basis function (RBF) neural network classification model is designed to carry out the state pattern recognition and fault diagnosis. As a practical application, the experimental data of rolling element bearing including four health status are analyzed to evaluate the performance of the proposed approach. The results demonstrate that the present hybrid fault diagnosis approach is very effective to extract the fault features and diagnose the fault pattern of rolling element bearing under different rotor speed, which may be a potential technology to enhance the condition monitoring of rotating equipment. Besides, the advantages of the developed approach are also confirmed by the comparisons with the other two approaches, i.e. the Wigner-Ville (WV) distribution and RBF neural network based method as well as the S-transform and Elman neural network based one.
- International Gas Turbine Institute
A Fault Diagnosis Approach for Rolling Element Bearing Based on S-Transform and Artificial Neural Network
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Zhao, N, Zheng, H, Yang, L, & Wang, Z. "A Fault Diagnosis Approach for Rolling Element Bearing Based on S-Transform and Artificial Neural Network." Proceedings of the ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. Volume 6: Ceramics; Controls, Diagnostics and Instrumentation; Education; Manufacturing Materials and Metallurgy. Charlotte, North Carolina, USA. June 26–30, 2017. V006T05A003. ASME. https://doi.org/10.1115/GT2017-63253
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