A Wavelet-Based Statistical Approach for Monitoring and Diagnosis of Compound Faults With Application to Rolling Bearings

作者：Fan, Wei; Zhou, Qiang^*; Li, Jian; Zhu, Zhongkui

来源：IEEE Transactions on Automation Science and Engineering, 2018, 15(4): 1563-1572.

DOI：10.1109/TASE.2017.2720177

• Compound faults monitoring and diagnosis
• discrete wavelet transform (DWT)
• generalized likelihood ratio test (GLRT)
• rolling bearing
• statistical signal detection

This paper proposes a wavelet-based statistical signal detection approach for monitoring and diagnosis of bearing compound faults at an early stage. The bearing vibration signal is decomposed by an orthonormal discrete wavelet transform to obtain its energy dispersions at multiple levels. We investigate the statistical properties of the decomposed signal energy under both the normal and faulty conditions, based on which a generalized likelihood ratio test is developed. An exponentially weighted moving average control chart is then constructed to detect faults at an early stage. Simulation studies and a real case study are conducted to demonstrate the effectiveness of the proposed method. Furthermore, the comparison studies show that the proposed method outperforms the empirical mode decomposition method and Hilbert envelope spectrum analysis method.

• 出版日期2018-10
• 单位苏州大学; 香港城市大学; 机械制造系统工程国家重点实验室; 西安交通大学