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

TLDR: Simulation results show the proposed approach outperforms existing methods, especially at an early stage, and will aim at improving the method’s sensitivity in distinguishing faults similar to each other.

Abstract: 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. Note to Practitioners —This paper is motivated by the problem of monitoring and diagnosis of compound faults in rolling bearings at the early stage, which are seldom considered in existing methods. In this paper, we propose a new approach by using statistical signal detection method and wavelet transform to handle the fault signals. This work aims at monitoring vibration signals and diagnosing fault types. Our simulation results show the proposed approach outperforms existing methods, especially at an early stage. Our future work will aim at improving the method’s sensitivity in distinguishing faults similar to each other.