Adaptive Weighted Signal Preprocessing Technique for Machine Health Monitoring

Abstract

Machine health monitoring (MHM) aims to timely detect the incipient faults and monotonically assess the machine degradation tendency for prediction of remaining useful life (RUL), which is the basis of condition-based maintenance. Construction of a health index (HI) is a core step to realize the aforementioned purposes. Among the existing HIs, sparsity measures (SMs), including kurtosis, smoothness index, Gini index, and negative entropy, have shown promising applications in fault diagnosis of rotating machines because they are widely used to quantify the repetitive transients caused by rotating faults. However, drawbacks of SMs for MHM still exist and they are that: 1) SMs are too fluctuating to detect the incipient faults; 2) SMs are prone to be affected by impulsive noise; and 3) SMs might not exhibit monotonic degradation tendency. To enhance the abilities of SMs as HIs for MHM, some improvements on SMs, coined as an adaptive weighted signal preprocessing technique (AWSPT), are proposed in this article. Subsequently, theoretical values of AWSPT-based SMs under healthy states are investigated. Numerical experiments reveal that AWSPT-based SMs can quantify the cyclostationarity and they are robust to the effects of impulsive noise. Bearing and gear run-to-failure data sets are used to show that the proposed AWSPT-based SMs can simultaneously detect the early bearing and gear faults and provide the monotonic degradation tendency. Moreover, AWSPT-based SMs are more effective in selecting an optimal envelope demodulation frequency band than traditional SMs.