A Reinforced Noise Resistant Correlation Method for Bearing Condition Monitoring

Abstract

Condition monitoring plays a significant role in guaranteeing the reliability and safety of rotating machinery, which aims to detect an incipient fault and assess the degradation tendency. The construction of a health index (HI) is a crucial step to realize above tasks. At present, kurtosis, crest factor, and so on have been recognized as popular HIs to depict the operating condition. However, shortcomings of these classical HIs still exist: 1) classical HIs are prone to be affected by strong white Gaussian noise; 2) classical HIs are not sensitive to incipient faults. To deal with these two shortcomings, a reinforced noise resistant correlation method is proposed in this paper. Firstly, a new signal is constructed using the steps of segmenting and averaging to suppress the interference of noise. Then, a novel correlation function is used to get the hidden period. The proposed HI is constructed based on the discrete version of this correlation function to increase the sensitivity of incipient faults. Subsequently, theoretical values of the proposed HI under healthy states are investigated. The effectiveness of the method is demonstrated using simulated degradation processes and two accelerated degradation datasets of rolling element bearings. Through comparisons with other classical HIs, the proposed HI can simultaneously suppress the interference of strong noise and detect incipient faults. The comparison results identify the effectiveness of the proposed method in monitoring the condition of rotating machinery. Note to Practitioners—This work aims to provide a novel health index construction method for rotating machinery condition monitoring. The key issues involved in this problem include 1) how to capture the complex relationship between the measured vibration signals and the underlying health condition of rotating machinery; 2) how to suppress the interference of environmental noise. The novelty of this work is that it develops a method for condition monitoring considering the measured signals with strong white Gaussian noise. It properly captures the complex relationship between measured signals and the underlying health condition. There are four main steps to implement this approach: 1) collecting the vibration signals from rotating machinery; 2) constructing new periodic signal to suppress the interference of strong background noise; 3) constructing novel correlation functions to establish the relationship between the vibration signals and the underlying health condition of the rotating machine; 4) modeling the HI. A simulation and two real cases of the bearing degradation process are used to show that the proposed HI can simultaneously suppress the interference of strong noise and detect the incipient faults compared with some classical HI construction methods. In the future, the problem of how to address the measured signals contaminated by non-Gaussian noise and the machinery containing compound faults should be solved to extend this method in a complex system.