A bearing prognosis framework based on deep wavelet extreme learning machine and particle filtering

Bearing prognosis plays an active role in preventing excessive or inadequate maintenance for major equipment. This paper develops a hybrid prognosis framework for bearings based on time-varying 3 Sigma criterion, deep wavelet extreme learning machine (DWELM) and particle filtering (PF). To be specific, a time-varying 3 Sigma criterion is proposed for bearing health monitoring to detect the fault occurrence time (FOT). Then, DWELM is established to evaluate the bearing performance degradation in degradation stage and construct a linear trend health indicator (HI) in a supervised way, termed as DWELM-HI. Compared to the original ELM, DWELM is equipped with more powerful feature representation and nonlinear approximation capabilities to map various nonlinear degradation trend to linear trend by deep structure and wavelet kernel. Additionally, a linear model is adopted to forecast the time evolution of the DWELM-HI. PF is collaboratively utilized to reduce random errors and estimate the probability of residual useful life (RUL). Finally, bearing prognosis is fully conducted on publicly available XJTU-SY bearing dataset to illustrate the effectiveness of the proposed method. The results show that the proposed method can detect an appropriate FOT and accurately estimate the RUL. Moreover, comparisons with other competing methods show that it performs better in bearing prognosis application.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a hybrid prognosis framework for bearings based on time-varying 3 Sigma criterion, DWELM, and PF. The framework can effectively detect fault occurrence time and accurately estimate the residual useful life of bearings, showing better performance compared to other competing methods.