Multilevel Feature Alignment Method for Advancing Remaining Useful Life Prediction of Rolling Bearing

• Published in: IEEE internet of things journal Vol. 12; no. 12; pp. 22023 - 22035
• Main Authors: Pei, Xuewu, Gao, Yiping, Li, Xinyu, Gao, Liang, Zhao, Xingxin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.06.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alignment; Data mining; Data models; Degradation; Electronic mail; Employee welfare; Failure; Industrial Internet of Things; Internet of Things; Life prediction; Manufacturing; Multilevel feature alignment (MLFA); Predictive models; remaining useful life (RUL) prediction; Roller bearings; Rolling bearings; Root-mean-square errors; State estimation; supervised RUL label; three-level feature alignment (TLFA) strategy; Training; Useful life
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2025.3549728

Rolling bearings are the key components of various equipment and easily subject to failure, remaining useful life (RUL) prediction technology can grasp their health statuses to make a reasonable maintenance plan. Transfer learning methods for rolling bearing cross-domain RUL prediction focus on the direct alignment of the degradation process between the target domain and source domain. However, the working condition and degradation process of rolling bearing to be predicted are unknown, which are different from the target domain or source domain. The caused data distribution discrepancy has seriously affected the RUL prediction acceptance. To solve this problem, a multilevel feature alignment (MLFA) method for advancing RUL prediction without target domain data for training is proposed. Specifically, a proposed weighted TOPSIS is implemented for supervised label making to train a robust RUL prediction prior model. The three-level feature alignment (TLFA) strategy, which involves feature alignment by the proposed adaptive nonlinear state estimation, envelope spectrum (ES), ES-combined deep convolutional autoencoder, is developed to mitigate the data distribution discrepancy between the predicted and prior entities. TLFA transforms prediction tasks from cross-domain to different failure behaviors. Extensive experiments conducted on both public and industrial scene run-to-failure bearing datasets validated the superiority of the MLFA. These results from comparison experimental show that MLFA improves mean absolute error and root mean absolute error (RMSE) about 0.061 and 0.054 individually than some state-of-the-art methods.