Transfer remaining useful life estimation of bearing using depth-wise separable convolution recurrent network. (May 2021)

• Record Type: Journal Article
• Authors: Huang, Gangjin; Zhang, Yuanliang; Ou, Jiayu
• Is Part Of: Measurement. Volume 176(2021)
• Publication Date: 2021-05
• Subjects: Depth-wise separable convolution recurrent network -- Transfer learning -- Rolling bearing -- Remaining useful life estimation; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109090
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: Rolling bearing is a vital part of the machinery, whose remaining useful life (RUL) estimation plays a critical role in ensuring the safety and maintenance decision-making. However, in most industrial applications, it is difficult to obtain run-to-failure data under complex operating conditions, which is inefficient for deep learning approaches. To solve the above problem, a new approach using transfer depth-wise separable convolution recurrent network (TDSCRN) for RUL estimation of bearing is presented. A novel prediction model so-called depth-wise separable convolution recurrent network (DSCRN) is designed and trained by the source-domain dataset. The parameters and model of DSCRN are transferred to the target-domain, and then TDSCRN is obtained for RUL estimation task. Two public run-to-failure datasets are used to validate the performance of the presented method. The results indicate that this framework can improve estimation accuracy and robustness in complex operating conditions.