Degradation-Trend-Aware Deep Neural Network With Attention Mechanism for Bearing Remaining Useful Life Prediction

作者：Yongkang Liu, Donghui Pan, Haifeng Zhang, Kai Zhong · 发表于：IEEE Transactions on Artificial Intelligence · 年份：2023 · DOI：10.1109/tai.2023.3333767 · 被引用次数：13 · 研究领域：Machine Fault Diagnosis Techniques、Mechanical Failure Analysis and Simulation、Reliability and Maintenance Optimization

Remaining useful life (RUL) prediction of bearings has extraordinary significance for prognostics and health management (PHM) of rotating machinery. RUL prediction approaches based on deep learning have been dedicated to finding a nonlinear mapping relationship between non-stationary monitoring data and RUL. However, most existing approaches pay little attention to the degradation trend of diverse health stages of bearing and lack the discriminative power of crucial degradation features, resulting in the loss of some important information associated with RUL. To address this challenge, this article proposes a novel RUL prediction framework based on degradation-trend-aware deep neural network with attention mechanism (DTADAN). Firstly, the multi-direction features with evident degradation trend are extracted via the analysis of bearing vibration signal from both time domain and time-frequency domain. Next, the deep neural network architecture with attention mechanism is utilized to adaptively learn the critical degradation features beneficial for RUL prediction. Distinct from the existing approaches, the proposed framework is able to dynamically extract key degradation features of the bearing including degradation trend information and effectively fuse multi-direction information to improve RUL prediction accuracy. The performance of the proposed approach is evaluated via case studies on XJTU-SY bearing dataset and PRONOSTIA bearing dataset. Compared with other state-of-the-ar...