Patch ModernTCN-Mixer: a dual-task temporal convolutional network framework for hybrid implementation of first prediction time detection and remaining useful life prognosis

作者：Dechen Yao, Bo Tang, Jianwei Yang, Wenbo Yue, Qiang Li, Shudong Guo · 发表于：Measurement Science and Technology · 年份：2024 · DOI：10.1088/1361-6501/ad6892 · 被引用次数：14 · 研究领域：Machine Fault Diagnosis Techniques、Fault Detection and Control Systems、Phonocardiography and Auscultation Techniques

Abstract Over the past few years, notable advancements have been achieved in predicting the remaining useful life (RUL) of rotating equipment through deep learning methodologies. However, existing RUL prediction models tend to implement the determination of the first prediction time (FPT) for stage division separately from the RUL prediction, ignoring their potential correlation in the degradation process. In response to this issue, this paper proposed a dual-task prediction network framework based on Patch ModernTCN-Mixer (PMTCN-Mixer), which adaptively and jointly achieved FPT detection and RUL prediction. Firstly, the network designed a hard sharing parameter feature extractor module, Patch ModernTCN, which is used to learn the temporal dependence and spatial correlation of degradation features. Secondly, to eliminate redundant information and noise during the feature extraction phase while enhancing the precision of detection and prognosis, a dynamic semi-soft thresholding (DST) module was constructed. Lastly, the dual-task learning network PMTCN-Mixer was constructed by combining Patch ModernTCN with DST, utilizing GradNorm to balance the gradients between FPT detection and RUL prediction tasks to achieve fusion prediction. The performance of the PMTCN-Mixer framework was validated on the XJTU-SY Bearing Datasets and IEEE PHM 2012 Challenge Datasets, compared with the state-of-the-art network’s optimal results, the RUL prediction metrics root mean square error, mean abso...