A Multi-Scale Temporal Convolutional Network Approach for Remaining Useful Life Prediction of Rolling Bearings
Journal Title: Precision Mechanics & Digital Fabrication - Year 2025, Vol 2, Issue 1
Abstract
Rolling bearings, as key components of rotating machinery, play a crucial role in the reliable operation of equipment. Over time, rolling bearings inevitably experience wear and fatigue, leading to damage. Accurate prediction of their Remaining Useful Life (RUL) is of paramount importance. This paper proposes an RUL prediction model based on the Multi-Scale Temporal Convolutional Network (MSTCN). The model effectively integrates both time-domain and frequency-domain information from bearing vibration signals through a multi-scale feature extraction module, enabling it to capture feature representations at different time scales. Additionally, the MSTCN's powerful temporal modeling capabilities allow it to capture long-term dependencies and short-term fluctuations in the bearing degradation process. Experimental results show that, compared to traditional methods, the proposed MSTCN model significantly improves the accuracy and stability of RUL predictions on the PHM2012 bearing dataset, demonstrating the effectiveness of the method in predicting the RUL of rolling bearings.
Authors and Affiliations
Tichun Wang, Qiji Teng
Keywords
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- EP ID EP767844
- DOI https://doi.org/10.56578/pmdf020103
- Views 21
- Downloads 0
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