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Research on Large Model for General Prognostics and Health Management of Machinery
LEI Yaguo, LI Xiwei, LI Xiang, LI Naipeng, YANG Bin
Journal of Mechanical Engineering
2025, 61 (6):
1-13.
DOI: 10.3901/JME.2025.06.001
In recent years, various deep learning-based health management models for mechanical equipment have made significant progress. However, existing models tend to be smaller in scale and are typically designed to handle data from specific frequencies, speeds, or modes, focusing on particular components such as gears and bearings to perform tasks like monitoring, diagnosis, and prediction. These models struggle to adapt to new scenarios and lack the capability for continuous evolution. With the increasing precision and complexity of high-end equipment, there is a growing demand for highly general, scalable, and evolvable "one-stop" health management services. Inspired by the trend of generalization in large language models like ChatGPT, which excel in handling diverse data, tasks, and scenarios, a large model for general prognostics and health management of machinery is proposed. First, multimodal data is resampled in the angular domain and segmented to token sequence. Then, the data is input into a Transformer-based information integration foundational model to extract health and degradation information into specific tokens. Finally, these specific tokens are used to perform downstream tasks such as monitoring, diagnosis, and prediction. The proposed large model's baseline performance, multitask synergy, and scalability were verified using fault and long-term degradation datasets. The results show that the proposed large model can simultaneously perform condition monitoring, fault diagnosis, and remaining useful life prediction for multiple objects like bearings and gears. Additionally, the diagnostic and predictive multitasks can effectively collaborate, mutually enhancing performance, and achieving better results compared to single-task models. In few-shot learning and continual learning scenarios, the large model can be rapidly deployed and continuously evolved. Therefore, the proposed large model features high generality, scalability, and sustainability, and is expected to provide universal "one-stop" health management services for mechanical equipment.
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