5 papers
PHM-Bench: A Domain-Specific Benchmarking Framework for Systematic Evaluation of Large Models in Prognostics and Health Management
Puyu Yang, Laifa Tao, Zijian Huang +11
With the rapid advancement of generative artificial intelligence, large language models (LLMs) are increasingly adopted in industrial domains, offering new opportunities for Progno…
UBMF: Uncertainty-Aware Bayesian Meta-Learning Framework for Fault Diagnosis with Imbalanced Industrial Data
Zhixuan Lian, Shangyu Li, Qixuan Huang +5
Fault diagnosis of mechanical equipment involves data collection, feature extraction, and pattern recognition but is often hindered by the imbalanced nature of industrial data, int…
Pre-Trained Large Language Model Based Remaining Useful Life Transfer Prediction of Bearing
Laifa Tao, Zhengduo Zhao, Xuesong Wang +8
Accurately predicting the remaining useful life (RUL) of rotating machinery, such as bearings, is essential for ensuring equipment reliability and minimizing unexpected industrial…
LLM-R: A Framework for Domain-Adaptive Maintenance Scheme Generation Combining Hierarchical Agents and RAG
Laifa Tao, Qixuan Huang, Xianjun Wu +5
The increasing use of smart devices has emphasized the critical role of maintenance in production activities. Interactive Electronic Technical Manuals (IETMs) are vital tools that…
LLM-based Framework for Bearing Fault Diagnosis
Laifa Tao, Haifei Liu, Guoao Ning +3
Accurately diagnosing bearing faults is crucial for maintaining the efficient operation of rotating machinery. However, traditional diagnosis methods face challenges due to the div…