Large language models for prognostic analysis in mechanical fault diagnosis - Hao Zhang; Wei Wang; Longfei Zhang; Siyu Shao; Qingli Wang; Jiandong Li; Jun Hu (PLoS ONE, 2025)
With the in-depth development of industrial intelligence, as the core basic component of high-end equipment, the fault diagnosis and health management of rotating machinery has become a key link to ensure the reliability of complex systems. Although the intelligent diagnosis technology based on mechanical vibration signals has made remarkable progress, in complex mechanical systems, it is difficult to comprehensively cover the fault feature space using vibration signal data only.This paper proposes an intelligent diagnosis framework based on a large language model. By empowering the large language model through multimodal data feature fusion and constructing a ternary data system of “raw vibration signals - time-frequency spectrum features - fault knowledge text”, the framework realizes cross-modal joint representation of mechanical fault features and breaks through the bottlenecks of traditional methods, such as insufficient feature extraction capability under complex working conditions and limited cross-scenario generalization. The framework innovatively integrates the deep semantic understanding ability of pre-trained large language models with mechanical fault mechanisms. Through the method of plugging in principle knowledge bases, the model can not only output fault location results but also simultaneously generate interpretable reports including fault cause analysis and maintenance strategy suggestions.The model proposed in this paper has been strictly tested on bearing datasets. Experimental results demonstrate that the model exhibits excellent performance and adaptability in different industrial scenarios.