This invention discloses a fault diagnosis
system for rotating equipment based on a large
language model, aiming to improve the accuracy and efficiency of fault diagnosis for industrial rotating equipment. The
system consists of six parts: data collection, data preprocessing, a neural
network model, a time-series
label knowledge base, a large
language model, and a user unit. The
system can not only detect faults in real time but also enable interaction through the user unit, allowing for analysis and prediction of the rotating equipment's operating status based on
user needs. The system optimizes input through data preprocessing techniques and enhances the large
language model using a time-series
label knowledge base. Based on this, and combining equipment operating data, historical records, and neural network diagnostic results, the large language model can accurately identify potential faults and generate actionable maintenance suggestions, thereby significantly improving equipment reliability and the safety of industrial production.