MWMOTE-MLLM: A cluster-level difficulty-aware oversampling multimodal large language model framework for imbalanced fault diagnosis of rolling bearings in horizontal reactors

The horizontal reactor serves as a critical piece of equipment in petrochemical production and operates under extreme conditions, including high temperature, high pressure, and strong corrosion. Under such conditions, rolling bearings are prone to faults caused by excessive vibration and lubrication failure, which may cause unscheduled system interruptions and pose safety risks. Owing to the randomness and imbalance of fault occurrence, the collected data typically exhibit a significant class imbalance distribution. Therefore, precise diagnosis of rolling bearing faults under imbalanced data conditions holds substantial engineering significance for improving operational reliability. To achieve precise perception of bearing fault categories, this article develops a diagnostic framework based on cluster-level classification difficulty oversampling and the multimodal large language model. The proposed framework employs the adaptive oversampling strategy guided by cluster-level classification difficulty to generate minority class samples with structural awareness and difficulty weighting, thereby effectively balancing the data distribution. Furthermore, vibration signals, time–frequency spectrograms, and textual information are integrated into a unified semantic space to construct a joint representation of the bearing’s comprehensive state, thereby improving the model’s ability to understand and discriminate fault conditions. The proposed framework yields an accuracy of 97.14% and an F1 score of 0.971 in rolling bearing fault diagnosis tasks under the 20 r/min operating condition based on the DEEPSEEK model, providing effective technical support for intelligent fault diagnosis under complex operating conditions.