Efficient fine-tuned preventive monitoring models of bearing failures without prior on-site fault data

Unexpected bearing failures always cause severe production disruptions. Such faults often arise under specific operating conditions correlated with the on-site environment. Therefore, it is highly desired to fine-tune the preventive monitoring models with prior fault data on site. However, bearings in industrial equipment typically exhibit low failure rates, and thus prior fault data cannot be collected in time before faults occur. To tackle this issue, we propose a novel preventive monitoring technology that utilizes a contrastive fine-tuning method for a pre-trained model, only leveraging on-site normal data from bearings. This approach aligns the features of the pre-trained model with practical bearing characteristics, enhancing failure prediction capabilities. Experiments demonstrate its efficiency and effectiveness in the prediction of practical bearing failures. Compared to other popular methods, it can identify an incipient fault at least 63 min earlier, i.e., 312 min before the bearing fault occurs.