Incrementally Contrastive Learning of Homologous and Interclass Features for the Fault Diagnosis of Rolling Element Bearings

Bearing condition is a non-negligible part of mechanical equipment health monitoring. Most of the existing bearing fault diagnosis methods are based on the premise that all data classes are known and lack the capability of incremental diagnosis of fault modes. However, in engineering practice, the initial monitoring data only provide normal condition, and the subsequent data of different classes of faults are collected gradually. To address this practical problem, we propose incremental contrastive learning (CL) of homologous and interclass features for bearing to achieve incremental diagnosis of bearing fault modes from single to multiple classes. Important homologous and interclass features of bearings are first extracted by CL. The obtained features are then employed to establish a distance threshold for the anomaly diagnosis of subsequent samples. Upon appearing anomalies incrementally up to a certain amount, novel classes are upgraded and fed back to the model. In this way, new class data are treated as incremental learning resources. The proposed method was evaluated using both benchmark bearing and gearbox bearing experiments. Results show supreme diagnostic performance compared to peer state-of-the-art approaches. The present method is intrinsic in extracting homologous and interclass features for practical bearing fault diagnostics.