Remaining useful life prediction of bearings using multi-source adversarial online regression under online unknown conditions

Most transfer learning-based methods require sufficient data for training, but the target data may not be available. Also, the health prognosis of target data under unknown conditions is a challenging online few-shot issue, which is still not effectively addressed. In addition, the limited knowledge learned from a single source domain may further limit the extraction of degradation features. To address these challenges, a multi-source adversarial online regression (MAOR) method considering the pseudo domain extension is proposed to predict the remaining useful life of bearings under online unknown conditions. It can obtain a target data stream for each round and perform an online learning task. Specifically, when generating pseudo-domains, the domain-level adaptation is designed by considering the heterogeneous distribution between pseudo-domains and the similarity of manifold between pseudo and source domains. Also, the feature-level adaptation is embedded in a multi-source adversarial adaptation architecture to learn robust domain-invariant features and build the offline model. An offline-online prediction framework is developed to predict online target data streams and update the online model with adaptive weighting. To validate the superiority of the proposed MAOR, two bearing cases are extensively evaluated. The experiment results show that MAOR can achieve significant outcomes in different online tasks with competitive performance.