A locally weighted multi-domain collaborative adaptation for failure prediction in SSDs

Recently, domain adaptation methods are widely applied to intelligent failure prediction to solve the problem of lack of labeled data for newly designed equipment. Generally, domain adaptation assumes that the target to be predicted is consistent with the label space of the source domain. However, a more practical scenario may be that the distribution of the source domain is a subset of the target's since the new types of equipment may have different failure characteristics. Learning from a single source domain is insufficient to support the prediction task on the target domain. Multiple-source domain adaptation becomes a desperately needed solution. To this end, this paper proposes a locally weighted multi-domain collaborative adaptation method (LWMDCA). A locally weighted technique is introduced to construct the multi-source collaborative joint domain based on the similarity weighted regular coefficient to provide complete diagnostic knowledge. Moreover, to further utilize the knowledge, a new feature extractor based on multi-instance learning with attention mechanism is designed to focus on the important failure characteristics. Finally, comparative analysis with many state-of-the-art failure prediction methods using Alibaba's SSDs datasets is presented to demonstrate the effectiveness of the proposed method. © 2023 Elsevier B.V.