Feature-oriented unified dictionary learning-based sparse classification for multi-domain fault diagnosis

Abstract

Aiming at the instability of sparse representation of multi-domain features caused by domain discrepancy and individual differences in feature sparse discriminative ability, the Feature-oriented Unified Dictionary Learning based Sparse Classification (FUDL-SC) is proposed for multi-domain fault diagnosis. Blending sources alignment is designed to unify the transformations of multiple domains at the sample global level to reduce the discrepancy in the data distribution across domains. The FUDL framework is established by learning dictionary atoms specific to each feature of different classes in the unified domain, thus generating a Feature-oriented Unified Dictionary (FUD). Reconstruction Scoring Matrix (RSM) is constructed to measure the sparse discrimination performance of individual FUD. The iterative update of the RSM is incorporated into the FUDL model, thus enhancing FUD's sparsity-preserving and discriminating ability in multiple domains. The efficacy of FUDL-SC is experimentally demonstrated on a multi-condition bearing failure dataset, a multi-device bearing failure dataset, and a small-sample gearbox failure dataset. The comparative studies verify that FUDL-SC can achieve higher recognition accuracy, better stability, and greater robustness than numerous state-of-the-art methods in multi-domain fault diagnosis.