MIFDELN: A multi-sensor information fusion deep ensemble learning network for diagnosing bearing faults in noisy scenarios

Abstract

Owing to the harsh operating environment of rolling bearings, acquired vibration signals contain strong noise interference, which makes it challenging for conventional methods to effectively extract fault information directly from the original signal. Additionally, obtaining satisfactory diagnostic results using traditional methods based on a single sensor is often difficult because the complementary information between different sensors is ignored. To address these challenges, this paper proposes a multi-sensor information fusion deep ensemble learning network (MIFDELN) for diagnosing bearing faults. First, the collected multi-sensor signals are organically fused using the weighted fusion strategy based on the composite index, which not only avoids the selection of poor-quality sensor signals but also suppresses part of the strong noise interference in the original signal. Second, the cross-scale attention feature extraction module (CAFEM) is presented to automatically learn discriminative features from the fused signals and reduce the impact of useless feature information through cross-scale learning and attentional weight enhancement. Subsequently, a weighted topology learning module (WTLM) is introduced to further excavate the spatial structure features and strengthen the distinctness of the learned features. Finally, the softmax classifier is employed to finalize the fault identification. Two experiments were conducted to verify the effectiveness and superiority of the proposed approach. The results indicated that the proposed approach performs best in extracting discriminating features with the highest accuracy and greatest robustness compared with several representative multi-sensor fusion technologies.