Bear fault diagnosis based on multiscale feature learning

For vibration signals measured at different speeds or loads, the different states of bearings will have a considerable internal variability, which further increases the difficulty of extracting fault signal consistency features. We believe that in order to improve the performance of feature learning and classification, a more comprehensive and extensive extraction and fusion of signals is needed. However, existing multiscale multi-stream architectures rely on contacting features at the deepest layers, which stack multiscale features by brute force but do not allow for a complete fusion. This paper proposes a novel multiscale shared learning network (MSSLN) architecture to extract and classify the fault feature inherent in multiscale factors of the vibration signal. The merits of the proposed MSSLN include the following: 1) multistream architecture is used to learn and fuse multiscale features from raw signals in parallel. 2) the shared learning architecture can fully exploit the shared representation with the consistency across multiscale factors. These two positive characteristics help MSSLN make a more faithful diagnosis in contrast to existing single-scale and multiscale methods. Extensive experimental results on Case Western Reserve dataset demonstrate that the proposed method has high accuracy and excellent generalization.