Interpretable Convolutional Sparse Modal Unrolling Network for Bearing Fault Diagnosis Across Unseen Time-Varying Working Conditions

Bearings frequently operate under time-varying working conditions where speed profiles may be unseen for intelligent diagnostic models. Frontier research succeeds in extracting discriminative and invariant features for accurate diagnosis. However, their extraction and generalization mechanisms lack physical interpretations, resulting in dubious generalizability and high data dependency. To tackle the challenges in practical scenarios, this study incorporates fault mechanism, proposing a fully interpretable network for credible fault diagnosis across unseen time-varying working conditions, called convolutional sparse modal unrolling network (CSMUNet). For interpretable time-varying feature extraction, a novel sparse coding algorithm is conceived and forms CSMUNet via algorithm unrolling. The algorithm utilizes modal response as convolutional dictionary for sparse vector optimization, under a masking constraint of time-varying impulsive moments. To generalize across unseen working conditions, an interpretable domain-invariant representation is conceived based on impulsive fault mechanism. The input is divided by equiangular span instead of time length, enabling impulses of different samples to have constant numbers for accurate moment recognition in CSMUNet. The proposed model is tested under unseen time-varying speed with simulation and experiment data. Results demonstrate the superior discriminative feature extraction, with key metrics rising to 3.2 times higher than raw inputs and classical convolutional models reaching over 35% accuracy improvement. The performance of CSMUNet is interpreted with the domain-invariant representation, which enhances its diagnostic credibility.