A Novel Angle-Domain Deep Deconvolution Method for Bearing Fault Diagnosis Under Speed-Varying Condition

The extraction of fault features under strong noise conditions (SNCs) has been successfully addressed by the deep-network-based maximum correlated kurtosis deconvolution (MCKD-DeNet) method. However, under speed-varying conditions (SVCs), this MCKD-DeNet method exhibits significant performance limitations due to inherent deficiencies in its objective function. To overcome the diagnostic challenge under SNC and SVC, this article presents a novel angle-domain deep deconvolution (ADD) method. Initially, the method establishes a multilayer neural network architecture, wherein feature learning is seamlessly integrated into the deconvolution framework to enhance the deep extraction capability of fault characteristics. Subsequently, the angle-domain index average kurtosis (AK), which can effectively measure the fault characteristics under SVC, acts as the guiding criterion for optimizing the network. Furthermore, through the implementation of adaptive weight updating and feature learning strategies, fault features are progressively extracted and strengthened. Ultimately, the effectiveness of ADD is rigorously verified via simulations and experimental analysis, which consistently demonstrate its enhanced ability to accurately extract fault features under both SNC and SVC conditions, outperforming conventional techniques in terms of robustness and diagnostic accuracy.