Deep Sparse Representation Classification for Aeroengine Inter-shaft Bearing Fault Diagnosis

Fault diagnosis of aero-engine inter-shaft bearing under variable operating conditions poses a significant challenge in the industry. Existing sparse classification methods with shallow architectures suffer from insufficient fault feature extraction and interference removal capabilities with limited training samples, resulting in low diagnostic accuracies. To address this issue, this study introduces an approach termed deep sparse representation classification (DSRC). DSRC seamlessly integrates multiple layers for dictionary learning and sparse coding. In the initial phase, the dictionary learning layer is employed to acquire the Fisher discriminative sparse representation information, while the sparse coding layer is utilized to eliminate interfering components and simultaneously enhance sparsity. The incorporation of a weight matrix, guided by a high-energy atom selection strategy, links the upward and downward processes of dictionary learning and sparse coding. Subsequently, the frequency-weighted energy operator kurtosis-based feature vectors are extracted from the reconstructed signals of the newly acquired dictionary and coding coefficients. Ultimately, these discriminative feature vectors are directly input into a straightforward classifier for intelligent fault diagnosis. DSRC is applied to an aero-engine inter-shaft bearing fault data under multiple speeds. Results demonstrate that it can effectively realize discriminative fault feature extraction and high-precision automatic fault identification.