Deep Discriminative Feature Learning and Feature Space Transformation for Scalable Machine Fault Diagnosis

Machine fault diagnosis for expensive high-capacity machines may be challenging as it is infeasible to induce faults and collect data. In this work, we propose a scalable fault modeling approach to address this challenge. The proposed method utilizes a low-capacity machine (source) to generate synthetic fault data representative of the high-capacity machine (target), facilitating fault modeling for the target machine. This is achieved by learning a feature space transformation from the source machine to the target machine in the healthy condition, employing constrained maximum likelihood linear regression (CMLLR). Feature spaces of the source and target machines in healthy condition are modeled using two Gaussian mixture models (GMMs) to estimate the CMLLR transformation parameters. This transformation is then applied on the source system’s fault data to synthesize the data representative of the high capacity machine’s fault data. For this feature space transformation to work effectively, the feature spaces should be linearly transformable. We employ a deep neural network with discriminative feature extraction capabilities to derive features. To validate the scalability of fault diagnosis across varying machine capacities, we conducted experiments on three distinct datasets. The scalable fault diagnosis system for synchronous generators achieved accuracy improvements of 4.65% (R-phase), 4.75% (Y-phase), and 5.11% (B-phase) compared to the baseline system. The system for geared motors showed a 23.76% accuracy improvement and the cross-machine scalable system achieved a 32.5% accuracy improvement over the baseline system. The results establish the effectiveness of the proposed approach for machine fault diagnosis across systems with varying capacities.