Stacked maximum independence autoencoders: A domain generalization approach for fault diagnosis under various working conditions

One of the major obstacles in rotating machinery fault diagnosis is the distribution discrepancy in feature space caused by the change of working conditions. To solve this problem, some single or multiple source domain adaptation methods have been developed. They minimize the distribution discrepancy between a source domain (or several source domains) and a target domain by maximum mean discrepancy (MMD) or its variants. However, the application scope of these methods is limited to the target domain used in adaptation training. The trained adaptation models are not generally adapted thus cannot be directly used for other unseen new domains. To address this limitation, this study proposes a generally domain adaptable approach-maximum independence stacked autoencoders (MI-SAE). First, a domain label which describes the working condition of a sample is defined. Then, maximum independence autoencoder (MI-AE) is proposed to minimize the dependence between the extracted features and the corresponding domain labels using Hilbert-Schmidt Independence Criterion (HSIC) instead of MMD. By stacking multiple MI-AEs and finetune them with labeled source samples and normal pattern samples of other supporting domains, MI-SAE is obtained. The proposed approach can learn generally domain invariant features which has maximum independence with working conditions by HSIC, thus overcome the limitations of MMD based domain adaptations. The proposed approach is applied to diagnose the faults of gearbox and engine rolling bearing. Results show it outperforms state-of-the-art single or multiple source domain adaptation models and has more practicability due to its lower requirement for labeled data.