Deep learning-based cross-domain adaptation for gearbox fault diagnosis under variable speed conditions

Abstract

Existing intelligent gearbox fault diagnosis approaches have two shortcomings: (a) their performance is mostly confined to manual handcrafted features, and (b) they follow a general assumption that the distribution of the data in the source domain (labeled data on which the model is trained) is similar to the target domain (unlabeled data on which the model is tested), which might not be the case in real-world applications. Substantial human expertise and domain knowledge is required for manual feature extraction, and moreover, deploying the same model for a target domain whose distribution is different from the source domain would lead to poor generalization. Since deep learning methods can automatically learn high dimensional feature representations from raw measurement data, this paper proposes a novel deep learning-based domain adaptation (DA) method for gearbox fault diagnosis under significant speed variations. A deep convolutional neural network is used as the main architecture. The paper proposes to minimize the summation of cross-entropy loss (between the labeled source domain data) and maximum mean discrepancy loss (between the labeled source and unlabeled target datasets) simultaneously to adapt the source domain model to be applied in the target domain. The proposed deep learning DA approach is evaluated using experimental data from a gearbox under variable speeds and multiple health conditions. An appropriate benchmarking with both traditional machine learning methods and other DA methods demonstrate the superiority of the proposed method.