Abstract
Deep learning techniques have been widely used to achieve promising results for fault diagnosis. In many real-world fault diagnosis applications, labeled training data (source domain) and unlabeled test data (target domain) have different distributions due to the frequent changes of working conditions, leading to performance degradation. This study proposes an end-to-end unsupervised domain adaptation bearing fault diagnosis model that combines domain alignment and discriminative feature learning on the basis of a 1D convolutional neural network. Joint training with classification loss, center-based discriminative loss, and correlation alignment loss between the two domains can adapt learned representations in the source domain for application to the target domain. Such joint training can also guarantee domain-invariant features with good intraclass compactness and interclass separability. Meanwhile, the extracted features can efficiently improve the cross-domain testing performance. Experimental results on the Case Western Reserve University bearing datasets confirm the superiority of the proposed method over many existing methods.
Highlights
Traditional machine learning techniques, especially deep learning, have recently made great achievements in the data-driven fault diagnosis field [1,2,3,4,5,6]
Compared with recent approaches, such as OFNN-DE and A2CNN, our method achieves an average accuracy of 99.47%, which is higher than those of OFNN-DE and A2CNN with average accuracies of 98.73% and 99.21%, respectively. is result shows that the features learned by the proposed method have better domain invariance and fault discrimination than those learned by other methods
In five out of six shifts, that is, A ⟶ B, A ⟶ C, B ⟶ C, C ⟶ B, and C ⟶ A, the fault diagnosis accuracy of the proposed method achieves state-of-the-art domain adaptation performance and reaches up to 100% in the first four domain shifts
Summary
Traditional machine learning techniques, especially deep learning, have recently made great achievements in the data-driven fault diagnosis field [1,2,3,4,5,6]. Tzeng et al [12] applied a single linear kernel to one layer for minimizing the maximum mean discrepancy (MMD), whereas Long et al [13, 14] minimized MMD by applying multiple kernels to multiple layers across domains Another impressive work is Deep Coral [15], which extends CORAL [16] to deep architectures and aligns the second-order statistics of the source and target distributions. Li et al [32] presented a deep domain adaptation method for bearing fault diagnosis on the basis of the multikernel maximum mean discrepancies between domains in multiple layers to learn representations from the source domain applied to the target domain.
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