Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis

Abstract

Domain adaptation aims at improving model performance by leveraging the learned knowledge in the source domain and transferring it to the target domain. Recently, domain adversarial methods have been particularly successful in alleviating the distribution shift between the source and the target domains. However, these methods assume an identical label space between the two domains. This assumption imposes a significant limitation for real applications since the target training set may not contain the complete set of classes. We demonstrate in this paper that the performance of domain adversarial methods can be vulnerable to an incomplete target label space during training. To overcome this issue, we propose a two-stage unilateral alignment approach. The proposed methodology makes use of the inter-class relationships of the source domain and aligns unilaterally the target to the source domain. The benefits of the proposed methodology are first evaluated on the MNIST$\to$MNIST-M adaptation task. The proposed methodology is also evaluated on a fault diagnosis task, where the problem of missing fault types in the target training dataset is common in practice. Both experiments demonstrate the effectiveness of the proposed methodology.

References in corpus (7)

• Learning Transferable Features with Deep Adaptation Networks
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• Domain Adaptation for Visual Applications: A Comprehensive Survey
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Cited by in corpus (7)

• Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study
• Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer
• Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series
• Integrating Expert Knowledge with Domain Adaptation for Unsupervised Fault Diagnosis
• SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech Emotion Recognition
• Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction
• An Intelligent Approach to Detecting Novel Fault Classes for Centrifugal Pumps Based on Deep CNNs and Unsupervised Methods