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Residual Dual Encoder Network using Distance Metric Learning for Intelligent Fault Recognition with Unknown Classes
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Residual Dual Encoder Network using Distance Metric Learning for Intelligent Fault Recognition with Unknown Classes
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Journal Article
Residual Dual Encoder Network using Distance Metric Learning for Intelligent Fault Recognition with Unknown Classes
2025
Overview
The paper proposes a residual dual encoder network using distance metric learning for intelligent fault recognition with unknown classes. The network is made up of two encoders and one decoder. In both the encoders and the decoder, residual blocks are used as the main structure for deep feature extraction. Besides, distance metric learning with triplet loss is used to train the residual dual encoder network to obtain features which could represent different health conditions. Benefiting from the metric learning principle, the proposed model could recognize the potential faults in mechanical systems even with a few additional unknown fault classes. The superiority of the residual dual encoder network is demonstrated by comparing with several intelligent detection methods on three different experimental datasets. Results indicate that the proposed residual dual encoder network could effectively recognize the unknown faults with an average classification accuracy of 98.3%, 99.9% and 94.4% and a recognition rate of 93.8%, 94.1% and 94.8% in three cases.
Publisher
IOP Publishing
Subject
