Few-label learning for fault diagnosis based on contrastive representations
ID:72 View Protection:ATTENDEE Updated Time:2022-12-23 00:41:36 Hits:395 Poster Presentation

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Abstract
It is a common scenario in industrial applications that though a large amount of monitoring data of mechanical machines are available, only a few of them are labeled due to the lack of expert knowledge and labor. This leads to the difficulty of developing powerful supervised fault diagnosis methods, which requires a relatively large fully-labeled dataset containing machine monitoring data collected under healthy and different faulty states. In terms of this issue, a novel few-label learning method for fault diagnosis is proposed in this work, which can first learn useful representations from a large amount of unlabeled data with the help of a contrastive learning technique, based on which a fault diagnosis model can be constructed with the support of only a few labeled data. To validate the effectiveness of The proposed method is applied to a benchmark bearing fault diagnosis dataset to validate its effectiveness in few-label scenarios. Results show that the proposed method obtains better accuracy than other state-of-the-art methods.
Keywords
fault diagnosis, few-label scenario, contrastive learning, residual network
Speaker
Zhe Yang
Dr.Yang Dongguan University of Technology

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Important Dates

15th August 2022 25th September 2022 - Manuscript Submission
15th October 2022 - Acceptance Notification
1st November 2022 - Camera Ready Submission
1st November 2022 10th November 2022Early Bird Registration

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