CDSPP-Theoretic Heterogeneous Domain Adaptation Method for Bearing Fault Diagnosis under Variable Working Conditions
ID:63 View Protection:PUBLIC Updated Time:2022-12-21 21:39:37 Hits:321 Poster Presentation

Start Time:Pending (Asia/Shanghai)

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Abstract
In bearing fault diagnosis field, training data in different feature space (heterogeneous data), resulted from variable working conditions of rotating machinery, inevitably leads to performance degradation of a well-trained model. Aiming at this problem, the paper presents a new heterogeneous domain adaption (HDA) strategy based on cross-domain structure preserving projection (CDSPP).  Ready for fault diagnosis, a new feature extraction strategy combines noise resistant correlation (NRC)and intrinsic time-scale decomposition (ITD) is proposed to enhance the robustness of signal features. Then, heterogeneous fault vectors from target and source domains are fed into CDSPP model to align the feature distribution by projecting two domains into a common low-dimensional space. The final experiments shows that this method can effectively correct the distributional drift among different feature types and prove that this method is expected to be new technique for boosting the performance of heterogeneous transfer in fault diagnosis task.
Keywords
bearing fault diagnosis, heterogeneous domain adaptation, noise resistant correlation, cross-domain structure preserving projection
Speaker
Yuhang Chen
Jiangsu University

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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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