A novel transfer learning fault diagnosis method based on Manifold Embedded Distribution Alignment with a little labeled data

Ke Zhao, Hongkai Jiang (), Zhenghong Wu and Tengfei Lu
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Ke Zhao: Northwestern Polytechnical University
Hongkai Jiang: Northwestern Polytechnical University
Zhenghong Wu: Northwestern Polytechnical University
Tengfei Lu: Northwestern Polytechnical University

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 1, No 8, 165 pages

Abstract: Abstract Accurate identification of rolling bearing faults is quite significant for the stable operation of mechanical systems. However, for practical diagnosis issues, it is difficult to obtain abundant labeled data due to the change of operating conditions and complex working environment, which puts forward higher requirements on the ability of the diagnosis methods. To tackle the mentioned problem, a novel transfer learning method based on a little labeled data is proposed, which uses bidirectional gated recurrent unit (BiGRU) and Manifold Embedded Distribution Alignment (MEDA). Firstly, frequency spectrum datasets are utilized to remove the redundant information of raw vibration signals. Secondly, the BiGRU network is constructed to generate auxiliary samples that are utilized as source domain. Finally, MEDA, as the most powerful non-deep transfer learning method, is applied to align the distribution of these auxiliary samples generated by BiGRU and the unlabeled samples from target domain. Experiment results indicate the excellent performance of the proposed method under a little labeled data.

Keywords: Transfer learning; Bidirectional gated recurrent unit; Manifold Embedded Distribution Alignment (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10845-020-01657-z

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