Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning

Xia, M. and Shao, H. and Williams, D. and Lu, S. and Shu, L. and de Silva, C.W. (2021) Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning. Reliability Engineering and System Safety, 215: 107938. ISSN 0951-8320

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Digital twin (DT) is emerging as a key technology for smart manufacturing. The high fidelity DT model of the physical assets can produce system performance data that is close to reality, which provides remarkable opportunities for machine fault diagnosis when the measured fault condition data are insufficient. This paper presents an intelligent fault diagnosis framework for machinery based on DT and deep transfer learning. First, the DT model of the machine is built by establishing the simulation model and with further updating through continuously measured data from the physical asset. Second, all important machine conditions can be simulated from the built DT. Third, a new-type deep structure based on novel sparse de-noising auto-encoder (NSDAE) is developed and pre-trained with condition data from the source domain, as generated from the DT. Then, to achieve accurate machine fault diagnosis with possible variations in working conditions and system characteristics, the pre-trained NSDAE is fine-tuned using parameter transfer with only one sample from the target domain. The presented method is validated through a case study of triplex pump fault diagnosis. The experimental results demonstrate that the proposed method achieves intelligent fault diagnosis with a limited amount of measured data and outperforms other state-of-the-art data-driven methods.

Item Type:
Journal Article
Journal or Publication Title:
Reliability Engineering and System Safety
Additional Information:
This is the author’s version of a work that was accepted for publication in Reliability Engineering & System Safety. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Reliability Engineering & System Safety, 215, 2021 DOI: 10.1016/j.ress.2021.107938
Uncontrolled Keywords:
?? deep transfer learningdigital twinfault diagnosisnovel sparse de-noising auto-encoderdeep learninge-learningfault detectionmachinerysignal encodingautoencodersconditionde-noisingfaults diagnosisintelligent fault diagnosismachine fault diagnosisphysical as ??
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Deposited On:
15 Sep 2021 14:31
Last Modified:
03 Jul 2024 00:07