Home > Research > Publications & Outputs > Intelligent fault diagnosis of machinery using ...

Associated organisational unit

Electronic data

  • Manuscript Single-column

    Rights statement: 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

    Accepted author manuscript, 1.21 MB, PDF document

    Embargo ends: 24/07/22

    Available under license: CC BY-NC-ND

Links

Text available via DOI:

View graph of relations

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

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
  • M. Xia
  • H. Shao
  • D. Williams
  • S. Lu
  • L. Shu
  • C.W. de Silva
Close
Article number107938
<mark>Journal publication date</mark>30/11/2021
<mark>Journal</mark>Reliability Engineering and System Safety
Volume215
Number of pages9
Publication StatusE-pub ahead of print
Early online date24/07/21
<mark>Original language</mark>English

Abstract

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.

Bibliographic note

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