A review on deep learning applications in prognostics and health management

Zhang, Liangwei and Lin, Jing and Liu, Bin and Zhang, Zhicong and Yan, Xiaohui and Wei, Muheng (2019) A review on deep learning applications in prognostics and health management. IEEE Access, 7. pp. 162415-162438. ISSN 2169-3536 (https://doi.org/10.1109/ACCESS.2019.2950985)

[thumbnail of Zhang-etal-IEEE-Access-2019-A-review-on-deep-learning-applications-in-prognostics-and-health-management]
Preview
 Text. Filename: Zhang_etal_IEEE_Access_2019_A_review_on_deep_learning_applications_in_prognostics_and_health_management.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (2MB)| Preview

Abstract

Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain.

ORCID iDs

Zhang, Liangwei, Lin, Jing, Liu, Bin ORCID logoORCID: https://orcid.org/0000-0002-3946-8124, Zhang, Zhicong, Yan, Xiaohui and Wei, Muheng;
CORE (COnnecting REpositories)