Achieving Predictive and Proactive Maintenance for High-Speed Railway Power Equipment with LSTM-RNN

Qi Wang, Siqi Bu, Zhengyou He

Research output: Journal article publicationJournal articleAcademic researchpeer-review

70 Citations (Scopus)

Abstract

Current maintenance mode for high-speed railway (HSR) power equipment is so outdated that can hardly adapt to the high-standard modern HSR. Therefore, a new possibility is proposed in this article to update the obsoleting maintenance mode of the HSR power equipment by adopting both predictive maintenance and proactive maintenance. With the combination of data-driven (predictive) and model-based (proactive) approaches, two principal constituents-the sample generator and the maintenance predictor-are designed. The maintenance predictor which is powered by the long short-term memory recurrent neural network is developed to realize the goal of predictive maintenance. The sample generator which is formulated by the physical degradation and failure model of HSR power equipment is proposed toward the goal of proactive maintenance. Test results on a gas-insulated switchgear have shown the powerful collaboration between the generator and the predictor, to not only accurately predict future maintenance timing of the switchgear based on historical sample data, but also enrich the data supply proactively to deal with potential data deficiency problems.

Original languageEnglish
Article number8957109
Pages (from-to)6509-6517
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Volume16
Issue number10
DOIs
Publication statusPublished - Oct 2020

Keywords

  • Artificial intelligence (AI)
  • deep learning
  • high-speed railway (HSR)
  • long short-term memory (LSTM) network
  • power equipment
  • predictive maintenance
  • proactive maintenance
  • recurrent neural network (RNN)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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