articleIEEE Transactions on Industrial ElectronicsJul 31, 2017Closed access

Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

RZRui ZhaoDWDongzhe WangRYRuqiang YanKMKezhi MaoFSFei Shen

Nanyang Technological University · Southeast University · +1 more institution

Indexed incrossref

Abstract

In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the future working conditions and the remaining useful life (prognosis). The numerical representation for raw sensory data is the key stone for various successful MHMS. Conventional methods are the labor-extensive as they usually depend on handcrafted features, which require expert knowledge. Inspired by the success of deep…

Citation impact

870
total citations
FWCI
49.38
Percentile
100%
References
35
Citations per year

Authors

6

Topics & keywords

Topics
Keywords
  • Downtime
  • Computer science
  • Artificial intelligence
  • Machine learning
  • Feature learning
  • Feature (linguistics)
  • Condition monitoring
  • Feature extraction
UN Sustainable Development Goals
  • Decent work and economic growth
No related works found for this paper.

Funding