A Novel Fault Detection with Minimizing the Noise-Signal Ratio Using Reinforcement Learning

Dapeng Zhang, Zhiling Lin, Zhiwei Gao

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
5 Downloads (Pure)

Abstract

In this paper, a reinforcement learning approach is proposed to detect unexpected faults, where the noise-signal ratio of the data series is minimized to achieve robustness. Based on the information of fault free data series, fault detection is promptly implemented by comparing with the model forecast and real-time process. The fault severity degrees are also discussed by measuring the distance between the healthy parameters and faulty parameters. The effectiveness of the algorithm is demonstrated by an example of a DC-motor system.
Original languageEnglish
Article number3087
Number of pages20
JournalSensors (Switzerland)
Volume18
Issue number9
DOIs
Publication statusPublished - 13 Sep 2018

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