A reinforcement learning based fault diagnosis for autoregressive-moving-average model

Dapeng Zhang, Yichuan Fu, Zhiling Lin, Zhiwei Gao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, a reinforcement learning approach is proposed to detect unexpected faults, where the noise-to-signal ratio of the data series is minimized for achieving robustness. The model parameter is taken as a special action of the reinforcement learning, and the policy valuation and policy improvement are utilized to find the parameters, which can make the estimated model consistent to the real-time system process. Compared with the model forecasted parameters and real-time process parameters, fault diagnosis is implemented. The fault degrees are also discussed by analyzing the distance differences between the healthy parameters and faulty parameters. The effectiveness of algorithm is demonstrated by a numerical simulation example.

Original languageEnglish
Title of host publicationProceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7067-7072
Number of pages6
ISBN (Electronic)9781538611272
DOIs
Publication statusPublished - 15 Dec 2017
Event43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017 - Beijing, China
Duration: 29 Oct 20171 Nov 2017

Publication series

NameProceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
Volume2017-January

Conference

Conference43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017
Country/TerritoryChina
CityBeijing
Period29/10/171/11/17

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