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

Zhang, Dapeng, Lin, Zhiling and Gao, Zhiwei (2018) A Novel Fault Detection with Minimizing the Noise-Signal Ratio Using Reinforcement Learning. Sensors, 18 (9). p. 3087. ISSN 1424-8220

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Official URL: http://dx.doi.org/10.3390/s18093087

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.

Item Type: Article
Uncontrolled Keywords: fault detection; reinforcement learning; noise-signal ratio
Subjects: H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Paul Burns
Date Deposited: 17 Oct 2018 16:21
Last Modified: 11 Oct 2019 17:53
URI: http://nrl.northumbria.ac.uk/id/eprint/36357

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