TY - GEN
T1 - A reinforcement learning based fault diagnosis for autoregressive-moving-average model
AU - Zhang, Dapeng
AU - Fu, Yichuan
AU - Lin, Zhiling
AU - Gao, Zhiwei
N1 - Funding Information:
This research was supported by China Scholarship Council and the Faculty of Engineering and Environment at Northumbria University.
Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2017/12/15
Y1 - 2017/12/15
N2 - 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.
AB - 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.
KW - Fault detection and diagnose
KW - Q-algorithm
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85046654501&partnerID=8YFLogxK
U2 - 10.1109/IECON.2017.8217236
DO - 10.1109/IECON.2017.8217236
M3 - Conference contribution
AN - SCOPUS:85046654501
T3 - Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
SP - 7067
EP - 7072
BT - Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017
Y2 - 29 October 2017 through 1 November 2017
ER -