Open Access
ARTICLE
A Reinforcement Learning System for Fault Detection and Diagnosis in Mechatronic Systems
Wanxin Zhang1,*, Jihong Zhu2
1 School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510006, China.
2 Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China.
* Corresponding Author: Wanxin Zhang. Email:
(This article belongs to the Special Issue: Machine Learning based Methods for Mechanics)
Computer Modeling in Engineering & Sciences 2020, 124(3), 1119-1130. https://doi.org/10.32604/cmes.2020.010986
Received 12 April 2020; Accepted 09 June 2020; Issue published 21 August 2020
Abstract
With the increasing demand for the automation of operations and processes in mechatronic systems, fault detection and diagnosis has become a major
topic to guarantee the process performance. There exist numerous studies on the
topic of applying artificial intelligence methods for fault detection and diagnosis.
However, much of the focus has been given on the detection of faults. In terms of
the diagnosis of faults, on one hand, assumptions are required, which restricts the
diagnosis range. On the other hand, different faults with similar symptoms cannot
be distinguished, especially when the model is not trained by plenty of data. In
this work, we proposed a reinforcement learning system for fault detection and
diagnosis. No assumption is required. Feature exaction is first made. Then with
the features as the states of the environment, the agent directly interacts with
the environment. Optimal policy, which determines the exact category, size and
location of the fault, is obtained by updating Q values. The method takes advantage of expert knowledge. When the features are unclear, action will be made to
get more information from the new state for further determination. We create
recurrent neural network with the long short-term memory architecture to approximate Q values. The application on a motor is discussed. The experimental results
validate that the proposed method demonstrates a significant improvement compared with existing state-of-the-art methods of fault detection and diagnosis.
Keywords
Cite This Article
APA Style
Zhang, W., Zhu, J. (2020). A reinforcement learning system for fault detection and diagnosis in mechatronic systems. Computer Modeling in Engineering & Sciences, 124(3), 1119-1130. https://doi.org/10.32604/cmes.2020.010986
Vancouver Style
Zhang W, Zhu J. A reinforcement learning system for fault detection and diagnosis in mechatronic systems. Comput Model Eng Sci. 2020;124(3):1119-1130 https://doi.org/10.32604/cmes.2020.010986
IEEE Style
W. Zhang and J. Zhu, "A Reinforcement Learning System for Fault Detection and Diagnosis in Mechatronic Systems," Comput. Model. Eng. Sci., vol. 124, no. 3, pp. 1119-1130. 2020. https://doi.org/10.32604/cmes.2020.010986