Fault detection and isolation in the DAMADICS system using recurrent neural networks

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作者

Maria Fernanda Avila-Diaz¹ , Marco Antonio Márquez-Vera¹

1. Polyechnic University of Pachuca

会议/活动

SIMCI, November 2023 (Hidalgo, Mexico)

海报摘要

An industry is required that the dispositives work free of faults. A fault is an undesired behavior of a system, being the fault detection the capability in recognizing an anomalous behavior, and the fault isolation is to know what fault is affecting the system. There are some approaches used for fault detection and isolation (FDI) like principal component analysis, artificial neural networks, fuzzy systems. In this work is shown the use of recurrent neural networks (RNN) which are simplets than deep learning and they can use past information to recognize the signals evolution early in time.

关键词

Fault diagnosis, Recurrent neural networks, Fault isolation, DAMADICS

研究领域

Electrical Engineering

参考文献

1. M.A. Márquez-Vera et al. 2021. Adaptive threshold PCA for fault detection and isolation. J. of Robotics and Control 2(3): 119-125.
2. K.A. Q and Y. Du. 2023. Simultaneous fault detection and isolation based on multi-task long short-term memory neural networks. Chemometrics and Intelligent Laboratory Syst. 240: 104881.
3. J. Choi and S.J. Lee. 2023. RNN-based integrated system for real-time sensor fault detection and fault-informed accident diagnosis in nuclear power plant accidents. Nuclear Eng. and Tech. 55(3): 814-826.
4. M.A. Márquez-Vera et al. 2021. Inverse fuzzy fault model for fault detection and isolation with least angle regression for variable selection. Comp. & Industrial Eng. 159: 107499.

基金

暂无数据

补充材料

暂无数据

附加信息

利益冲突

No competing interests were disclosed.

数据可用性声明

The datasets generated during and / or analyzed during the current study are available from the corresponding author on reasonable request.

知识共享许可协议

Copyright © 2023 Avila-Diaz et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.