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Fault Detection Method Based on the Monitoring State Synchronization for Industrial Process System

  • Hao Ren
  • Yi Chai
  • Jian-feng Qu
  • Ke Zhang
  • Qiu Tang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 528)

Abstract

Currently, with the requirements of quality, safety and service in many modern industrial process systems, fault detection and diagnosis become a significant issue to ensure the high control performances. Under these circumstances, this paper presents a fault detection method based on the monitoring state synchronization to perceive and evaluate the system operation situation. This model firstly considers the system states measured by some sensors as the nodes and defines the distributed monitoring state network with changing dynamically over time, all of which have been given their definition, calculation method and actual physical meaning, and finally its synchronization state of distributed network can be employed to achieve fault detection. Furthermore, this method can be used to provide a novel and feasible research method for the global assessment of the operation states and for monitoring the local operation fault of modern industrial process systems. The application of this method on a simple multivariate process system example shown that it can not only track the operation state of the whole system well to detect the fault, and not only monitor the situation of each distributed network node in real time to achieve fault diagnosis, but also make use of the correlation between the network node states to effectively locate the system operation fault.

Keywords

Industrial process system Monitoring State Synchronization Fault Detection 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China Under Grant 61633005, 61673076 and 61773080, and it is also funded by the Natural Science Foundation of Chongqing City, China (cstc2016jcyjA0504).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Hao Ren
    • 1
  • Yi Chai
    • 1
  • Jian-feng Qu
    • 1
  • Ke Zhang
    • 1
  • Qiu Tang
    • 1
  1. 1.Key Laboratory of Complex System Safety and Control, Ministry of Education and the School of AutomationChongqing UniversityChongqingPeople’s Republic of China

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