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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
J. Montmain, C. Labreuche, Multi-criteria Improvement of complex systems. Inf. Sci. 291, 61–68 (2015)
H. Ren, Y. Chai, J.F. Qu, A novel adaptive fault detection methodology for complex system using deep belief networks and multiple models: a case study on cryogenic propellant loading system. Neurocomputing 275, 2111–2125 (2018)
L. Luo, S. Bao, J. Mao, Nonlinear process monitoring based on kernel global-local preserving projections. J. Process Control 38, 11–21 (2016)
J.S. Busby, B. Green, D. Hutchison, Analysis of affordance, time, and adaptation in the assessment of industrial control system cybersecurity risk. Risk Anal. 37(7), 1298–1314 (2017)
T. Kontogiannis, M.C. Leva, Total safety management: principles, processes and methods. Saf. Sci. 100, 128–142 (2017)
J. Ahn, D. Chang, Fuzzy-based HAZOP study for process industry. J. Hazard. Mater. 317, 303–311 (2016)
Y. Sadra, S. Ahadpour, Markov-binary visibility graph: a new method for analyzing time series. Inf. Sci. 274, 286–302 (2014)
Y. Tang, F. Qian, Synchronization in complex networks and its application-a survey of recent advances and challenges. Annu. Rev. Control 38, 184–198 (2014)
Y. Kim, S.J. Lee, T. Park, Robust leak detection and its localization using interval estimation for water distribution network. Comput. Chem. Eng. 92, 1–17 (2016)
Q. Zhang, S. Geng, Dynamic uncertain causality graph applied to dynamic fault diagnoses of large and complex systems. IEEE Trans. Reliab. 64(3), 910–927 (2015)
H. Ren, J. Qu, Y. Chai, et al., Deep learning for fault diagnosis: the state of the art and challenge. Control Decis. 32(8) 1345–1358 (2017)
H. Yu, F. Khan, V. Garaniya, Self-organizing map based fault diagnosis technique for non-gaussian processes. Ind. Eng. Chem. Res. 53(21), 8831–8843 (2014)
E. Cai, D. Liu, L. Liang, Monitoring of chemical industrial processes using integrated complex network theory with PCA. Chemom. Intell. Lab. Syst. 140, 22–35 (2015)
L. Bakule, Decentralized control: status and outlook. Annu. Rev. Control 38(1), 71–80 (2014)
F. Khan, S.J. Hashemi, N. Paltrinieri, Dynamic risk management: a contemporary approach to process safety management. Curr. Opin. Chem. Eng. 14, 9–17 (2016)
A. Primadianto, C.N. Lu, A review on distribution system state estimation. IEEE Trans. Power Syst. 32(5), 3875–3883 (2017)
Z.E. Bhatti, P.S. Roop, R. Sinha, Unified functional safety assessment of industrial automation systems. IEEE Trans. Ind. Inf. 13(1), 17–26 (2017)
G. Li, S.J. Qin, D. Zhou, A new method of dynamic latent-vatiable modeling for process monitoring. IEEE Trans. Ind. Electron. 61(11), 6438–6445 (2014)
S. Yin, X. Li, H. Gao, Data-based techniques focused on modern industry: an overview. IEEE Trans. Ind. Electron. 62(1), 657–667 (2015)
Y. Jia, Robust control with decoupling performance for steering and traction of 4WS vehicles under velocity-varying motion. IEEE Trans. Control Syst. Technol. 8(3) 554–569 (2000)
Y. Jia, Alternative proofs for improved LMI representations for the analysis and the design of continuous-time systems with polytopic type uncertainty: a predictive approach. IEEE Trans. Autom. Control 48(8), 1413–1416 (2003)
N. Alileche, V. Cozzani, Thresholds for domino effects and safety distances in the process industry: a review of approaches and regulations. Reliab. Eng. Syst. Saf. 143, 74–84 (2015)
F. Baghernezhad, K. Khorasani, Computationally intelligent strategies for robust fault detection, isolation, and identification of mobile robots. Neurocomputing 171, 335–346 (2016)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ren, H., Chai, Y., Qu, Jf., Zhang, K., Tang, Q. (2019). Fault Detection Method Based on the Monitoring State Synchronization for Industrial Process System. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2018 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 528. Springer, Singapore. https://doi.org/10.1007/978-981-13-2288-4_9
Download citation
DOI: https://doi.org/10.1007/978-981-13-2288-4_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2287-7
Online ISBN: 978-981-13-2288-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)