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SCIENCE CHINA Information Sciences, Volume 64 , Issue 10 : 202203(2021) https://doi.org/10.1007/s11432-020-3029-6

A Bayesian belief-rule-based inference multivariate alarm system for nonlinear time-varying processes

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  • ReceivedMay 4, 2020
  • AcceptedJun 29, 2020
  • PublishedSep 15, 2021

Abstract


Acknowledgment

This work was supported by NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization, China (Grant No. U1709215), National Natural Science Foundation of China (Grant No. 61673358), Zhejiang Province Key RD Projects (Grant Nos. 2019C03104, 2018C04020), Zhejiang Province Public Welfare Technology Application Research Project (Grant No. LGF20H270004), and Research Fund of National Health Commission (Grant No. WKJ-ZJ-2038).


References

[1] Wang J, Yang F, Chen T. An Overview of Industrial Alarm Systems: Main Causes for Alarm Overloading, Research Status, and Open Problems. IEEE Trans Automat Sci Eng, 2016, 13: 1045-1061 CrossRef Google Scholar

[2] Wang J, Yang Z, Chen K. Practices of detecting and removing nuisance alarms for alarm overloading in thermal power plants. Control Eng Practice, 2017, 67: 21-30 CrossRef Google Scholar

[3] Adnan N A, Izadi I, Chen T. On expected detection delays for alarm systems with deadbands and delay-timers. J Process Control, 2011, 21: 1318-1331 CrossRef Google Scholar

[4] Adnan N A, Cheng Y, Izadi I. Study of generalized delay-timers in alarm configuration. J Process Control, 2013, 23: 382-395 CrossRef Google Scholar

[5] Zang H, Yang F, Huang D. Design and Analysis of Improved Alarm Delay-Timers. IFAC-PapersOnLine, 2015, 48: 669-674 CrossRef Google Scholar

[6] Afzal M S, Chen T, Bandehkhoda A. Analysis and design of time-deadbands for univariate alarm systems. Control Eng Practice, 2018, 71: 96-107 CrossRef Google Scholar

[7] Kondaveeti S, Shah S, Izadi I. Application of multivariate statistics for efficient alarm generation. In: Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety Technology, 2009. 657--662. Google Scholar

[8] Gupta A, Giridhar A, Venkatasubramanian V. Intelligent Alarm Management Applied to Continuous Pharmaceutical Tablet Manufacturing: An Integrated Approach. Ind Eng Chem Res, 2013, 52: 12357-12368 CrossRef Google Scholar

[9] Hao Z, Hongguang L. Optimization of process alarm thresholds: A multidimensional kernel density estimation approach. Proc Saf Prog, 2014, 33: 292-298 CrossRef Google Scholar

[10] Han L, Gao H, Xu Y. Combining FAP, MAP and correlation analysis for multivariate alarm thresholds optimization in industrial process. J Loss Prevention Process Industries, 2016, 40: 471-478 CrossRef Google Scholar

[11] Brooks R, Thorpe R, Wilson J. A new method for defining and managing process alarms and for correcting process operation when an alarm occurs. J Hazard Mater, 2004, 115: 169-174 CrossRef Google Scholar

[12] Yu Y, Zhu D, Wang J. Abnormal data detection for multivariate alarm systems based on correlation directions. J Loss Prevention Process Industries, 2017, 45: 43-55 CrossRef Google Scholar

[13] Xu X, Weng X, Xu D. Evidence updating with static and dynamical performance analyses for industrial alarm system design. ISA Trans, 2020, 99: 110-122 CrossRef Google Scholar

[14] Cao Y, Sun Y, Xie G. Fault Diagnosis of Train Plug Door Based on a Hybrid Criterion for IMFs Selection and Fractional Wavelet Package Energy Entropy. IEEE Trans Veh Technol, 2019, 68: 7544-7551 CrossRef Google Scholar

[15] Cao Y, Zhang Y, Wen T. Research on dynamic nonlinear input prediction of fault diagnosis based on fractional differential operator equation in high-speed train control system. Chaos, 2019, 29: 013130 CrossRef ADS Google Scholar

[16] Alrowaie F, Gopaluni R, Kwok K. Alarm design for nonlinear stochastic systems. In: Proceedings of the 11th World Congress on Intelligent Control Automation, 2014. 437--479. Google Scholar

[17] Zhu J, Shu Y, Zhao J. A dynamic alarm management strategy for chemical process transitions. J Loss Prevention Process Industries, 2014, 30: 207-218 CrossRef Google Scholar

[18] Yu Y, Wang J, Ouyang Z. Designing Dynamic Alarm Limits and Adjusting Manipulated Variables for Multivariate Systems. IEEE Trans Ind Electron, 2020, 67: 2314-2325 CrossRef Google Scholar

[19] Cheng C, Qiao X, Teng W. Principal component analysis and belief-rule-base aided health monitoring method for running gears of high-speed train. Sci China Inf Sci, 2020, 63: 199202 CrossRef Google Scholar

[20] Xiong W, Wang J, Chen K. Multivariate Alarm Systems for Time-Varying Processes Using Bayesian Filters With Applications to Electrical Pumps. IEEE Trans Ind Inf, 2018, 14: 504-513 CrossRef Google Scholar

[21] Xu X, Xu H, Wen C. A belief rule-based evidence updating method for industrial alarm system design. Control Eng Practice, 2018, 81: 73-84 CrossRef Google Scholar

[22] Tang X, Xiao M, Liang Y. Online updating belief-rule-base using Bayesian estimation. Knowledge-Based Syst, 2019, 171: 93-105 CrossRef Google Scholar

[23] Xu X, Yan X, Sheng C. A Belief Rule-Based Expert System for Fault Diagnosis of Marine Diesel Engines. IEEE Trans Syst Man Cybern Syst, 2017, : 1-17 CrossRef Google Scholar

[24] Li G, Zhou Z, Hu C. A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base. Saf Sci, 2017, 93: 108-120 CrossRef Google Scholar

[25] Zhou Z J, Hu C H, Xu D L. A model for real-time failure prognosis based on hidden Markov model and belief rule base. Eur J Operational Res, 2010, 207: 269-283 CrossRef Google Scholar

[26] Chang L, Sun J, Jiang J. Parameter learning for the belief rule base system in the residual life probability prediction of metalized film capacitor. Knowledge-Based Syst, 2015, 73: 69-80 CrossRef Google Scholar

[27] Jian-Bo Yang , Jun Liu , Jin Wang . Belief rule-base inference methodology using the evidential reasoning Approach-RIMER. IEEE Trans Syst Man Cybern A, 2006, 36: 266-285 CrossRef Google Scholar

[28] Doucet A, Freitas N, Gordan N. Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag, 2011. Google Scholar

[29] Doucet A, Godsill S, Andrieu C. Stat Computing, 2000, 10: 197-208 CrossRef Google Scholar

[30] Yang J B. Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties. Eur J Operational Res, 2001, 131: 31-61 CrossRef Google Scholar

[31] Izadi I, Shah S L, Shook D, et al. A framework for optimal design of alarm systems. In: Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision Safety Techn, 2009. 651--656. Google Scholar

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