CIESC Journal ›› 2018, Vol. 69 ›› Issue (8): 3517-3527.doi: 10.11949/j.issn.0438-1157.20180063

Previous Articles     Next Articles

Association rules based conditional state fuzzy Petri nets with applications in fault diagnosis

LI Peijie, YANG Bo, LI Hongguang   

  1. College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China
  • Received:2018-01-15 Revised:2018-03-26 Online:2018-08-05 Published:2018-04-09


As a knowledge representation model, fuzzy Petri nets can be potentially used in industrial process systems for fault reasoning and diagnosis. The establishment of fuzzy Petri nets model usually demands for a priori knowledge, which discourages the use in practice. To utilize industrial process data effectively, association rules based on conditional state fuzzy Petri nets are proposed, which are subsequently applied to industrial process fault reasoning and diagnosis. Fuzzy rules along with confidences of fuzzy Petri nets are extracted by association rule algorithms of data mining. Key principal components (conditional variables) affecting the confidence are extracted by correlation analysis between variables and conditional states, thus creating the conditional state fuzzy Petri nets. The reverse reasoning of dynamic confidence is performed with the iterative algorithm of the maximal algebra, obtaining the probability of fault occurrence in industrial processes. This approach realizes data driven fault diagnosis, so as enhancing the speed and accuracy of fault diagnosis. A case study on chemical reactions shows the effectiveness of the proposed method.

Key words: process systems, association rules, conditional state fuzzy Petri nets, dynamic confidence, reverse reasoning, fault diagnosis, chemical reaction

CLC Number: 

  • TP182

[1] PETRI C A. Kommunikation mit automaten[D]. Bonn:Institut für Instrumentelle Mathematik, 1962.
[2] WANG Y N, YE J F, XU G J, et al. Novel hierarchical fault diagnosis approach for smart power grid with information fusion of multi-data resources based on fuzzy petri net[C]//2014 IEEE International Conference on Fuzzy Systems. 2014:1183-1189.
[3] SHEN V R L, LAI H Y, LAI A F. The implementation of a smartphone-based fall detection system using a high-level fuzzy Petri net[J]. Applied Soft Computing, 2015, 26(C):390-400.
[4] 林晓琳. 基于模糊Petri网的专家系统实现与应用[D]. 北京:北京化工大学, 2009. LIN X L. Implementation and application of expert system based on fuzzy Petri net[D]. Beijing:Beijing University of Chemical Technology, 2009.
[5] TAO B, WEI H C, ZHEN L. Software hazard analysis for nuclear digital protection system by colored petri net[J]. Annals of Nuclear Energy, 2017, 110:486-491.
[6] 付阶辉. 基于Petri网的故障诊断方法研究[D]. 南京:东南大学, 2004. FU J H. Research on fault diagnosis method based on Petri net[D]. Nanjing:Southeast University, 2004.
[7] IFTAR A. Supervisory control of manufacturing systems modeled by timed Petri nets[J]. IFAC Papersonline, 2016, 49(31):120-124
[8] DISTEFANO S, LONGO F, SCARPA M. Marking dependency in non-Markovian stochastic Petri nets[J]. Performance Evaluation, 2017, 110:22-47.
[9] GAO M M, ZHOU M C, HUANG X G, et al. Fuzzy reasoning Petri nets[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A:Systems and Humans, 2003, 33(3):314-324.
[10] GONG M F, SONG H H, TAN J W, et al. Fault diagnosis of motor based on mutative scale back propagation net evolving fuzzy Petri nets[C]//2017 IEEE. 2017:3826-3829
[11] BASILE F. Fault diagnosis and prognosis in Petri nets by using a single generalized marking estimation[C]//Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes. 2009:1396-1401.
[12] LIU H C, LIN Q L, REN M L. Fault diagnosis and cause analysis using fuzzy evidential reasoning approach and dynamic adaptive fuzzy Petri nets[J]. Computers & Industrial Engineering, 2013, 66(4):899-908.
[13] 甘正佳, 甘正宁, 成新明. 基于概率Petri网的柴油机故障诊断方法研究[J]. 长沙铁道学院学报, 2003, 21(1):79-83. GAN Z J, GAN Z N, CHENG X M. Study on diesel engine fault diagnosis method based on probabilistic Petri net[J]. Journal of Changsha Railway Institute, 2003, 21(1):79-83.
[14] 赵熙临, 周建中, 刘辉. 基于概率Petri网的故障诊断模型研究[J]. 计算机工程与应用, 2008, 44:224-227. ZHAO X L, ZHOU J Z, LIU H. Research on fault diagnosis model based on probabilistic Petri net[J]. Computer Engineering and Application, 2008, 44:224-227.
[15] 盛晟, 肖明清, 赵亮亮, 等. 故障Petri网的概率变迁方法研究[J]. 仪器仪表学报, 2014, 35(3):715-720. SHENG S, XIAO M Q, ZHAO L L, et al. Study on the probability transitions of fault Petri nets[J]. Journal of Instrumentation, 2014, 35(3):715-720.
[16] 韩光臣, 孙树栋, 司书宾, 等. 基于模糊概率Petri网系统的故障诊断仿真研究[J]. 计算机集成制造系统, 2006, 12(4):520-524. HAN G C, SUN S D, SI S B, et al. Research on fault diagnosis based on fuzzy probability Petri net system[J]. Computer Integrated Manufacturing System, 2006, 12(4):520-524.
[17] 卓宏明, 李献丽. 基于模糊Petri网的船用齿轮箱可靠性分析系统[J]. 机械研究与应用, 2014, 29(4):204-208. ZHUO H M, LI X L. Reliability analysis system of marine gearbox based on fuzzy Petri net[J]. Mechanical Research and Application, 2014, 29(4):204-208.
[18] LI X Z, ZHAO P, LIU Y, et al. The study of improved fault Petri nets diagnosis and its application[C]//2nd Workshop on Advanced Research and Technology in Industry Applications. 2016:1920-1925.
[19] MENG F X, LEI Y J, ZHANG B, et al. Intuitionistic fuzzy Petri nets for knowledge representation and reasoning[J]. Journal of Digital Information Management, 2016, 14(2):104-112.
[20] 王南兰. 基于数据挖掘的柴油机磨损故障诊断Petri网模型[J]. 机械与电子, 2008, (1):34-37. WANG N L. Petri net model for diesel engine wear fault diagnosis based on data mining[J]. Machinery and Electronics, 2008, (1):34-37.
[21] LIANG Y H, YUAN B C. Method for generating fuzzy petri nets fault diagnosis model based on rough set theory[C]//20102nd International Conference on Future Computer and Communication. 2010:411-414.
[22] YANG B, LI H. A novel dynamic timed fuzzy Petri nets modeling method with applications to industrial processes[J]. Expert Systems with Applications, 2018, 97(1):276-289.
[23] YANG B, LI H. A similarity elastic window based approach to process dynamic time delay analysis[J]. Chemometrics & Intelligent Laboratory Systems, 2017, 170:13-24.
[24] YANG B, LI H, WEN B. A dynamic time delay analysis approach for correlated process variables[J]. Chemical Engineering Research & Design, 2017, 122:141-150.
[25] LIU H C, YOU J X, TIAN G D. Determining truth degrees of input places in fuzzy Petri net[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2017, 47(12):3425-3431.
[26] SHAH A. Association rule mining with modified APRIORI algorithm using top down approach[C]//2nd International Conference on Applied and Theoretical Computing and Communication Technology. 2016:748-752.
[27] DU P, GAO Y P. A new improvement of APRIORI algorithm for mining association rules[C]//International Conference on Computer Application and System Modeling. 2010:529-532
[28] LEE Y C, HONG T P, WANG T C. Multi-level fuzzy mining with multiple minimum supports[J]. Expert Systems with Applications, 2008, 34(1):459-468.
[29] HU H S, LI Z W, AI-AHMARI A, et al. Reversed fuzzy Petri nets and their application for fault diagnosis[J]. Computers & Industrial Engineering, 2011, 60(4):505-510
[30] 袁杰, 史海波, 刘昶, 等. 基于模糊统计的故障Petri网Token确定方法[J]. 山东大学学报(理学版), 2008, 43(3):30-33. YUAN J, SHI H B, LIU C, et al. A method for determining the Token of the fault Petri net based on fuzzy statistics[J]. Journal of Shandong University (Natural Science), 2008, 43(3):30-33.

[1] TANG Junmiao, YU Haizhen, SHI Xuhua, TONG Chudong. Dynamic monitoring of chemical processes based on latent variable auto-regressive algorithm [J]. CIESC Journal, 2019, 70(3): 987-994.
[2] WANG Yaowu, PENG Jianping, DI Yuezhong, HAO Pengcheng. Mechanism of deterioration for dry barrier material in aluminum electrolysis cells [J]. CIESC Journal, 2019, 70(3): 1035-1041.
[3] GU Bingbin, XIONG Weili. Fault diagnosis based on PCA method with multi-block information extraction [J]. CIESC Journal, 2019, 70(2): 736-749.
[4] PENG Kaixiang, ZHANG Chuanfang, MA Liang, DONG Jie, JIAO Ruihua, TANG Peng. System-levels-based holographic fault diagnosis for complex industrial processes [J]. CIESC Journal, 2019, 70(2): 590-598.
[5] TANG Jian, QIAO Junfei. Dioxin emission concentration soft measuring approach of municipal solid waste incineration based on selective ensemble kernel learning algorithm [J]. CIESC Journal, 2019, 70(2): 696-706.
[6] CHEN Haisheng, WANG Tengfei, HUANG Kejin, YUAN Yang, QIAN Xing, ZHANG Liang. Decentralized control system designs for reactive distillation columns with external recycle [J]. CIESC Journal, 2019, 70(2): 440-449.
[7] LU Chunyan, LI Wei. Fault diagnosis method of petrochemical air compressor based on deep belief network [J]. CIESC Journal, 2019, 70(2): 757-763.
[8] ZHAI Kun, DU Wenxia, LYU Feng, XIN Tao, JU Xiyuan. Fault detect method based on improved dynamic kernel principal component analysis [J]. CIESC Journal, 2019, 70(2): 716-722.
[9] MU Peng, GU Xiangbai, ZHU Qunxiong. Modeling and optimization of ethylene cracking feedstock scheduling based on P-graph [J]. CIESC Journal, 2019, 70(2): 556-563.
[10] MENG Xiangkun, CHEN Guoming, ZHENG Chunliang, WU Xiangfei, ZHU Gaogeng. Risk evaluation model of deepwater drilling blowout accident based on risk entropy and complex network [J]. CIESC Journal, 2019, 70(1): 388-397.
[11] XIONG Xiaojun, HE Ting, LIN Wensheng. Process of BOG treating in LNG receiving station with normal temperature compressor [J]. CIESC Journal, 2018, 69(S2): 425-430.
[12] DONG Shun, LI Yiguo, SUN Shuanzhu, LIU Xichui, SHEN Jiong. Fault detection method based on state space-PCANet [J]. CIESC Journal, 2018, 69(8): 3528-3536.
[13] XU Yuge, SUN Chengli, LAI Chunling, LUO Fei. Ensemble WELM method for imbalanced learning in fault diagnosis of wastewater treatment process [J]. CIESC Journal, 2018, 69(7): 3114-3124.
[14] TIAN Haifeng, YAO Lu, GAO Jialiang, ZHA Fei, GUO Xiaojun. Effects of silylation and organic weak alkali modified Mo/HZSM-5 on catalytic performance in non-oxidative aromatization of methane reaction [J]. CIESC Journal, 2018, 69(7): 3009-3017.
[15] HUANG Pan, LIU Zhen, SHAO Yunqi, DENG Shifeng, LIU Boping. Influences of organic additives on inhibiting by-products in zinc-catalyzed synthesis of alkynylsilane [J]. CIESC Journal, 2018, 69(7): 2993-3000.
Full text



No Suggested Reading articles found!