基于模糊有色Petri网的故障诊断方法
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摘要
故障诊断技术是降低事故风险、提高系统可靠性、保证系统正常运转的重要方法。然而,随着系统规模的日趋庞大,其结构的复杂性也越来越高,当系统出现故障时,伴随出现的征兆信息也蕴含了诸多不确定性因素,这些都是故障诊断中需要解决的难点问题。信号分析技术、解析模型技术和人工智能技术的快速发展将故障诊断技术逐步带入崭新的阶段。Petri网以其强大的建模和分析能力在故障诊断领域得到了广泛应用。本文在Petri网理论的基础上,提出了一种故障诊断新方法。
针对已有Petri网模型在故障诊断问题中表现出模型结构复杂、规模庞大以及故障征兆信息伴随不确定性的问题,提出一种模糊有色Petri网模型,该模型将同类库所和变迁归并,减少库所与变迁的数量,简化模型的复杂度使网的规模降低,并在托肯的颜色属性中加入置信度属性,设定变迁阈值和弧函数,以此描述模糊信息,解决部分故障征兆信息不确定性问题。基于模糊有色Petri网模型提出故障诊断方法,该方法适用于一类根据故障征兆信息进行推理的故障问题,尤其针对结构复杂的系统故障,将故障征兆信息分类后作为输入,诊断的故障状态作为输出,设定三类变迁的触发规则包括分析、诊断和整合限制模型的状态标识演变,按照故障诊断步骤依次对故障信息进行分层处理,求解最终诊断结果。本文提出的方法由于对基本Petri网进行折叠并加入模糊因素,模型结构简单,规模小,可以解决故障诊断中运用单一基本Petri网方法不能解决的模型结构复杂、规模大和故障信息不确定性的问题。
仿真实验中,分别以铁路信号设备故障与输电网故障为应用实例,通过分析各自的信号特点建立了适用于上述系统的模型,实验结果验证了该方法的有效性和正确性。由于实验所涉及的实例分属不同的领域,故障诊断过程所运用的参数也有较大差异,因此本文提出的方法是一种基于故障征兆信息进行诊断的适用性较强的方法。实验结果表明：该模型语义表述能力较强,建立的模型结构简单,规模较小且模型不易受系统结构改变的影响,因此具有较强的适应性,此外,托肯中的置信度属性和弧函数的设定还解决了部分信息缺失或存在误差等情况下模型对某些不确定性问题的处理。
The system of fault diagnosis technology is an important method to reduce accident risk, improve the reliability of system and ensure normal operation of the system. However, with the scale and complexity of the system become more and more huge and complex, and the fault symptom information also has a lot of uncertainty factors. These problems are urgent to solve by fault diagnosis technology. With the technology proliferating, fault diagnosis technology gradually gets into a new stage, which above signals analysis technology, Analytic model, and artificial intelligence technology. Petri net has been with widely applied in the field of fault diagnosis with its strong modeling ability and analytical skills. Based on Petri net theory, this paper puts forward a new method for fault diagnosis.
Aiming at the problems about complex model structure, vast scale and uncertain issue of fault symptom information when Petri Net is modeling about fault diagnosis troubles, we propose a fuzzy-colored Petri nets model. We combine the places and transitions which describe the same class, then the number of places and transitions will lessen. The complexity and scale of model also will reduce. We put credibility as one of the attributions of tokens, set threshold of transitions and functions of arcs as fuzzy information, and solve some uncertain problems about fault symptom information. Then we put forward a fault diagnosis method based on this model. This method can be used for a kind of fault diagnosis problem which reasoning according to fault symptom information, especially for some complex system fault. We put fault symptom information after classification as input, fault status as output. We also set three different trigger rules of transitions describe the evolution of the status marking which about analysis transition, diagnosis transition and concordant transition. Then we get the final result after hierarchical processing according to the steps of fault diagnosis. So this method fold and addition fuzzy factor, the structure of this model become simple, the scale become small and can solve uncertain problems at the same time when we just use Petri Net.
We use the fault of railway signal equipment and power grid as the examples. Modeling after analysis their characteristics of signal, the result show that:our method is effective and right. Because the instances in our experiments belong to different fields, and the parameters in the process of fault diagnosis are different from each other, the model of this paper is a kind of nomal model which can solve problems according to fault symptom information reasoning. The experiment results show that this model has a strong semantic describing capacity, simple process of modeling, strong adaptability and has little affluence by the change of system structure. At the same time, the credibility attribution of tokens and the functions of arcs solved the problems about some uncertain information which above information loss and error.
针对已有Petri网模型在故障诊断问题中表现出模型结构复杂、规模庞大以及故障征兆信息伴随不确定性的问题,提出一种模糊有色Petri网模型,该模型将同类库所和变迁归并,减少库所与变迁的数量,简化模型的复杂度使网的规模降低,并在托肯的颜色属性中加入置信度属性,设定变迁阈值和弧函数,以此描述模糊信息,解决部分故障征兆信息不确定性问题。基于模糊有色Petri网模型提出故障诊断方法,该方法适用于一类根据故障征兆信息进行推理的故障问题,尤其针对结构复杂的系统故障,将故障征兆信息分类后作为输入,诊断的故障状态作为输出,设定三类变迁的触发规则包括分析、诊断和整合限制模型的状态标识演变,按照故障诊断步骤依次对故障信息进行分层处理,求解最终诊断结果。本文提出的方法由于对基本Petri网进行折叠并加入模糊因素,模型结构简单,规模小,可以解决故障诊断中运用单一基本Petri网方法不能解决的模型结构复杂、规模大和故障信息不确定性的问题。
仿真实验中,分别以铁路信号设备故障与输电网故障为应用实例,通过分析各自的信号特点建立了适用于上述系统的模型,实验结果验证了该方法的有效性和正确性。由于实验所涉及的实例分属不同的领域,故障诊断过程所运用的参数也有较大差异,因此本文提出的方法是一种基于故障征兆信息进行诊断的适用性较强的方法。实验结果表明：该模型语义表述能力较强,建立的模型结构简单,规模较小且模型不易受系统结构改变的影响,因此具有较强的适应性,此外,托肯中的置信度属性和弧函数的设定还解决了部分信息缺失或存在误差等情况下模型对某些不确定性问题的处理。
The system of fault diagnosis technology is an important method to reduce accident risk, improve the reliability of system and ensure normal operation of the system. However, with the scale and complexity of the system become more and more huge and complex, and the fault symptom information also has a lot of uncertainty factors. These problems are urgent to solve by fault diagnosis technology. With the technology proliferating, fault diagnosis technology gradually gets into a new stage, which above signals analysis technology, Analytic model, and artificial intelligence technology. Petri net has been with widely applied in the field of fault diagnosis with its strong modeling ability and analytical skills. Based on Petri net theory, this paper puts forward a new method for fault diagnosis.
Aiming at the problems about complex model structure, vast scale and uncertain issue of fault symptom information when Petri Net is modeling about fault diagnosis troubles, we propose a fuzzy-colored Petri nets model. We combine the places and transitions which describe the same class, then the number of places and transitions will lessen. The complexity and scale of model also will reduce. We put credibility as one of the attributions of tokens, set threshold of transitions and functions of arcs as fuzzy information, and solve some uncertain problems about fault symptom information. Then we put forward a fault diagnosis method based on this model. This method can be used for a kind of fault diagnosis problem which reasoning according to fault symptom information, especially for some complex system fault. We put fault symptom information after classification as input, fault status as output. We also set three different trigger rules of transitions describe the evolution of the status marking which about analysis transition, diagnosis transition and concordant transition. Then we get the final result after hierarchical processing according to the steps of fault diagnosis. So this method fold and addition fuzzy factor, the structure of this model become simple, the scale become small and can solve uncertain problems at the same time when we just use Petri Net.
We use the fault of railway signal equipment and power grid as the examples. Modeling after analysis their characteristics of signal, the result show that:our method is effective and right. Because the instances in our experiments belong to different fields, and the parameters in the process of fault diagnosis are different from each other, the model of this paper is a kind of nomal model which can solve problems according to fault symptom information reasoning. The experiment results show that this model has a strong semantic describing capacity, simple process of modeling, strong adaptability and has little affluence by the change of system structure. At the same time, the credibility attribution of tokens and the functions of arcs solved the problems about some uncertain information which above information loss and error.
引文
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[23]LoKL, NgHS, Trecat J. Power System Fault Diagnosis Using Petri Nets[J]. IEEE Proceedings:Generation, Transmission and Distribution,1977,144(3):231-236.
[24]LIU Jiu-fu. Knowledge Representation and Reasoning for Flight Control System Based on Weighted Fuzzy Petri Nets(C). The 5th International Conference on Computer Science and Education, Hefei, China,2010.
[25]Michael Westergaard, Fabrizio M. Maggi. Modeling and Verification of a Protocol for Operational Support Using Coloured Petri Nets[C]. Petri Net 2011 Springer-Verlag Berlin Heidelberg,2011:268-287.
[26]Yang Jian-wei, He Zheng-you, Tan Xi-jing. A Distributed Fault Diagnosis Approach in Power System Based on Fuzzy Reasoning Petri Net[C], Asia-Pacific Power and Energy Engineering Conference, Chengdu, China, March 28-31,2010.
[27]曾庆锋,何正友,杨健维.基于有色Petri网的电力系统故障诊断模型研究[J].电力系统保护与控制,2010,38(14)：5-11.
[28]董海鹰,刘玲.基于Petri网和故障树的变电站故障诊断方法[J].测试技术学报,2007,21(2)：176-182.
[29]佘维,叶阳东.一种基于贝叶斯Petri网的故障诊断方法[J].小型微型计算机系统,2011,32(11)：2303-2308.
[30]Zhang Mingrui. Modeling and Simulation of Substation Fault Diagnosis Based on Petri Nets[C]. Proceedings of the Eighth International Conference on Electrical Machines and Systems,Nanjing,China,2005:1978-1982.
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[32]袁崇义Petri网原理[M].北京：电子工业出版,1998.
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[34]Ding Caihong. Application of Petri Net to Fault Diagnosis in Satellite[J]. Journal of Systems Engineering and Electronics,2001,12(2):92-96.
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[2]Benkouider A.,Buvat J.,Cosmao J.,et al.Fault Detection in Semi-batch Reactor Using the EKF and Statistical Method[J].Journal of Loss Prevention in the Process Industries,2009,22(2):153-161.
[3]Simoglou A.,Martin E.B.,Morris A.J..Statistical Performance Monitoring of Dynamic Multivariate Processes Using State Space Modeling[J].Computer and Chemical Engineering,2002,26:909-920.
[4]Li YaChao, Xing MengDao, Zhang Long. Detection, parameter estimation and imaging of maneuvering target in wide-band signal[J]. Science in China(Series F:Information Sciences),2009,52(6):1015-1026.
[5]Gang Tao. Ruiyun,Chang Tan. A parameter estimation based adaptive actuator failure compensation control scheme[J]. Journal of Systems Engineering and Electronics, 2011,22(1):1-11.
[6]宋国明,王厚军,刘红,姜书艳.基于提升小波变换和SVM的模拟电路故障诊断[J].电子测量与仪器学报,2010,24(1)：17-22.
[7]周鑫,吕飞鹏,吴飞,张默霓.基于小波变换的T型线路故障测距新算法[J].电力系统保护与控制,2010,38(2)：8-11.
[8]Naik A. On the Application of PCA Technique to Fault Diagnosis[J]. Tsinghua Science and Technology,2010,15(2):138-144.
[9]JUNG D Y, LEE S M,Wang Hongmei, KIM J H, LEE S H. Fault detection method with PCA and LDA and its application to induction motor[J]. Journal of Central South University of Technology,2010,17(2010),1238-1242.
[10]Russell Evan L.,Chiang Leo H.,Braatz Richard D..Fault Detection in Industrial Processes Using Canonical Variate Analysis and Dynamic Principal Component Analysis[J].Chemometrics and Intelligent Laboratory Systems,2000,51:81-93.
[11]Heung-Jae Lee, Bok-shin Ahn, Young-Moon Park. A Fault Diagnosis Expert System for Distribution Substations[J]. IEEE Transactions on Power Delivery,2000,15(1):92-97.
[12]Lai Jun-chen,Ming Bo,Li Shao-kun,Wang Ke-ru, Xie Rui-zhi. An Image-Based Diagnostic Expert System for Corn Diseases[J]. Agricultural Sciences in China,2010,9(8):1221-1229.
[13]LI Jie, SHEN Shi-tuan. Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System[J]. Chinese Journal of Aeronautics,2007,20(2007):223-229.
[14]Mohammadi R, Naderi E, Khorasani K, et al. Fault Diagnosis of Gas Turbine Engines by using Dynamic Neural Networks[C]. In:IEEE 54th International Midwest Symposium on Circuits and Systems. Seoul, Korea,2011,1-4.
[15]Hua Su, Kil To Chong. Induction Machine Condition Monitoring Using Neural Network Modeling[J]. IEEE Transactions on Industrial Electronics,2007,54(1):241-249.
[16]Franciseh, Shenlx. Fault diagnosis based on roughset theory[J]. Engineering applications of artificial intelligence,2003,16(1):39-43.
[17]Luo Yanchun, Liu Guoqing, Lv yingjun. Acquisition of Rule of Aero-Engine Fault Diagnosis Based on Rough Set Theory[C].Advances in Biomedical Engineering■2012 International Conference on Mechanical Engineering and Automation. Seoul, Korea,2012.
[18]王珏,苗夺谦,周育健.关于Rough Set理论与应用的综述[J].模式识别与人工智能,1996,9(4)：337-344.
[19]XieYun-fang,Zhou Yu-hong, Cai Jin-jin. The research on fault diagnosis of distribution network based on rough set theory[C].2009 ISECS International Colloquium on Computing, Communication, Control, and Management,Sanya,China,2009:194-197.
[20]范贵生,虞慧群,陈丽琼,刘冬梅.基于Petri网的服务组合故障诊断与处理[J].软件学报,2010,21(2)：231-247.
[21]Giua A, Seatzu C. Modeling and supervisory control of railway networks using Petri nets [J]. IEEE Transactions on Automation Science and Engineering,2008,5(3):431-445.
[22]Stephen J H, Yang Jeffery J P Tsai, Chyan Chyi Chen. Fuzzy Rule Base Systems Verification Using High-Level Petri Nets. IEEE Transactions on Knowledge and Data Engineering, 2003,15(2):457-473.
[23]LoKL, NgHS, Trecat J. Power System Fault Diagnosis Using Petri Nets[J]. IEEE Proceedings:Generation, Transmission and Distribution,1977,144(3):231-236.
[24]LIU Jiu-fu. Knowledge Representation and Reasoning for Flight Control System Based on Weighted Fuzzy Petri Nets(C). The 5th International Conference on Computer Science and Education, Hefei, China,2010.
[25]Michael Westergaard, Fabrizio M. Maggi. Modeling and Verification of a Protocol for Operational Support Using Coloured Petri Nets[C]. Petri Net 2011 Springer-Verlag Berlin Heidelberg,2011:268-287.
[26]Yang Jian-wei, He Zheng-you, Tan Xi-jing. A Distributed Fault Diagnosis Approach in Power System Based on Fuzzy Reasoning Petri Net[C], Asia-Pacific Power and Energy Engineering Conference, Chengdu, China, March 28-31,2010.
[27]曾庆锋,何正友,杨健维.基于有色Petri网的电力系统故障诊断模型研究[J].电力系统保护与控制,2010,38(14)：5-11.
[28]董海鹰,刘玲.基于Petri网和故障树的变电站故障诊断方法[J].测试技术学报,2007,21(2)：176-182.
[29]佘维,叶阳东.一种基于贝叶斯Petri网的故障诊断方法[J].小型微型计算机系统,2011,32(11)：2303-2308.
[30]Zhang Mingrui. Modeling and Simulation of Substation Fault Diagnosis Based on Petri Nets[C]. Proceedings of the Eighth International Conference on Electrical Machines and Systems,Nanjing,China,2005:1978-1982.
[31]吴哲辉.Petri网导论[M].北京：机械工业出版社,2006.
[32]袁崇义Petri网原理[M].北京：电子工业出版,1998.
[33]Wang xiaoli, Chen guangju,Xie yue,Guo zhaoxin. Fault Detection and Diagnosis Based on Time Petri Net[C].20078th International Conference on Electronic Measurement & Instruments,Xi'an,China,2007:259-263.
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