一种面向时间不确定性问题的故障诊断方法研究
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摘要
故障诊断是一种利用故障信息之间的逻辑关系和故障机理联合分析而进行故障的辨识与定位的技术。建立合理的诊断模型以及消除诊断过程中的不确定性问题带来的故障扰动,一直是诊断技术中亟需解决的关键问题。其中,时间不确定性问题对故障信息的归属以及推理都产生了不利的影响,国内外学者在不同背景下从不同的角度提出了许多分析和处理方法。
针对大量故障信息在时序上混杂在一起不能明确其隶属关系的影响,本文基于模糊理论、贝叶斯概率推理和Petri网相关理论,提出了一种基于时间不确定性问题的时间贝叶斯Petri网模型TBPN (Time Bayes Petri net)及其相应的故障诊断方法。该模型定义了4种库所、4种变迁、时间因素相关的支持度函数以及贝叶斯概率推理函数等,可以将原本时间不确定性的问题转化为故障信息的定量描述,从而得出故障可能发生的概率,与其他Petri网故障诊断模型相比,在时间因素的考虑上更为明确,并将时间作为一种参数,参与故障信息对规则的支持程度的计算。该故障诊断方法在信息的归属阶段给出信息的归属策略,不仅可以使用后续没有使用过的信息,而且还对已经使用过的信息进行重新利用；在信息推理阶段,利用已经匹配好规则的信息和贝叶斯概率推理函数进行概率推理。文中给出一个变电站故障诊断应用案例,结果表明：该模型及其故障诊断方法可以对变电站故障发生过程进行建模与分析,可以定量的给出故障的发生概率,尤其,在信息不完备的情况下,能够快速给出故障的发生概率。该模型有自身的数学的形式化描述,有很好的语义描述能力,容错性和适应性很强,可以适用于较为复杂的诊断过程。
The fault diagnosis is a technology about logical relationship between the fault information and analysis of failure mechanism. Establishing the reasonable diagnosis model and eliminate the uncertainty problem in the process of diagnosis of fault disturbance are need to solve the key problems in the diagnostic techniques. Among them, the study is a difficulty for the uncertainty. Different scholars both at home and abroad from different perspectives put forward a lot of analysis and processing method.
In view of the many uncertainties existing in the fault diagnosis problem,this paper consider the time uncertainty impact of the distribution and reasoning of the temporal information, Based on the fuzzy theory, bayes probability reasoning and Petri net theory, the paper put forward a kind of time bayes Petri net model (TBPN). About the model originally time uncertainty problem, the model can be transformed to quantitative description of fault information, and concluded the probability of failure. TBPN model is divided into the classified information processing stages and information inference phase two parts on the network struct:The information belonging to stage the belonging of the fault information is used to put forward a kind of ownership strategy, and gives the fault information and the corresponding matching rules; According to have good information, the information processing stage calculates failure probability, and finds the failure probability of the failure source. The paper proposes token's formal representation, the corresponding time ownership strategy, information support function and information, support function.
The result of the fault diagnosis example of substation indicates that this model and method can construct the reasonable model and solve the problem. Espically, when the informations are incomplete, the method can complete the fuzzy reasoning smoothly. It can be used in more complicate fault diagnosis process.
针对大量故障信息在时序上混杂在一起不能明确其隶属关系的影响,本文基于模糊理论、贝叶斯概率推理和Petri网相关理论,提出了一种基于时间不确定性问题的时间贝叶斯Petri网模型TBPN (Time Bayes Petri net)及其相应的故障诊断方法。该模型定义了4种库所、4种变迁、时间因素相关的支持度函数以及贝叶斯概率推理函数等,可以将原本时间不确定性的问题转化为故障信息的定量描述,从而得出故障可能发生的概率,与其他Petri网故障诊断模型相比,在时间因素的考虑上更为明确,并将时间作为一种参数,参与故障信息对规则的支持程度的计算。该故障诊断方法在信息的归属阶段给出信息的归属策略,不仅可以使用后续没有使用过的信息,而且还对已经使用过的信息进行重新利用；在信息推理阶段,利用已经匹配好规则的信息和贝叶斯概率推理函数进行概率推理。文中给出一个变电站故障诊断应用案例,结果表明：该模型及其故障诊断方法可以对变电站故障发生过程进行建模与分析,可以定量的给出故障的发生概率,尤其,在信息不完备的情况下,能够快速给出故障的发生概率。该模型有自身的数学的形式化描述,有很好的语义描述能力,容错性和适应性很强,可以适用于较为复杂的诊断过程。
The fault diagnosis is a technology about logical relationship between the fault information and analysis of failure mechanism. Establishing the reasonable diagnosis model and eliminate the uncertainty problem in the process of diagnosis of fault disturbance are need to solve the key problems in the diagnostic techniques. Among them, the study is a difficulty for the uncertainty. Different scholars both at home and abroad from different perspectives put forward a lot of analysis and processing method.
In view of the many uncertainties existing in the fault diagnosis problem,this paper consider the time uncertainty impact of the distribution and reasoning of the temporal information, Based on the fuzzy theory, bayes probability reasoning and Petri net theory, the paper put forward a kind of time bayes Petri net model (TBPN). About the model originally time uncertainty problem, the model can be transformed to quantitative description of fault information, and concluded the probability of failure. TBPN model is divided into the classified information processing stages and information inference phase two parts on the network struct:The information belonging to stage the belonging of the fault information is used to put forward a kind of ownership strategy, and gives the fault information and the corresponding matching rules; According to have good information, the information processing stage calculates failure probability, and finds the failure probability of the failure source. The paper proposes token's formal representation, the corresponding time ownership strategy, information support function and information, support function.
The result of the fault diagnosis example of substation indicates that this model and method can construct the reasonable model and solve the problem. Espically, when the informations are incomplete, the method can complete the fuzzy reasoning smoothly. It can be used in more complicate fault diagnosis process.
引文
[1]李晗,萧德云.基于数据驱动的故障诊断方法综述[J].控制与决策,2011,26(1)：1-9.
[2]Basir O, Yuan X. Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory[J]. Information Fusion,2007,8(4): 379-386.
[3]D. D. R. IN, T. P. I. C. DEL TITOLO, and D. R. DI DOTTORE. Model-based fault diagnosis in dynamic systems using identification techniques[J].2003.
[4]Yongli Z, Limin H, Jinling L. Bayesian networks-based approach for power systems fault diagnosis [J]. Power Delivery, IEEE Transactions on,2006,21(2):634-639.
[5]廖佚.基于知识图谱的专家系统发展综述[J].现代情报,2012,32(2)：159-166.
[6]许萍.机械故障诊断过程中信息融合技术的应用分析[J].中国城市经济,2012,3：317.
[7]王娟,朱庆保,崔靖.复杂环境下基于贝叶斯决策的机器人路径规划[J].计算机工程与应用,2012,48(2)：245-248.
[8]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.
[9]王坚强,王佩.基于直觉模糊熵的直觉语言多准则决策方法[J].控制与决策,2012,27(11).
[10]周曙.基于贝叶斯网的电力系统故障诊断方法研究[D].西南交通大学,2010.
[11]杨海生,李江林,郭金叶等.变电站故障诊断中的不确定性问题研究[J].计算机工程与设计,2012,33(5)：2017-2021.
[12]高庆狮.Zadeh模糊集合理论存在问题证明及其改进——一个满足全部经典集合公式的C-模糊集合系统[J].大连理工大学学报,2005,45(5)：772-780.
[13]何敏,张志利,刘辉等.故障诊断技术方法综述[J].国外电子测量技术,2006,25(5)：4-6.
[14]范贵生,虞慧群,陈丽琼等.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.
[15]孙静,秦世引,宋永华.种基于Petri网和概率信息的电力系统故障诊断方法[J].电力系统自动化,2003,27(13).
[16]赵熙临,刘辉.基于规则获取的Petri网故障诊断模型研究[J].控制工程,2008,15(002)：196-199.
[17]任惠,赵洪山,米增强,等.基于编码PETRI网的电网故障诊断[J].电网技术,2004,28(5)：64-68.
[18]粟然,仇晓龙.基于模糊Petri网的输电网故障诊断改进方法[J].中国电力,2008,41(5)：50-54.
[19]陈书义,闻英友,赵宏.基于模糊集合的可信计算信任模型评估[J].计算机科学,2008,35(11)：39-41.
[20]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(24)：224-227.
[21]杨健维,何正友.基于时间约束Petri网的电网警报处理及故障诊断[J].电力系统保护与控制ISTIC El,2012,40(1).
[22]赵熙临.基于信息融合的电力系统故障诊断技术研究[D].华中科技大学,2009.
[23]王熙照,郗亚辉,董春茹,等.加权模糊产生式规则的泛化能力研究[J].科学技术与工程,2006,6(5)：534-539.
[24]刘玉坤,石伟,张晓萍,等.描述离散动态系统的集合标识Petri网STPNs[J].系统工：程论与实践,2004,7(7)：59-65.
[25]张莉莉,武艳.模糊理论概述[J].硅谷,2012(17)：177-178.
[26]侯祥林,陈长征,虞和济,等.神经网络权值和阈值的优化方法[J].东北大学学报(自然科学版),1999,20(4)：447-450.
[27]胡广朋,裴勇,于枫,等.事件的模糊离散时问区问的表示[J].科学技术与工程,2006,6(24).
[28]杨健维,何正友.基于时序模糊Petri网的电力系统故障诊断[J].电力系统自动化,2011,35(15)：46-51.
[29]Lo K L, Ng H S, Trecat J. Power systems fault diagnosis using Petri nets[C]. In:Generation, Transmission and Distribution, IEE Proceedings-. IET,1997,144(3):231～236.
[30]Zhenzhi Lin, Fushuan Wen, Chung CY, et al. A survey on the applications of Petri net theory in power systems[C]. In:IEEE Power Engineering Society General Meeting. Montreal, Que, 2006.
[31]Haddad S, Mairesse J, Nguyen H-T. Synthesis and Analysis of Product-Form Petri Nets[C]. In:The 32nd International Conference on Applications and Theroy of Petri Nets. Newcastle, UK,2011,288～307
[32]李雨舒,李明珍.无人值班变电站遥控常见故障的分析与处理[J].电力系统保护与控制,2009,37(18)：145-146
[33]姬书军,朱学科,李淮海.变电站智能告警专家系统研究[J].华东电力,2011,39(5)：773～775
[34]Zadeh LA. Fuzzy Sets [J]. Information and Control,1965,8:338～353
[35]袁崇义.Petri网原理[M].北京：电子工业出版,1998.
[36]吴哲辉.Petri网导论[M].北京：机械工业出版社,2006.
[37]叶阳东,杜彦华,高军伟,等.时间Petri网的时间知识推理算法及在铁路智能运输系统中的应用[J][J].铁道学报,2002,24(5)：5-10.
[38]叶阳东,王娟,贾利民.基于模糊时间Petri网的列车运行时间不确定性问题的处理[J].铁道学报,2005,27(1)：6-13.
[39]佘维,郭金叶,李江林,等.变电站故障诊断的时间不确定性问题研究[J].电力系统保护与控制,2012,8：004.
[40]Haiwen Yuan, Haibin Yuan, Xingshan Li. Fuzzy Petri Nets Reasoning for Application of Electric Control System Fault Diagnosis[C]. In:IEEE Conference on Robotics and Automation and Mechatronics. Bangkok, Thailand,2006,1～6
[41]Jiufu Liu. Knowledge Representation andReasoning for Flight Control System Based on Weighted Fuzzy Petri Nets[C]. In:5th International Conference on Computer Science and Education. Hefei, China,2010,528～534
[42]Yuanhua Liang, Bingcheng Yuan. Method for Generating Fuzzy Petri Net Fault Diagnosis Model based on Rough Set Theory[C]. In:2nd International Conference on Future Computer and Communication. Wuhan, China,2010,411～414
[43]江西赣州供电公司编.变电站信号辨识手册(220kV及以下部分)[M].北京：中国电力出版社,2010
[44]孟祥忠,隋嫒嫒,王立刚.基于BPN方法的电网故障诊断[J].电力系统保护与控制,2009.
[45]宋功益,王晓茹,周曙.基于贝叶斯网的电网多区域复杂故障诊断研究[J].电力系统保护与控制,2011.
[2]Basir O, Yuan X. Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory[J]. Information Fusion,2007,8(4): 379-386.
[3]D. D. R. IN, T. P. I. C. DEL TITOLO, and D. R. DI DOTTORE. Model-based fault diagnosis in dynamic systems using identification techniques[J].2003.
[4]Yongli Z, Limin H, Jinling L. Bayesian networks-based approach for power systems fault diagnosis [J]. Power Delivery, IEEE Transactions on,2006,21(2):634-639.
[5]廖佚.基于知识图谱的专家系统发展综述[J].现代情报,2012,32(2)：159-166.
[6]许萍.机械故障诊断过程中信息融合技术的应用分析[J].中国城市经济,2012,3：317.
[7]王娟,朱庆保,崔靖.复杂环境下基于贝叶斯决策的机器人路径规划[J].计算机工程与应用,2012,48(2)：245-248.
[8]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.
[9]王坚强,王佩.基于直觉模糊熵的直觉语言多准则决策方法[J].控制与决策,2012,27(11).
[10]周曙.基于贝叶斯网的电力系统故障诊断方法研究[D].西南交通大学,2010.
[11]杨海生,李江林,郭金叶等.变电站故障诊断中的不确定性问题研究[J].计算机工程与设计,2012,33(5)：2017-2021.
[12]高庆狮.Zadeh模糊集合理论存在问题证明及其改进——一个满足全部经典集合公式的C-模糊集合系统[J].大连理工大学学报,2005,45(5)：772-780.
[13]何敏,张志利,刘辉等.故障诊断技术方法综述[J].国外电子测量技术,2006,25(5)：4-6.
[14]范贵生,虞慧群,陈丽琼等.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.
[15]孙静,秦世引,宋永华.种基于Petri网和概率信息的电力系统故障诊断方法[J].电力系统自动化,2003,27(13).
[16]赵熙临,刘辉.基于规则获取的Petri网故障诊断模型研究[J].控制工程,2008,15(002)：196-199.
[17]任惠,赵洪山,米增强,等.基于编码PETRI网的电网故障诊断[J].电网技术,2004,28(5)：64-68.
[18]粟然,仇晓龙.基于模糊Petri网的输电网故障诊断改进方法[J].中国电力,2008,41(5)：50-54.
[19]陈书义,闻英友,赵宏.基于模糊集合的可信计算信任模型评估[J].计算机科学,2008,35(11)：39-41.
[20]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(24)：224-227.
[21]杨健维,何正友.基于时间约束Petri网的电网警报处理及故障诊断[J].电力系统保护与控制ISTIC El,2012,40(1).
[22]赵熙临.基于信息融合的电力系统故障诊断技术研究[D].华中科技大学,2009.
[23]王熙照,郗亚辉,董春茹,等.加权模糊产生式规则的泛化能力研究[J].科学技术与工程,2006,6(5)：534-539.
[24]刘玉坤,石伟,张晓萍,等.描述离散动态系统的集合标识Petri网STPNs[J].系统工：程论与实践,2004,7(7)：59-65.
[25]张莉莉,武艳.模糊理论概述[J].硅谷,2012(17)：177-178.
[26]侯祥林,陈长征,虞和济,等.神经网络权值和阈值的优化方法[J].东北大学学报(自然科学版),1999,20(4)：447-450.
[27]胡广朋,裴勇,于枫,等.事件的模糊离散时问区问的表示[J].科学技术与工程,2006,6(24).
[28]杨健维,何正友.基于时序模糊Petri网的电力系统故障诊断[J].电力系统自动化,2011,35(15)：46-51.
[29]Lo K L, Ng H S, Trecat J. Power systems fault diagnosis using Petri nets[C]. In:Generation, Transmission and Distribution, IEE Proceedings-. IET,1997,144(3):231～236.
[30]Zhenzhi Lin, Fushuan Wen, Chung CY, et al. A survey on the applications of Petri net theory in power systems[C]. In:IEEE Power Engineering Society General Meeting. Montreal, Que, 2006.
[31]Haddad S, Mairesse J, Nguyen H-T. Synthesis and Analysis of Product-Form Petri Nets[C]. In:The 32nd International Conference on Applications and Theroy of Petri Nets. Newcastle, UK,2011,288～307
[32]李雨舒,李明珍.无人值班变电站遥控常见故障的分析与处理[J].电力系统保护与控制,2009,37(18)：145-146
[33]姬书军,朱学科,李淮海.变电站智能告警专家系统研究[J].华东电力,2011,39(5)：773～775
[34]Zadeh LA. Fuzzy Sets [J]. Information and Control,1965,8:338～353
[35]袁崇义.Petri网原理[M].北京：电子工业出版,1998.
[36]吴哲辉.Petri网导论[M].北京：机械工业出版社,2006.
[37]叶阳东,杜彦华,高军伟,等.时间Petri网的时间知识推理算法及在铁路智能运输系统中的应用[J][J].铁道学报,2002,24(5)：5-10.
[38]叶阳东,王娟,贾利民.基于模糊时间Petri网的列车运行时间不确定性问题的处理[J].铁道学报,2005,27(1)：6-13.
[39]佘维,郭金叶,李江林,等.变电站故障诊断的时间不确定性问题研究[J].电力系统保护与控制,2012,8：004.
[40]Haiwen Yuan, Haibin Yuan, Xingshan Li. Fuzzy Petri Nets Reasoning for Application of Electric Control System Fault Diagnosis[C]. In:IEEE Conference on Robotics and Automation and Mechatronics. Bangkok, Thailand,2006,1～6
[41]Jiufu Liu. Knowledge Representation andReasoning for Flight Control System Based on Weighted Fuzzy Petri Nets[C]. In:5th International Conference on Computer Science and Education. Hefei, China,2010,528～534
[42]Yuanhua Liang, Bingcheng Yuan. Method for Generating Fuzzy Petri Net Fault Diagnosis Model based on Rough Set Theory[C]. In:2nd International Conference on Future Computer and Communication. Wuhan, China,2010,411～414
[43]江西赣州供电公司编.变电站信号辨识手册(220kV及以下部分)[M].北京：中国电力出版社,2010
[44]孟祥忠,隋嫒嫒,王立刚.基于BPN方法的电网故障诊断[J].电力系统保护与控制,2009.
[45]宋功益,王晓茹,周曙.基于贝叶斯网的电网多区域复杂故障诊断研究[J].电力系统保护与控制,2011.