基于定性趋势分析的道岔故障诊断方法研究
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摘要
道岔连接不同轨道并通常安装在两股或者多股轨道之间,不仅负责铁路线路的转换还保障线路的运营安全。随着我国轨道交通的快速发展,铁路线路和行车密度不断增长,道岔设备故障频率也日趋频繁,因此研究道岔故障诊断方法、提高诊断自动化水平具有重要现实意义。从定性的角度提出基于定性趋势分析的道岔故障诊断方法,该方法先采用区间半分法对道岔不同状态下的典型运转信号进行趋势提取,建立故障诊断知识库,之后对待诊断信号进行趋势提取,并计算其趋势序列与所有故障趋势规则的匹配度,综合比较匹配度值从而实现道岔故障诊断。实验结果表明,该方法具有良好的准确度。
The turnout is connected to different tracks and usually installed between two or more tracks. It is not only responsible for switching track line but also ensures operation safety. With rapid development of rail transit and continuous growing of railway lines and traffic density,the failure of turnout equipment is getting more frequent. Therefore,it is of great practical significance to study the fault diagnosis methods to improve the automatic diagnosis level. The turnout fault diagnosis method based on qualitative trend analysis is proposed. In this method,the trend of the reference operation signal in different state of the switch is extracted by using the interval-halving method,and the knowledge base of fault diagnosis is established. Then,the trend of signal to be diagnosed is extracted and the matching values of trend sequence and all fault trend rules are calculated. The matching values are finally compared comprehensively to fulfill switch fault diagnosis. The test results show that this method is of high accuracy.
The turnout is connected to different tracks and usually installed between two or more tracks. It is not only responsible for switching track line but also ensures operation safety. With rapid development of rail transit and continuous growing of railway lines and traffic density,the failure of turnout equipment is getting more frequent. Therefore,it is of great practical significance to study the fault diagnosis methods to improve the automatic diagnosis level. The turnout fault diagnosis method based on qualitative trend analysis is proposed. In this method,the trend of the reference operation signal in different state of the switch is extracted by using the interval-halving method,and the knowledge base of fault diagnosis is established. Then,the trend of signal to be diagnosed is extracted and the matching values of trend sequence and all fault trend rules are calculated. The matching values are finally compared comprehensively to fulfill switch fault diagnosis. The test results show that this method is of high accuracy.
引文
[1]张秀臣.北京南机公司高速重载铁路道岔项目评价研究[D].哈尔滨:哈尔滨工业大学,2010.
[2]Witczak M,Korbicz J,Mrugalski M,et al.A GMDH neural network-based approach to robust fault diagnosis:Application to the DAMADICS benchmark problem[J].Control Engineering Practice,2006,14(6):671-683.
[3]苏旷宇,陈洪云,欧阳宏志.提速道岔故障的人工智能诊断实现[J].微计算机信息,2007,23(22):188-189.
[4]Kourd Y,Lefebvre D,Guersi N.Fault diagnosis based on neural networks and decision trees:Application to DAMADICS[J].International Journal of Innovative Computing Information&Control Ijicic,2013,9(8):3185-3196.
[5]何攸旻.高速铁路道岔故障诊断方法研究[D].北京:北京交通大学,2014.
[6]姚利哲.ZD6道岔电流曲线监测原理与分析[J].企业技术开发,2014(5):79-79.
[7]张海洋.定性趋势分析在故障诊断中的应用研究[D].北京:北京化工大学,2007.
[8]卿宇搏,莫学芳,吴上海.故障诊断技术综述及发展趋势[J].中国储运,2012(11):124-127.
[9]林波.低信噪比下语音识别技术的研究[D].成都:电子科技大学,2006.
[10]Dash S,Maurya M R,Venkatasubramanian V,et al.A novel interval-halving framework for automated identification of process trends[J].Aiche Journal,2004,50(1):149-162.
[11]宋政辉,宁波,张新春.基于定性趋势分析的非线性系统故障诊断研究[J].计算机工程与应用,2015,51(8):250-254.
[12]Dash S,Rengaswamy R,Venkatasubramanian V.Fuzzy-logic based trend classification for fault diagnosis of chemical processes[J].Computers&Chemical Engineering,2003,27(3):347-362.
[13]薛艳青.道岔设备故障诊断专家系统实现方法研究[D].北京:北京交通大学,2012.
[14]张宇宁.基于机器视觉的铁路道岔检测研究[D].郑州:郑州大学,2014.
[15]孙旻.铁路信号设备故障诊断专家系统相关技术研究[D].青岛:青岛大学,2009.
[16]铁道部安全监察司.铁路行业事故案例选编[M].北京:中国铁道出版社,1999.
[17]李雅美,魏文军.基于模糊神经网络的道岔故障诊断系统研究[J].铁路计算机应用,2012,21(1):35-39.
[18]田健.基于模糊神经网络的高速铁路道岔故障诊断方法研究[D].北京:北京交通大学,2015.
[19]杨阳.神经网络和专家系统在道岔故障诊断中的应用研究[D].兰州:兰州交通大学,2013.
[20]李志春.道岔动作电流曲线的分析[J].铁道通信信号,2005,41(11):20-21.
[2]Witczak M,Korbicz J,Mrugalski M,et al.A GMDH neural network-based approach to robust fault diagnosis:Application to the DAMADICS benchmark problem[J].Control Engineering Practice,2006,14(6):671-683.
[3]苏旷宇,陈洪云,欧阳宏志.提速道岔故障的人工智能诊断实现[J].微计算机信息,2007,23(22):188-189.
[4]Kourd Y,Lefebvre D,Guersi N.Fault diagnosis based on neural networks and decision trees:Application to DAMADICS[J].International Journal of Innovative Computing Information&Control Ijicic,2013,9(8):3185-3196.
[5]何攸旻.高速铁路道岔故障诊断方法研究[D].北京:北京交通大学,2014.
[6]姚利哲.ZD6道岔电流曲线监测原理与分析[J].企业技术开发,2014(5):79-79.
[7]张海洋.定性趋势分析在故障诊断中的应用研究[D].北京:北京化工大学,2007.
[8]卿宇搏,莫学芳,吴上海.故障诊断技术综述及发展趋势[J].中国储运,2012(11):124-127.
[9]林波.低信噪比下语音识别技术的研究[D].成都:电子科技大学,2006.
[10]Dash S,Maurya M R,Venkatasubramanian V,et al.A novel interval-halving framework for automated identification of process trends[J].Aiche Journal,2004,50(1):149-162.
[11]宋政辉,宁波,张新春.基于定性趋势分析的非线性系统故障诊断研究[J].计算机工程与应用,2015,51(8):250-254.
[12]Dash S,Rengaswamy R,Venkatasubramanian V.Fuzzy-logic based trend classification for fault diagnosis of chemical processes[J].Computers&Chemical Engineering,2003,27(3):347-362.
[13]薛艳青.道岔设备故障诊断专家系统实现方法研究[D].北京:北京交通大学,2012.
[14]张宇宁.基于机器视觉的铁路道岔检测研究[D].郑州:郑州大学,2014.
[15]孙旻.铁路信号设备故障诊断专家系统相关技术研究[D].青岛:青岛大学,2009.
[16]铁道部安全监察司.铁路行业事故案例选编[M].北京:中国铁道出版社,1999.
[17]李雅美,魏文军.基于模糊神经网络的道岔故障诊断系统研究[J].铁路计算机应用,2012,21(1):35-39.
[18]田健.基于模糊神经网络的高速铁路道岔故障诊断方法研究[D].北京:北京交通大学,2015.
[19]杨阳.神经网络和专家系统在道岔故障诊断中的应用研究[D].兰州:兰州交通大学,2013.
[20]李志春.道岔动作电流曲线的分析[J].铁道通信信号,2005,41(11):20-21.