基于序列数据异常趋势识别的故障诊断方法
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摘要
在专家诊断经验中经常会采用序列数据的变化趋势作为诊断依据。由于重大设备诊断缺乏故障样本,多以专家经验为依据,使一般的定性趋势分析方法不易在智能诊断系统中直接应用。因此,提出了一种融合专家经验的序列数据趋势识别方法。该方法基于专家对趋势特征的描述,以模糊矢量形式描述序列数据的变化趋势。然后通过趋势识别决策树,实时判断数据趋势类型。将该方法应用于汽轮机故障案例中,验证了该方法提取的趋势特征可有效提高汽轮机故障诊断模型的准确度。
The variation trend of sequential data is often used as diagnosis basis in experts' experience.Because of lacking fault samples,fault diagnosis of key equipment usually relays on experts' experience.Common qualitative trend analysis methods are difficult to be used in intelligent diagnosis.Therefore,a trend feature identification method based on fusing experts' knowledge is proposed.Based on experts' description of trend feature,the trend of sequential is described by fuzzy vectors.Then,though decision tree of trend feature identification,the type of trend is identified timely.The method is used in the diagnosis of a real turbine fault case.The result verifies that the identified trend feature can effectively improves the accuracy of diagnosis model for steam turbine.
The variation trend of sequential data is often used as diagnosis basis in experts' experience.Because of lacking fault samples,fault diagnosis of key equipment usually relays on experts' experience.Common qualitative trend analysis methods are difficult to be used in intelligent diagnosis.Therefore,a trend feature identification method based on fusing experts' knowledge is proposed.Based on experts' description of trend feature,the trend of sequential is described by fuzzy vectors.Then,though decision tree of trend feature identification,the type of trend is identified timely.The method is used in the diagnosis of a real turbine fault case.The result verifies that the identified trend feature can effectively improves the accuracy of diagnosis model for steam turbine.
引文
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[17]ANANDAKRISHNAN S,RAJAGOPALAN S.Monitoring transitions in chemical plants using enhanced trend analysis[J].Computers&Chemical Engineering,2003,27(10):1455-1472.
[18]ANTONIO M G,JUAN Mg,LUIS J L,et al.Time series represented by means of fuzzy piecewise lineal segments[J].Journal of Computational and Applied Mathematics,2016,318:156-167.
[19]DENG W,WANG G,XU J.Piecewise two-dimensional normal cloud representation for time-series data mining[J].Information Sciences,2016,374:32-50.
[20]陈国兵,杨自春,费志方.船舶汽轮机滑油系统动态安全性分析方法研究[J].汽轮机技术,2017,59(5):361-364.CHEN Guobing,YANG Zichun,FEI Zhifang.Research on dynamic safety analysis method of steam turbine oil system[J].Turbine Technology,2017,59(5):361-364.
[21]王守鹏,赵冬梅.电网故障诊断的研究综述与前景展望[J].电力系统自动化,2017,41(19):164-175.DOI:10.7500/AEPS20170126001.WANG Shoupeng,ZHAO Dongmei.Research review and prospects for power grid fault diagnosis[J].Automation of Electric Power Systems,2017,41(19):164-175.DOI:10.7500/AEPS20170126001.
[2]MADRIGAL E G,OSORIO G L.Fault detection and isolation system for boiler-turbine unit of a thermal power plant[J].Electric Power Systems Research,2017,148:237-244.
[3]YE Jun.Single-valued neutrosophic similarity measures based on cotangent function and their application in the fault diagnosis of steam turbine[J].Soft Computing,2017,21(3):817-825.
[4]SALEHPOUR O F,POURGOL M M.Fault diagnosis improvement using dynamic fault model in optimal sensor placement:a case study of steam turbine[J].Quality and Reliability Engineering International,2017,33(3):531-541.
[5]杨挺,赵黎媛,王成山.人工智能在电力系统及综合能源系统中的应用综述[J].电力系统自动化,2019,43(1):2-14.DOI:10.7500/AEPS20180706005.YANG Ting,ZHAO Liyuan,WANG Chengshan.Review on application of artificial intelligence in power system and integrated energy system[J].Automation of Electric Power Systems,2019,43(1):2-14.DOI:10.7500/AEPS20180706005.
[6]宋光雄,何永勇,褚福磊.汽轮机振动故障诊断知识信息服务系统[J].电力系统自动化,2006,30(1):78-82.SONG Guangxiong,HE Yongyong,CHU Fulei.Turboset vibration fault diagnosis knowledge-base system based on the dual factors method[J].Automation of Electric Power Systems,2006,30(1):78-82.
[7]张栋良,黄昕宇,李帅位.基于变分模态分解和改进模糊支持向量机的汽轮机故障诊断方法[J].济南大学学报:自然科学版,2019,33(2):142-149.ZHANG Dongliang,HUANG Xinyu,LI Shuaiwei.Turbine fault diagnosis based on variable model decomposition and improved fuzzy support vector machine[J].Journal of University of Jinan(Science and Technology),2019,33(2):142-149.
[8]夏飞,郝硕涛,张浩,等.改进粒子滤波和小波包在汽轮机振动诊断中的应用[J].系统仿真学报,2016,28(11):2823-2831.XIA Fei,HAO Shuotao,ZHANG Hao,et al.Application of improved particle filter and wavelet packet in turbine vibration diagnosis[J].Journal of System Simulation,2016,28(11):2823-2831.
[9]陈长河,石志标,曹丽华.基于云粒子群优化SVM的汽轮机转子故障诊断[J].汽轮机技术,2018,60(3):201-204.CHEN Changhe,SHI Zhibiao,CAO Lihua,et al.Fault diagnosis of steam turbine rotor based on CPSO-SVM[J].Turbine Technology,2018,60(3):201-204.
[10]李文业,杨帆.基于改进EMD与PNN的汽轮机通流部分故障诊断[J].振动、测试与诊断,2018,38(6):1138-1142.LI Wenye,YANG Fan.Research on fault diagnosis of steam turbine flow passage based on improved EMD and PNN[J].Journal of Vibration,Measurement&Diagnosis,2018,38(6):1138-1142.
[11]MORADI M,CHAIBAKHSH A,RAMEZANI A.An intelligent hybrid technique for fault detection and condition monitoring of a thermal power plant[J].Applied Mathematical Modelling,2018,60:34-47.
[12]蔡文方,陆颂元,吴文健,等.ICA在汽轮机组动静碰磨故障诊断中的应用研究[J].汽轮机技术,2017,59(6):451-455.CAI Wenfang,LU Songyuan,WU Wenjian,et al.Research on ICA for rotor-to-stator rubbing diagnosis of turbo-generator units[J].Turbine Technology,2017,59(6):451-455.
[13]戈建新,陶有宏,夏杰,等.超临界630 MW机组汽轮机高中压转子低频振动故障诊断与处理[J].热力发电,2018,47(7):75-80.GE Jianxin,TAO Youhong,XIA Jie,et al.Fault diagnosis and treatment of low frequency vibration of high and medium pressure rotor in a supercritical 630 MW steam turbine[J].Thermal Power Generation,2018,47(7):75-80.
[14]何成兵,蔡顺凯.基于FMEA和FTA的汽轮机转子与轴封碰摩故障分析[J].噪声与振动控制,2018,38(增刊2):699-703.HE Chengbing,CAI Shunkai.Analysis of rubbing fault between steam turbine rotor and shaft seal based on FMEA and FTA methods[J].Noise and Vibration Control,2018,38(Supplement 2):699-703.
[15]钟锦源,张岩,文福拴,等.基于时间序列相似性匹配的输电系统故障诊断方法[J].电力系统自化,2015,39(6):60-67.ZHONG Jinyuan,ZHANG Yan,WEN Fushuan,et al.Afault diagnosing method in power transmission systems based on time series similarity matching[J].Automation of Electric Power Systems,2015,39(6):60-67.
[16]石志标,苗莹.基于FOA-SVM的汽轮机振动故障诊断[J].振动与冲击,2014,33(22):111-114.DAN Zhibiao,MIAO Ying.Vibration fault diagnosis for steam turbine by using support vector machine based on fruit fly optimization algorithm[J].Journal of Vibration and Shock,2014,33(22):111-114.
[17]ANANDAKRISHNAN S,RAJAGOPALAN S.Monitoring transitions in chemical plants using enhanced trend analysis[J].Computers&Chemical Engineering,2003,27(10):1455-1472.
[18]ANTONIO M G,JUAN Mg,LUIS J L,et al.Time series represented by means of fuzzy piecewise lineal segments[J].Journal of Computational and Applied Mathematics,2016,318:156-167.
[19]DENG W,WANG G,XU J.Piecewise two-dimensional normal cloud representation for time-series data mining[J].Information Sciences,2016,374:32-50.
[20]陈国兵,杨自春,费志方.船舶汽轮机滑油系统动态安全性分析方法研究[J].汽轮机技术,2017,59(5):361-364.CHEN Guobing,YANG Zichun,FEI Zhifang.Research on dynamic safety analysis method of steam turbine oil system[J].Turbine Technology,2017,59(5):361-364.
[21]王守鹏,赵冬梅.电网故障诊断的研究综述与前景展望[J].电力系统自动化,2017,41(19):164-175.DOI:10.7500/AEPS20170126001.WANG Shoupeng,ZHAO Dongmei.Research review and prospects for power grid fault diagnosis[J].Automation of Electric Power Systems,2017,41(19):164-175.DOI:10.7500/AEPS20170126001.
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