电站设备参数异动搜索分析与故障预警研究
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摘要
传统在线监控系统的报警模式大多采用设定阂值,通过监测参数实测值与设定阈值的比较实现报警功能。由于这种单一报警机制在系统状态监测过程中难以及时发现故障早期的征兆,并对其发展趋势进行跟踪而往往导致被迫停机,造成惨重的损失。因此针对这种故障后处理的阈值报警在复杂系统状态监测方面的不完善,本文提出了一套以主动预防故障为特色的系统在线状态监测与故障早期预警体系,充分利用现场实时/历史数据库,在线地展示系统参数的动态变化过程,反映出系统运行的健康状态。
本文提出了一种参数异动搜索算法,基于重要点划分时间子序列为各个子模式,根据参数的监测信号类型分别提取各个子模式的模式特征值,并将其映射到高维空间,从模式的角度搜索出明显不满足一般数据模式的异常参数序列及其异常持续时间和基本趋势：融合灰色等维加权预测模型与时序预测模型各自的优势,提出了一种灰色—时序组合动态预测方法,应用灰色模型预测序列的趋势项,应用时序模型预测序列的残差项,对系统监测参数的异常发展趋势进行预测,提高了预测的精度并为系统异常状态分析奠定了基础。
本文对复杂系统进行了合理且有效的层次结构划分；挖掘出参数对预测故障模式发生的重要程度及参数与参数之间的相关关系,为系统的异常状态分析提供了知识化信息；建立了基于系统异常监测参数预警潜在故障发生的计算模型,架起了参数异常与系统状态异常分析的桥梁。
本文阐明了汽轮发电机组状态监测参数异动搜索分析及故障预警的应用,结果表明该方法体系能够从海量的监测参数数据中挖掘出机组出现劣化的证据,根据系统异常状态分析对潜在故障做出正确的预警,及时采取有效措施将故障消灭在萌芽状态从而避免可能发生的强迫停机或者将故障损失程度降到最低,具有有效性和优越性。
总之,本文提出的预警体系为系统的安全长周期运行提供了坚实的保障,为维修的智能化提供了可靠的决策依据,为企业的“零故障”管理目标铺平了道路。
With the comparison of the measured value and the threshold of the parameters, traditional on-line monitoring systems have realized the alarm function. However there are still some deficiencies in applying this single warning mechanism, which works after the fault appears. It can't detect the early fault signs of systems timely and tracked the abnormal trends during monitoring process, usually leaving the system in downtime and resulting in heavy losses. So an improved warning mechanism is in urgent need.
Aiming at the imperfection of the existing warning mode, this thesis proposed a new system of on-line condition monitoring and early fault warning for a complex system. The system could make full use of real-time/historical database to timely indicate the dynamic changes of parameters and the health of system status.
Firstly, an algorithm of searching abnormal parameters was put forward, which divided time subsequence into each subschema based on the important points, then extracted each subschema's eigenvalue respectively according to the types of parameter monitoring signal and mapped the eigenvalue into high dimension space. The system would search out anomaly parameters sequence which was obviously different from the other data. Taking into account the advantages of Grey Model and Time Series Prediction Model, the thesis proposed a new combined prediction method of time siries, using the Gray Model to predict the tendency, and applying the Time Series Prediction Model to predict the residuals. With this method, the trends of abnormal parameters were advanced found, which laid the solid foundation for the analysis of system abnormality, and the Prediction accuracy was improved.
Secondly, the hierarchy of the complex system was devided effectively. The method of determining the dependence between faults and parameters was proposed, and the correlation between parameters was also studied. Moreover, the calculation of the possibility of potenitial fault occurrence was given. All studies above could provide the knowledge-based information for the analysis of system state according to the abnormal parameters.
Finally, the idea of on-line condition monitoring and early fault warning for a complex system was applied to turbine. Results indicate that this system is effective and superious. It can dig out the evidence of units'deterioration from immense amounts of monitoring data and predict the potential fault. According to the abnormal system status, measures can be taken timely and effectively, leaving the potential failure nipped in the bud. In other words, forced shutdown of units can be avoided and the damage losses caused by failure can be ninimized.
In short, the early fault warning mechanism can provide a solid guarantee for systems'secure and long-term operation, provide reliable decisions for intelligent maintenance, and pave the road for the company's management goal of "zero fault".
本文提出了一种参数异动搜索算法,基于重要点划分时间子序列为各个子模式,根据参数的监测信号类型分别提取各个子模式的模式特征值,并将其映射到高维空间,从模式的角度搜索出明显不满足一般数据模式的异常参数序列及其异常持续时间和基本趋势：融合灰色等维加权预测模型与时序预测模型各自的优势,提出了一种灰色—时序组合动态预测方法,应用灰色模型预测序列的趋势项,应用时序模型预测序列的残差项,对系统监测参数的异常发展趋势进行预测,提高了预测的精度并为系统异常状态分析奠定了基础。
本文对复杂系统进行了合理且有效的层次结构划分；挖掘出参数对预测故障模式发生的重要程度及参数与参数之间的相关关系,为系统的异常状态分析提供了知识化信息；建立了基于系统异常监测参数预警潜在故障发生的计算模型,架起了参数异常与系统状态异常分析的桥梁。
本文阐明了汽轮发电机组状态监测参数异动搜索分析及故障预警的应用,结果表明该方法体系能够从海量的监测参数数据中挖掘出机组出现劣化的证据,根据系统异常状态分析对潜在故障做出正确的预警,及时采取有效措施将故障消灭在萌芽状态从而避免可能发生的强迫停机或者将故障损失程度降到最低,具有有效性和优越性。
总之,本文提出的预警体系为系统的安全长周期运行提供了坚实的保障,为维修的智能化提供了可靠的决策依据,为企业的“零故障”管理目标铺平了道路。
With the comparison of the measured value and the threshold of the parameters, traditional on-line monitoring systems have realized the alarm function. However there are still some deficiencies in applying this single warning mechanism, which works after the fault appears. It can't detect the early fault signs of systems timely and tracked the abnormal trends during monitoring process, usually leaving the system in downtime and resulting in heavy losses. So an improved warning mechanism is in urgent need.
Aiming at the imperfection of the existing warning mode, this thesis proposed a new system of on-line condition monitoring and early fault warning for a complex system. The system could make full use of real-time/historical database to timely indicate the dynamic changes of parameters and the health of system status.
Firstly, an algorithm of searching abnormal parameters was put forward, which divided time subsequence into each subschema based on the important points, then extracted each subschema's eigenvalue respectively according to the types of parameter monitoring signal and mapped the eigenvalue into high dimension space. The system would search out anomaly parameters sequence which was obviously different from the other data. Taking into account the advantages of Grey Model and Time Series Prediction Model, the thesis proposed a new combined prediction method of time siries, using the Gray Model to predict the tendency, and applying the Time Series Prediction Model to predict the residuals. With this method, the trends of abnormal parameters were advanced found, which laid the solid foundation for the analysis of system abnormality, and the Prediction accuracy was improved.
Secondly, the hierarchy of the complex system was devided effectively. The method of determining the dependence between faults and parameters was proposed, and the correlation between parameters was also studied. Moreover, the calculation of the possibility of potenitial fault occurrence was given. All studies above could provide the knowledge-based information for the analysis of system state according to the abnormal parameters.
Finally, the idea of on-line condition monitoring and early fault warning for a complex system was applied to turbine. Results indicate that this system is effective and superious. It can dig out the evidence of units'deterioration from immense amounts of monitoring data and predict the potential fault. According to the abnormal system status, measures can be taken timely and effectively, leaving the potential failure nipped in the bud. In other words, forced shutdown of units can be avoided and the damage losses caused by failure can be ninimized.
In short, the early fault warning mechanism can provide a solid guarantee for systems'secure and long-term operation, provide reliable decisions for intelligent maintenance, and pave the road for the company's management goal of "zero fault".
引文
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[8]Rafieid, Mendelzon A O. Querying time series data based on similarity[J].IEEE Trans on Know ledge and Data Engineering,2000,12(5):675-693
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[10]Struzik Z R, Siebesa. Measuring time series'similarity through large singular features revealed with wavelet transformation[C]. Proc of the International Workshop on Database and Expert Systems Application. Florence:IEEE Computer Society Press,1999:162-166
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[15]Rath TM, Manmatha R. Lower-bounding of dynamic time warping distances for multivariate time series, TechnicalReportMM-40[R]. Amherst:Center for Intelligent Information Retrieval Technical Report, University of M assachusetts,2003
[16]Chiu S, Keogh E, Hart D, Pazzanim. Iterative deepening dynamic time warping for time series[C]. Proc of the 2nd SIAM International Conference on Data Mining. Baltimore:SIAM Press,2002:148-156
[17]Keogh E, Pazzanim J. Derivative dynamic time warping [C]. Proc of the 1st SIAM International Conference on Data Mining. Chicago:SIAM Press,2001: 209-211
[18]Keogh E, Chakrabartik, Pazzanim, et al. D in ensionality reduction for fast similarity search in large time series databases [J]. Journal of Know ledge and Information System s,2001,3(3):264-286
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