Condition monitoring of electrical power plant components during operational transients

被引:25
作者
Baraldi, Piero [1 ]
Di Maio, Francesco [1 ]
Pappaglione, Luca [1 ]
Zio, Enrico [1 ,2 ]
Seraoui, Redouane [3 ]
机构
[1] Politecn Milan, Dipartimento Energia, I-20133 Milan, Italy
[2] Ecole Cent Paris, European Fdn New Energy Elect France EDF, Chair Syst Sci & Energet Challenge, Paris, France
[3] EDF R&D STEP Simulat & Traitement Informat Exploi, Chatou, France
关键词
Condition monitoring; signal reconstruction; auto-associative kernel regression; Haar transform; gas turbine; start-up transients; SELECTING FEATURES; GENETIC ALGORITHM; NUCLEAR;
D O I
10.1177/1748006X12463502
中图分类号
T [工业技术];
学科分类号
120111 [工业工程];
摘要
Monitoring the condition of a component is typically based on an empirical model that estimates the values of some measurable variables (signals) in normal conditions and triggers the fault alarm when the reconstruction deviates from the measured signal. When condition monitoring is performed during plant operational transients the intrinsically dynamic behavior of the signals should be taken into account. To this purpose, two approaches are proposed in this work. The former is based on the development of several reconstruction models, each one dedicated to a different operational zone of the component. The latter is based on the preprocessing of the signals by means of Haar wavelet transforms. The performance of the two proposed approaches are compared with that of the traditional reconstruction approach used for stationary conditions, with respect to a case study concerning the condition monitoring of a gas turbine during start-up transients.
引用
收藏
页码:568 / 583
页数:16
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