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Automatic identification of pump unit axis orbit based on invariant moments features and neural networks |
Chen Jian1, Ye Yuanjie2, Chen Shu3, Chen Guangda4, Yu Yonghai5, Wang Jianming6 |
(1.State Key Laboratory of Water Resources and Hydropower Engineering Science,Wuhan University, Wuhan, Hubei 430072, China; 2.HydroChina Zhongnan Engineering Corporation, Changsha, Hunan 410014, China; 3.Wuhan Urban Environmental Engineering Technology Co. Ltd., Wuhan, Hubei 430072, China; 4.School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei 430072, China; 5.College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, Jiangsu 210098, China; 6.Administrative Bureau of Dayudu Pumping Station, Yuncheng, Shanxi 044600, China) |
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Abstract To meet the needs of signal processing on pump unit fault diagnosis, the principle of invariant moment theory was introduced. In addition, the neural network modeling as well as the sample acquisition in detail was discussed. As the shape of axis orbit responded the pump unit operation is related to a variety of fault, the real-time detection swing signals of axis on invariant moment were processed according to the invariant features of translation, scaling and rotation of invariant moment. And then the shape of axis orbit was determined by using BP neural network on pattern recognize. The combination of numerical simulation and on-site test were used to compensate the shortage of neural network training samples. All samples of both processed on invariant moment and the corresponding actual shape of the samples are of the neural network training ones. After network training completed, the output was compared with the actual shape of axis loci to validate this method. Taken the fault detection and diagnosis of Dayudu Pump Station in Shanxi for example, 10 sets of data of the sample were selectd to be compared, and the results show that the neural network recognition of the results are accurate. The method can provide the basis for orbit shape automatic identification and realizing fault diagnosis system intellectualization of pump unit.
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Received: 14 June 2010
Published: 30 January 2011
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