Identification of shaft orbit for hydraulic generator unit using chain code and probability neural network |
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Authors: | Changqing Wang Jianzhong Zhou Pangao KouZhimeng Luo Yongchuan Zhang |
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Affiliation: | a College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China b The UAV T&R Section, Artillery Academy of PLA, Hefei 230031, PR China |
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Abstract: | Shaft orbit identification plays an important role in the hydraulic generator unit fault diagnosis. In this paper, a novel shaft orbit identification method based on chain code and probability neural network (PNN) is proposed. For this approach, firstly, a modified chain code histogram and shape numbers are used to represent the feature of the shaft orbit contour. It has properties of less data, easy to calculate, and invariance to rotation, scaling and translation. Then, the feature vectors are input to PNN to identify various kinds of shaft orbit for hydraulic generator unit. In comparison with previous methods, the experimental results show the proposed method is effective and training the network is faster, and identifying the shaft orbit achieves satisfactory accuracy. |
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Keywords: | Chain code Probability neural network Shaft orbit Fault diagnosis |
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