Identification of shaft orbit for hydraulic generator unit using chain code and probability neural network

Authors:
Changqing Wang;Jianzhong Zhou;Pangao Kou;Zhimeng Luo;Yongchuan Zhang
Affiliations:
College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China and The UAV T&R Section, Artillery Academy of PLA, Hefei 230031, PR China;College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China;College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China;College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China;College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China
Venue:
Applied Soft Computing
Year:
2012

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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.