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

Authors
Journal
Applied Soft Computing
1568-4946
Publisher
Elsevier
Publication Date
Volume
12
Issue
1
Identifiers
DOI: 10.1016/j.asoc.2011.08.028
Keywords
  • Chain Code
  • Probability Neural Network
  • Shaft Orbit
  • Fault Diagnosis
Disciplines
  • Computer Science
  • Medicine

Abstract

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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