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

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.

Graphical abstractModified chain code histogram combined with shape numbers is 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.Figure optionsDownload full-size imageDownload as PowerPoint slideHighlights► Chain code and probability neural network are used to identify shaft orbit. ► Chain code combined with shape numbers is used to extract the feature vector. ► PNN is used to classify various kinds of shaft orbit. ► The method is effective and achieved satisfactory accuracy.