Apply Low-Level Image Feature Representation and Classification Method to Identifying Shaft Orbit of Hydropower Unit

Abstract

Shaft orbit plays an important role in condition monitoring and fault diagnosis for hydropower unit. A novel method of shaft orbit identification based on low-level image feature representation and classification is proposed. The main characteristic is that the vibrations of the shaft in terms of displacements are used to draw points in an image panel at a fixed scale, resulting in the shaft orbit image set. Histogram of Oriented Gradients (HOG) is then used as the low-level local shape descriptor. Accordingly, a given shaft orbit image can be represented by a plenty of HOG local descriptors which are further aggregated into a feature vector. The feature vectors associated with class labels are fed to linear classifiers for multi-class classification. To deal with noisy samples robustly and solve the problem that training samples always cannot be separated in original space, kernel-based soft-margin Support Vector Machine (SVM) is employed. The proposed algorithm is implemented and tested on the challenging data set which is collected from a testing apparatus under different fault settings. It yields a satisfactory recognition rate which is 98.35% on the overall data set.