Intelligence identification for multi-class shaft centerline orbit of hydropower unit based on improved SVM model

Abstract: In the fault diagnosis system of hydropower units, the shaft centerline orbit is an important feature for the recognition of the unit operating condition, and different types of shaft centerline orbits reflect different operation state and fault information of shaft centerline orbit. In the actual operation of hydropower unit, there are few fault samples for shaft centerline orbits. Hence, the intelligent fault diagnosis cannot be performed accurately, and this problem must be solved with the combination of the corresponding spectral characteristics. Aimed at this problem, based on the improved support vector machine, a multi-fault classification algorithm was presented, the Hu invariant moment data of shaft centerline orbit graph were selected as training sample of the classification system, the error threshold level was inducted to effectively control category interference phenomenon, and a multi-fault shaft centerline orbits classifier was set up. Furthermore, it was applied to carry out the fault diagnosis of hydropower units. Results of the fault diagnosis application showed that just a few measured samples of shaft centerline orbits and a certain number of stimulated samples were needed in order to establish a fault classifier with superior performance, when the number of samples was 16 and 50, the classification accuracy was up to 96.3% and 91.2%, and the four different shapes of shaft centerline orbit graphs such as double ring-shaped, eight-shaped, ellipse-shaped and banana-shaped can be clearly distinguished. Meanwhile, the classification accuracy increased with an increase in the number of classification and the classification accuracy decreased rapidly with an increase in the number of sample, that is to say, the number of classification and the number of sample had an important influence on the classification accuracy. In addition, the optimum classification surface of invariant line moment can be obtained by adjustment of kernel function coefficient, the ability of multi-category classification can be obviously improved by introduction of distinct matrix, and it has been successfully verified in four different classifications. This fault classifier can realize the identification and diagnosis of multi-faults. And it has both the advantages of simple algorithm and strong capacity in pattern classification for multi-fault shaft centerline orbits. So the result provides a reference for the intelligent fault diagnosis of shaft centerline orbits of hydropower units with few fault samples.