An intelligent fault diagnosis scheme for hydropower units based on the pattern recognition of axis orbits

Abstract

Fault diagnosis plays an important role in maintaining the safe and stable operation of hydropower units. This paper presents an intelligent fault diagnosis scheme for hydropower units based on the pattern recognition of axis orbits. Firstl the vibration signals in the X and Y directions that constitute the axis orbit of the unit shaft are processed by the denoising method based on modal decomposition and permutation entropy. Second, the relative position and distribution of the axis orbits for different samples in the image window are unified. Third, the trained convolution neural network is chosen as the classifier to recognize the axis orbit image for fault-type recognition. Through the analysis of the measured data of a hydropower station, the influence of the sample number of the training set and the size of the axis orbit image on the performance of the proposed method and the necessity of denoising operation are studied. Compared with the existing methods, the proposed method has higher fault recognition accuracy and better generalization performance for different training sample sets. The results indicate that the proposed method is an effective alternative for the fault diagnosis of hydropower units.