Prediction of bearing damage in wind turbines based on the quadratic root mean square of sub-band manifold

Abstract

When a high-power wind turbine runs in normal status, and if bearing current exists for a long time, multiple point corrosion would occur and gradually increase, eventually forming a ripple groove on the inner ring race, outer ring race and rolling body. It would lead to more vibration and shock, thereby causing fault in the equipment; the best way to prevent the this kind of fault is to find the effective fault characteristics and predict the damage’s degree on the bearing. In this paper, an adaptive neural network prediction method based on the quadratic root mean square of sub-band manifold is proposed. The damage characteristics can be analyzed by following steps: firstly, the vibration signal is decomposed into multidimensional time frequency space by wavelet packet method. Secondly, the sub-band of the manifold is constructed. The third step is to extract the root mean square value. Finally, the damage characteristics of the bearing current of the two square root sub-band manifold are obtained. Based on the back propagation network, the adaptive prediction model is built, and the training speed could be adjusted automatically according to the prediction error and precision. According to the bearing’s fault mechanism with current damage on the high-power wind turbine, one fault experiment platform has been built to simulate the current damage process of the bearing and verify the prediction method based on the quadratic root mean square of sub-band manifold. The experimental results show that the method can effectively predict the degree of bearing current damage, and the relative error of prediction is less than 5%.