Overtemperature fault diagnosis of front bearing for main spindle based on CNN + LSTM

Abstract

The main spindle is an important transmission component of the wind turbine. The overtemperature fault of the front bearing of the main spindle is caused due to mechanical wear, grease failure and other reasons. A neural network based on convolutional neural networks (CNN) and long short memory network is built (LSTM) to judge the early fault. Method used in this paper can find the fault in advance. Compared with BP neural network, support vector machine, the accuracy of the model used in this paper is higher, which is up to 99.77%. The mechanism model of spindle operation will be established to analyse the manifestations of various faults and improve the accuracy in the future.