Method for predicting service life of bearing of wind driven generator

Abstract

The invention provides a method for predicting the service life of a bearing of a wind driven generator. Themethod includes the following steps: obtaining full life cycle vibration signal data of a bearing, and generating an original data set; performing normalization processing on the original data set; building an improved multi-scale neural network model; inputting normalized data into an input layer of the model, setting dilated convolution layers with different convolution kernel scales, and obtaining abstract features of input signals layer by layer; setting a global average pooling layer, and inputting the extracted abstract features into the global average pooling layer to obtain output features of the improved multi-scale 1DCNN model; inputting the output features of the global average pooling layer into an LSTM model, and extracting bearing performance degradation information implied in the output features through a multi-layer LSTM memory unit; and predicting the residual life of the bearing according to the extracted bearing performance degradation information to obtain a prediction result. According to the method, the residual life of the wind driven generator bearing can be efficiently and accurately predicted.

Description

technical field [0001] The invention belongs to the field of bearing fault diagnosis, and in particular relates to a method for predicting the service life of a wind power generator bearing. Background technique [0002] Wind power has experienced nearly 40 years of rapid development in my country. The process of my country's wind power installed capacity from zero at the beginning to the world's number one today can be described as magnificent. However, in the process of rapid development, the design of wind turbine equipment operation is immature, and problems such as poor reliability of related equipment components are gradually exposed. Wind turbine failure accidents also occur frequently, causing huge losses to wind power related enterprises. According to statistics, about 40% of the failures of wind turbines are caused by the failure of rolling bearings. Therefore, it is very important for the remaining life evaluation of bearing equipment. During the operation of w...

AI Technical Summary

Problems solved by technology

Although the deep learning models of these applications have achieved very good results in the field of condition monitoring of bearing equipment, it is difficult for this single model to effectively extract relevant characterization information for increasingly complex equipment working environments

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