Predictive maintenance method and system for high-speed bearing of wind driven generator

Abstract

The invention provides a predictive maintenance method and system for a high-speed bearing of a wind driven generator, and belongs to the technical field of industrial automation. The predictive maintenance method for the high-speed bearing of the wind driven generator comprises the following steps: collecting vibration data of the high-speed bearing of the wind driven generator, processing the vibration data, fitting an index degradation model according to the processed vibration data, carrying out incremental updating on the index degradation model, and predicting the residual life of the wind power generation high-speed bearing. According to the predictive maintenance method, prediction of the residual life of the high-speed bearing of the wind driven generator without historical numbers in the initial stage of production is achieved, and then predictive maintenance is guided. Moreover, the method provided by the invention can update model parameters on line by utilizing newly acquired data along with the operation of equipment, can predict the residual life of the wind power generation high-speed bearing in real time, and can calculate the confidence coefficient of a predictionresult at the same time.

Description

technical field [0001] The invention belongs to the technical field of industrial automation, and in particular relates to a predictive maintenance method and system for high-speed bearings of wind power generators. Background technique [0002] Wind turbines are usually located in the wild and offshore, and the installation of wind turbines is also widely distributed, and there is a certain distance between each generator, which is not as concentrated as traditional power stations. In addition, the condition of the roads around the wind turbines is very poor, which inevitably leads to the problems of long maintenance and low efficiency during the operation of the wind turbines. High-speed bearings are important components of wind turbines and also vulnerable parts in wind turbines. By predicting the remaining useful life (RUL) of high-speed bearings, condition-based maintenance of wind turbines can be realized—predictive maintenance, which can predict the time of failure i...

AI Technical Summary

Problems solved by technology

Based on the method of signal analysis, the prediction model is highly interpretable, but it has problems such as low prediction accuracy, high threshold of use, and difficulty in popularization.

The method based on historical data is highly dependent on the historical degradation data of the model. In the early stage of wind turbine production, prediction cannot be realized, and the offline fitting of the model requires high computing power, poor interpretability, and difficult to...

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