Abstract
In order to solve the problems of redundancy calculation and inefficiency of traditional machine learning algorithm in dealing with large amount of historical data of fan, a new predictive algorithm based on gated recurrent unit (GRU) is proposed to predict the remaining service life of fan spindle bearing. Firstly, the vibration history data of the main shaft bearing of the fan is analyzed to find out the relationship between the characteristic value and the remaining life, and the characteristic parameters which can reflect the remaining life are selected; Then, GRU is used to build the remaining service life prediction model of spindle bearing, and the main parameters of the model are adjusted to improve the prediction accuracy of the model. Compared with long short term (LSTM) algorithm, GRU is an effective tool to deal with a large number of data sets.
Highlights
With the strong growth of global energy consumption, the contradiction between supply and demand is worsening, and the rapid economic development makes the global demand for energy unprecedented
In the drive system of wind turbine, as the key component of the drive system of wind turbine, the spindle bearing bears the weight of large components such as blades, hubs and pitch system of wind turbine and bears the task of transferring the torque generated by the rotation of blades
The red line is the actual remaining life of the bearing, the blue line is the remaining life predicted by gated recurrent unit (GRU) model, and the green line is the remaining service life predicted by long short term (LSTM) model
Summary
With the strong growth of global energy consumption, the contradiction between supply and demand is worsening, and the rapid economic development makes the global demand for energy unprecedented. Once the main shaft bearing breaks down, it is difficult to repair It will consume a lot of maintenance time and cost, which will seriously affect the efficiency of the whole wind farm. The commonly used life prediction methods mainly include two methods, one is based on failure physical model and the other is based on data-driven method The former does not need to count a lot of historical data, and the latter is based on a lot of historical data to establish the functional relationship between the observed amount and the prediction model, and to forecast the remaining life. This paper proposes a Gru based prediction method for the remaining service life of spindle bearing. The test set is used to test the built model, and compared with the LSTM network model, the effectiveness and efficiency of the experiment are verified
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