A Prediction Algorithm For the Fan Tooth Belt Fracture Fault Based on Big Data

Abstract

In order to accurately predict the fracture fault of fan tooth belt, the NARIMA method is proposed in this paper. The method is based on ARIMA model, and effectively combines the run length stationary test method, differential stationary processing method, linear minimum variance prediction algorithm, etc.. The model is used to fit the time series of the fracture fault of fan tooth belt, and the model is used to predict the fracture fault of fan tooth belt. It is found that the NARIMA model can well fit the given time series, and the predicted values are in line with the actual situation and trend. The test results show the effectiveness of the proposed algorithm.