Remaining life predictions of fan based on time series analysis and BP neural networks

摘要

Cooling fan is widely used in various fields. At present, owing to lack of reasonable and effective monitoring methods of fan's condition, the normal operation of the main system is seriously affected. The air cooling experiment is specially designed to solve this problem, and the monitoring and data acquisition lasted for a year. Through the analysis of the fan experimental data of whole life, a new method based on a combined model of time-series and BP neural networks is proposed to predict the remaining life of fan. This method utilizes the easily acquired speed signal as a parameter to assess the state of the running fan and the ARIMA model of time series is built to forecast the overall trend of fan's remaining life. It is combined with BP neural networks model which improves the prediction accuracy to make up for the disadvantage of single time series model which prediction error is large. By comparing the forecast values of the proposed model with experimental values, the results demonstrate that the method can accurately predict the remaining life of the fan while running, which provides the guidance for the condition monitoring of fan.