The Time Series method and Statistical Process Control strategy is applied to predict failures of wind turbine gearboxes. First, based on the real-time temperature data of gearboxes measured by temperature sensors, the temperature prediction model under normal operating conditions is established by ARIMA model. The analysis of the predicted values and the actual values of gearbox temperature is done, and proves that its residuals are normally distributed; then combined with statistical process control (SPC) methods, the big number of temperature data is used to calculate the standard deviation(σ) of residuals, and the gearbox failure threshold will be identified; Finally, the temperature data are analyzed both in normal operating condition and the failure condition to determine the operation status of the gearbox, statistical analysis and residual charts are carried out for gearbox failure prediction, verifying the feasibility and effectiveness of the proposed method.

This paper mainly discusses the application of the mass real-time data mining technology in equipment safety state evaluation in the power plant and the realization of the equipment comprehensive quantitative assessment and early warning of potential failure by mining analysis and modeling massive amounts of real-time data the power equipment. In addition to the foundational technology introduced in this paper, the technology is also verified by the application case in the power supply side remote diagnosis center of Guangdong electric institute.