FAULT DIAGNOSIS OF WIND POWER MACHINERY IN THE CONTEXT OF BIG DATA AND ARTIFICIAL INTELLIGENCE

Abstract

To ensure the healthy, safe, and stable operation in the design working life, a gear box safe operation remote monitoring model is designed based on modern sensing, computer detection, wireless communication, and big data analysis. It is of great significance to realize the early diagnosis and early warning of gearbox fault and to improve the reliability and safety of the whole wind turbine operation. First, the mechanical structure and operation characteristics of the gearbox are studied, and the failure of the mechanical mechanism is analysed. Then, based on this fault situation, a set of monitoring scheme and fault diagnosis, including mechanical vibration analysis and on-site video monitoring, is proposed. Finally, the hardware and software of the model as well as digital signal processing module are designed to collect and process the data of various sensors. ARM is responsible for data storage and data display. WEB server provides convenience for wind turbine operation and maintenance personnel to perform fault monitoring, analysis and diagnosis in real time in the background. SIM900A is responsible for remote data transmission. Through actual experiments, under the background of big data, the fault detection of wind turbine gearbox in the operation process is realized, and mechanical accidents caused by gear wear and aging are avoided to a large extent. The operation of the gear is guaranteed and the safety is improved, making it more stable and real-time. Moreover, the operation and maintenance costs of enterprises are reduced. In short, intelligent analysis of wind turbines through big data can ensure the safe operation of the gearbox, which has reference significance for the healthy progression of wind power industry.