Abstract:The rapid development of new generation information technology, such as big data and cloud computing, promotes the progress of wind power equipment condition monitoring technology. Through the construction practice of remote monitoring and fault diagnosis platform for wind power equipment, this paper discusses how to realize the fault pre-diagnosis of wind turbine based on large data from the aspects of system function, platform construction and fault analysis, so as to improve the monitoring and management level of wind turbine. Through real-time on-line monitoring of unit operation status, using intelligent data alarm strategy in the system to accurately screen unit abnormalities, and by means of comprehensive analysis and evaluation of artificial intelligence analysis and diagnosis system and remote experts, rolling prediction of future development trend of fault can help users optimize fan maintenance and repair work and reduce downtime loss. Loss, reduce maintenance costs, improve fan utilization. The large data storage, processing, analysis and diagnosis servers are established in the pre-detection platform of the centralized control center. After collecting all data of the wind farm, the data resources stored by the professional manufacturers are integrated, and various types of fan fault models are established. The centralized control center trains diagnostic analysts to find out the characteristics and rules of the fault curve through comparison, and to real-time numbers. According to the diagnosis and analysis, the improvement measures are put forward to prevent the expansion of fan failure in advance, develop relevant monitoring and alarm systems, and combine the traditional fan monitoring system to improve the equipment management ability of the monitoring center.