深度学习综述及其在汽轮机振动异常检测中的应用OA

The steam turbines are core equipment in power plants,whose safety and stability have decisive impact on the overall efficiency of the system.The application of traditional fault diagnosis methods,which rely on expert experience and rule-based models,is insufficient to meet the requirements for real-time performance and accuracy under complex operating conditions.In recent years,deep learning technology has demonstrated broad application prospects in data processing and anomaly detection due to its powerful capabilities in feature extraction and pattern recognition.This technology is reviewed,meanwhile,focusing on processing and feature extraction of steam turbine vibration data,this paper systematically explores the application of deep learning in data acquisition and anomaly detection based on data fusion.This study reviews the current state of deep learning technology and can provide reference for its application in engineering practice.