Wind Turbine Equipment Operation Fault Detection and Diagnosis Research Based on Sensor Technology and I-LSTM Algorithm
Effective fault detection and diagnosis will greatly improve the operational efficiency and reliability of wind turbine e-quipment,reduce maintenance costs,and ensure the smooth progress of production process.To achieve efficient equipment fault warning and maintenance,an equipment operation fault detection and diagnosis method based on sensor technology and machine learn-ing is researched.the box plots and wavelet packet denoising methods are used to preprocess the data signals transmitted by sensors.the bidirectional long short-term memory network is used to construct the time series prediction model.Based on prediction residuals and Bayesian probability theory,a signal anomaly recognition strategy is designed to monitor and warn faults in real-time.Through experimental testing,the diagnostic accuracy of the research and design model is 98.88％,with no missed diagnosis and a misdiagno-sis rate of below 1.5％,achieving early warning more than 14 hours in advance.Through practical application,the research and de-sign model meets the timely needs of wind turbine equipment fault warning,and can diagnose faults with high accuracy.
sensorsmachine learningmechanical equipmentfault detectiontime series prediction
万方数据
维普
