Research on Feature Extraction Method of Hydropower Unit Vibration Signal based on GAF-CNN
As the operational lifespan of hydropower units increases,the volume of operational data correspondingly expands.This growth presents several challenges in identifying the health status of these units,including an overabundance of health samples and ambiguous characteristic parameters.This study proposes a method that combines the strengths of the Gram Angular Field(GAF)and Convolutional Neural Network(CNN)in feature representation and extraction to process the healthy operational data of hydropower units.The vibration signal of the unit is encoded by the GAF to generate a corresponding feature image,which is then input into the CNN model for feature extraction and classification.The performance of this GAF-CNN feature extraction method is compared with the traditional Long Short-Term Memory(LSTM)network model using both simulated and measured unit data.The results demonstrate that the feature extraction method of GAF-CNN model has higher accuracy and robustness,and can still maintain good accuracy and anti-noise performance in the face of longer time series data,which provides data basis for the performance improvement of hydropower unit health assessment model.
NETL
NSTL
万方数据
