基于人工智能和深度学习的电力设备故障诊断方法OACSTPCD

A power equipment fault diagnosis method based on artificial intelligence and deep learning is proposed to address the issue of low accuracy caused by neglecting noise elimination in traditional association rule algorithms.By capturing signal changes during normal operation of the equipment,collecting frequency domain fault data,and mapping it into a comprehensive grayscale fault image using metric spatial distance,overfitting functions are used to eliminate image noise and obtain pure fault data.Artificial intelligence algorithms are used to fuse these data to form a single feature set of equipment fault vectors.A fault diagnosis model is constructed through deep learning,and vector data is input,Output fault types to achieve accurate diagnosis.The simulation experiment results show that this method has higher diagnostic accuracy and practical application value.