Electromagnetic interference (EMI) diagnostics aid in identifying insulation and mechanical faults arising in high voltage (HV) electrical power assets. EMI frequency scans are analysed to detect the frequencies associated with these faults. Time-resolved signals at these key frequencies provide important information for fault type identification and trending. An end-to-end fault classification approach based on real-world EMI time-resolved signals was developed which consists of two classification stages each based on 1D-convolutional neural networks (1D-CNNs) trained using transfer learning techniques. The first stage filters the in-distribution signals relevant to faults from out-of-distribution signals that may be collected during the EMI measurement. The fault signals are then passed to the second stage for fault type classification. The proposed analysis exploits the raw measured time-resolved signals directly into the 1D-CNN which eliminates the need for engineered feature extraction and reduces computation time. These results are compared to previously proposed CNN-based classification of EMI data. The results demonstrate high classification performance for a computationally efficient inference model. Furthermore, the inference model is implemented in an industrial instrument for HV condition monitoring and its performance is successfully demonstrated in tested in both a HV laboratory and an operational power generating site.
- filtering theory
- fault diagnosis
- learning (artificial intelligence)
- feature extraction
- power engineering