NDLI: 1D-CNN based real-time fault detection system for power asset diagnostics

Content Provider	IET Digital Library
Author	Mitiche, Imene Nesbitt, Alan Conner, Stephen Boreham, Philip Morison, Gordon
Abstract	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.
Starting Page	5766
Ending Page	5773
Page Count	8
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 24, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/24
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2020.0773
Journal	IET Generation, Transmission & Distribution
Publisher Date	2020-08-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	1D-CNN 1D-convolutional Neural Networks Civil And Mechanical Engineering Computing Computational Complexity Computational Time Reduction Condition Monitoring Digital Signal Processing Electromagnetic Compatibility Electromagnetic Interference Electromagnetic Interference Diagnostics Aid EMI Frequency Scans EMI Measurement EMI Time-resolved Signal End-to-end Fault Classification Fault Diagnosis Fault Signal Fault Type Identification Feature Extraction Filtering Method in Signal Processing Filtering Theory High Voltage Electrical Power Assets In-distribution Signal Filtering Insulation Identification Interference Learning in AI Maintenance And Reliability Mathematical Analysis Mechanical Engineering Mechanical Engineering Application of IT Mechanical Fault Identification Neural Computing Technique Out-of-distribution Signal Filtering Power Asset Diagnostics Power Engineering Computing Real-time Fault Detection System Signal Classification Signal Processing And Detection Time Domain Analysis Transfer Learning Vibrational Signal Processing Vibrations And Shock Wave
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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