Abstract
Fault signals in high-voltage (HV) power plant assets are captured using the electromagnetic interference (EMI) technique. The extracted EMI signals are taken under different conditions, introducing varying noise levels to the signals. The aim of this work is to address the varying noise levels found in captured EMI fault signals, using a deep-residual-shrinkage-network (DRSN) that implements shrinkage methods with learned thresholds to carry out de-noising for classification, along with a time-frequency signal decomposition method for feature engineering of raw time-series signals. The approach will be to train and validate several alternative DRSN architectures with previously expertly labeled EMI fault signals, with architectures then being tested on previously unseen data, the signals used will firstly be de-noised and a controlled amount of noise will be added to the signals at various levels. DRSN architectures are assessed based on their testing accuracy in the varying controlled noise levels. Results show DRSN architectures using the newly proposed residual-shrinkage-building-unit-2 (RSBU-2) to outperform the residual-shrinkage-building-unit-1 (RSBU-1) architectures in low signal-to-noise ratios. The findings show that implementing thresholding methods in noise environments provides attractive results and their methods prove to work well with real-world EMI fault signals, proving them to be sufficient for real-world EMI fault classification and condition monitoring.
Highlights
The thresholding process consists of creating value ranges which correspond to noise dependent upon the thresholding method values both within and outwith these ranges, they are altered
The mean test accuracies over the ten runs are provided by testing the thresholding architectures against corresponding known noise level data and the results can be found in architectures, with the alternating known noise level datasets
Condition monitoring field, as to the author’s knowledge soft thresholding is the only learned thresholding method used in the condition monitoring field prior to this study and it is shown to be outperformed in a majority of noise environments in electromagnetic interference (EMI) signal data
Summary
Condition monitoring is carried out manually by experts [2] observing electromagnetic interference (EMI) data in differing forms based upon individual preferences following the observations present faults are classified, this method is often used to detect partial-discharge (PD) in assets [3]. The current expert-led manual approach to condition monitoring is problematic operationally due to high cost, sole reliance on experts to detect and classify faults, and lack of continuous monitoring. This leads to faults going unnoticed when experts are not available to carry out condition monitoring practices. With machine learning (ML) being implemented to focus on condition monitoring using
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