Using Deep Neural Networks for On-Load Tap Changer Audio-based Diagnostics (CROSBI ID 299585)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Šečić, Adnan ; Krpan, Matej ; Kuzle, Igor
engleski
Using Deep Neural Networks for On-Load Tap Changer Audio-based Diagnostics
This paper proposes a sound separation methodology based on deep learning neural network (DLNN) to extract useful diagnostic material in non-invasive audio-based On-Load Tap Changer (OLTC) diagnostics. The proposed methodology has been experimentally verified on both artificial mixtures (created by reproducing the targeted data by the speakers placed next to the active transformers) and actual mixtures (recorded by the microphone during OLTC live operation in the field). The results show that the method produces high-quality estimates (correlation to referent fingerprints ρ > 0.9) compared to other sound separation methods, e.g. Independent Component Analysis (ICA). The proposed framework can also perform source separation from a monaural mixture (mixture recorded with a single microphone only), which is impossible for ICA methods. Moreover, the results show that DLNN trained with healthy OLTC data produces diagnostically valuable estimates even when fed with a faulty OLTC audio mixture. For that reason, once trained, the DLNN can produce the diagnostic signal estimates from monaural mixtures that can be used with existing vibration-based diagnostic methods.
OLTC ; maintenance ; Vibro-acoustic diagnostics ; Deep Learning Neural Networks ; Harmonic-Percussive Source Separation ; Semi Blind Source Separation
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Podaci o izdanju
37 (4)
2022.
3038-3050
objavljeno
0885-8977
1937-4208
10.1109/TPWRD.2021.3121472