Partial discharge (PD) detection is essential for assessing insulation health in high-voltage equipment but is hindered by noise and non-stationary signals. This paper proposes an explainable, physics-informed hybrid framework for PD detection under synthetic noisy conditions. The method integrates wavelet-based denoising with time-frequency feature extraction using short-time Fourier transform (STFT) and continuous wavelet transform (CWT). A dual-input deep learning architecture combining convolutional layers, bidirectional long short-term memory (BiLSTM), and multi-head attention captures spatial-temporal dependencies from raw and engineered features. Explainable artificial intelligence (XAI) techniques, including attention visualization and Integrated Gradients, are incorporated to improve interpretability by identifying signal regions that drive model decisions. On a controlled synthetic dataset, the proposed framework achieves 89.38% accuracy, demonstrating promising discrimination between PD and noise signals. These results establish a proof of concept; validation on real-world PD datasets remains an important direction for future work.
Partial discharge (PD) is a nondestructive testing method to identify defects inside electrical insulation. Hence, accurate PD measurement plays a crucial role in the condition monitoring of high-voltage apparatus. Since PD signals have very low amplitude, the real-life PD signals are often perturbed with field noise a...
Reliable partial discharge detection is critical for ensuring the insulation integrity of automotive electrical machines. However, deploying highly accurate diagnostic algorithms on resource-constrained edge computing devices remains challenging due to severe industrial background noise. This paper proposes a lightweig...
Yu-Qi Zheng, Le-Rong Hong, Hong-Zhuang Cao et al.· 2026 IEEE 22nd International...· 0 citations
Reliable and auditable assessment of insulation aging in high-voltage assets is essential for condition-based maintenance and failure prevention. We present a methodology that combines controlled excitation using a broadband reference signal (MHz-range chirp) with explainable AI (XAI) to enable interpretable classifica...
Mikhail Olkhovskiy, E. Műllerová, Petr Martínek et al.· 2026 International Conferenc...· 0 citations
Fault diagnosis in power transmission systems requires the simultaneous identification of fault type and location from high-dimensional, multichannel, and temporally evolving measurements. Conventional deep learning approaches may struggle to jointly capture localized transient patterns, global temporal dependencies, a...
Shantanu Dinesh Wani, S. Abinaya, S. Abirami et al.· Frontiers in Artificial Inte...· 0 citations
An interpretable multi-sensor fault diagnosis framework, termed LCWC-DAMiT, is proposed that introduces a learnable continuous wavelet convolution (LCWC) module to incorporate time–frequency priors into patch embedding and focuses on physically meaningful fault characteristic frequencies and informative sensor location...
