Sep 2026· 2026 International Conference on Diagnostics in Electrical Engineering (Diagnostika)· pp. 1-6· 0 citations· 6 references
Abstract
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 classification of aging levels. The system records the response and constructs a multi-channel input comprising time-domain representations, spectral features, and differential-to-reference channels for a convolutional neural network (CNN). For interpretability, we employ SHAP (SHapley Additive exPlanations), Grad-CAM (Gradient-weighted Class Activation Mapping), and Integrated Gradients (IG) to provide attribution maps indicating the time-frequency regions and specific channels that drive the predicted class. In controlled aging experiments, the explanations consistently emphasize discriminative spectral bands and support error analysis by flagging likely misclassifications. The approach increases confidence in model outputs and supports deployment in condition-based maintenance workflows.
A framework that combines vibration-based and image-based damage assessment with explainable artificial intelligence and a data-driven digital twin to support a continuously updated data-driven digital twin for structural health monitoring is presented.
V. K. Kiran, Ranjitha B. Tangadagi, M. Manjunatha· Frontiers in Built Environme...· 0 citations
To address the systematic errors introduced by the coupling of excitation current drift, reference voltage drift, and ADC gain/offset in PT1000 platinum resistance measurement systems, this paper proposes an error-structure-aware two-stage compensation framework. A three-dimensional physical feature vector is derived f...
Qi-Tao Liu, Yi Fan· International Conference on...· 0 citations
This study proposes a physics-proxy-residual-guided cross-attention ensemble neural network (PGAE-NN) for oil-immersed transformer bushing insulation-condition assessment and early warning. Eight core indicators are selected from twelve candidates via Pearson correlation and Fisher discriminant analyses, with four insu...
Motor current signature analysis (MCSA) enables non-invasive condition monitoring of three-phase induction motors, but most published studies use short laboratory records with artificially seeded faults and report only a discrete fault label without any measure of confidence. This paper proposes a physics-informed temp...
Ahmed Mohammed, B. Abdullah, Qian Zhang et al.· Machines· 0 citations
The results indicate that classification, probability calibration, OOD detection, noise robustness, and feature-level interpretability can be jointly delivered by a single energy-based model on bearing fault diagnosis benchmarks, without auxiliary classifiers or post-hoc explanation modules.
E. Pacheco, C. Pedroso· IEEE Access· 0 citations
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