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Seman, L. O. , Buratto, W. G., Villarrubia Gonzalez, G., Leithardt, V. R. Q., Nied, A. & Stefenon, S. F. (2026). Differentiable neural search architecture with zero-cost metrics for insulator fault prediction. Results in Engineering. 29

L. O. Seman et al.,  "Differentiable neural search architecture with zero-cost metrics for insulator fault prediction", in Results in Engineering, vol. 29, 2026

@article{seman2026_1775424562521,
	author = "Seman, L. O.  and Buratto, W. G. and Villarrubia Gonzalez, G. and Leithardt, V. R. Q. and Nied, A. and Stefenon, S. F.",
	title = "Differentiable neural search architecture with zero-cost metrics for insulator fault prediction",
	journal = "Results in Engineering",
	year = "2026",
	volume = "29",
	number = "",
	doi = "10.1016/j.rineng.2026.109716",
	url = "https://www.sciencedirect.com/journal/results-in-engineering"
}

TY  - JOUR
TI  - Differentiable neural search architecture with zero-cost metrics for insulator fault prediction
T2  - Results in Engineering
VL  - 29
AU  - Seman, L. O. 
AU  - Buratto, W. G.
AU  - Villarrubia Gonzalez, G.
AU  - Leithardt, V. R. Q.
AU  - Nied, A.
AU  - Stefenon, S. F.
PY  - 2026
SN  - 2590-1230
DO  - 10.1016/j.rineng.2026.109716
UR  - https://www.sciencedirect.com/journal/results-in-engineering
AB  - Reliable monitoring of high-voltage insulators is critical for maintaining the stability of electrical power systems, particularly under environmental contamination that can lead to flashover. Traditional inspection techniques struggle to anticipate degradation dynamics, while data-driven models often rely on fixed neural architectures that inadequately capture the complex temporal patterns in leakage current signals. This work proposes a Differentiable Neural Architecture Search (DARTS) framework, based on zero-cost metrics, tailored for time series forecasting in insulator monitoring. The method based on DARTS integrates a mixed encoder-decoder design with learnable selection over long short-term memory, gated recurrent units, and transformer components, coupled with a cross-attention bridge featuring temporal bias and gating mechanisms. To ensure efficient architecture exploration, the search leverages metrics such as SynFlow and Jacobian covariance for early candidate screening, followed by a bilevel optimization stage with entropy and diversity regularization. Experiments on real-world leakage current data demonstrate that the discovered architectures outperform manually designed baselines, offering improved forecasting performance.
ER  -