aiqbench analysis
This study proposes a simulation-driven variational quantum classifier (VQC) specifically designed for diagnosing transformer faults using dissolved gas analysis, addressing challenges like nonlinear interactions and limited data. By integrating domain-aware feature encoding with a lightweight two-qubit representation, the framework demonstrates robust diagnostic accuracy under realistic noise constraints while minimizing quantum resource requirements. The work validates that simulation-informed modeling offers a practical pathway for deploying quantum-enhanced methods in industrial fault diagnosis scenarios.
Transformer fault diagnosis using an efficient simulation-driven variational quantum classifier with domain-aware feature encoding
arXiv:2609.20214v1 Announce Type: new Abstract: Early transformer fault diagnosis is challenged by nonlinear dissolved-gas interactions, overlapping fault signatures, and limited labeled data, while practical deployment further requires reliable performance under realistic computational constraints. This paper presents a simulation-driven modeling framework for dissolved gas analysis-based transformer fault diagnosis, in which a carefully engineered variational quantum classifier (VQC) is employed as the computational core and systematically analyzed through simulation. The framework integrates domain-aware feature modeling derived from Duval geometry with a lightweight two-qubit quantum representation, enabling nonlinear gas-interaction effects to be captured within a shallow parameterized circuit. A hybrid ZX-YY quantum feature map is designed to model non-commuting feature interactions, while a full-entanglement EfficientSU2 ansatz provides adequate expressive capacity under strict resource limits. Model behavior is evaluated using a comprehensive simulation pipeline including noise-aware circuit emulation, cross-dataset validation, and limited hardware-in-the-loop execution, allowing key effects of circuit depth, noise, and optimization strategy to be examined. Simulation results on benchmark dissolved-gas-analysis datasets demonstrate high diagnostic accuracy, strong generalization capability, and robustness to realistic noise levels with minimal quantum resources. The results highlight the effectiveness of simulation-informed modeling for practical transformer diagnostic applications, offering a reproducible and resource-efficient pathway for evaluating quantum-enhanced fault diagnosis methods.
Abstract:Early transformer fault diagnosis is challenged by nonlinear dissolved-gas interactions, overlapping fault signatures, and limited labeled data, while practical deployment further requires reliable performance under realistic computational constraints. This paper presents a simulation-driven modeling framework for dissolved gas analysis-based transformer fault diagnosis, in which a carefully engineered variational quantum classifier (VQC) is employed as the computational core and systematically analyzed through simulation. The framework integrates domain-aware feature modeling derived from Duval geometry with a lightweight two-qubit quantum representation, enabling nonlinear gas-interaction effects to be captured within a shallow parameterized circuit. A hybrid ZX-YY quantum feature map is designed to model non-commuting feature interactions, while a full-entanglement EfficientSU2 ansatz provides adequate expressive capacity under strict resource limits. Model behavior is evaluated using a comprehensive simulation pipeline including noise-aware circuit emulation, cross-dataset validation, and limited hardware-in-the-loop execution, allowing key effects of circuit depth, noise, and optimization strategy to be examined. Simulation results on benchmark dissolved-gas-analysis datasets demonstrate high diagnostic accuracy, strong generalization capability, and robustness to realistic noise levels with minimal quantum resources. The results highlight the effectiveness of simulation-informed modeling for practical transformer diagnostic applications, offering a reproducible and resource-efficient pathway for evaluating quantum-enhanced fault diagnosis methods.
This paper has been published in Alexandria Engineering Journal. Please cite the published version
Quantum Physics (quant-ph); Machine Learning (cs.LG)
arXiv:2609.20214 [quant-ph]
(or arXiv:2609.20214v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2609.20214 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
Alexandria Engineering Journal. Volume 144, May 2026, Pages 58-78
https://doi.org/10.1016/j.aej.2026.04.032 Focus to learn more DOI(s) linking to related resources
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