{
  "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_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Alexandria Engineering Journal. Volume 144, May 2026, Pages 58-78",
  "arxiv": "2609.20214",
  "author_total": 4,
  "authors": [
    {
      "name": "Huy Hoang Le"
    },
    {
      "name": "Ba Tu Phung"
    },
    {
      "name": "Dai Huynh"
    },
    {
      "name": "Kim-Anh Nguyen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4769kqkkmh2ax.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.20214",
  "fulltext_url": null,
  "id": "4769kqkkmh2ax",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.20214",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.20214",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4769kqkkmh2ax.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:19Z",
      "native_id": "2609.20214",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.20214"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Transformer fault diagnosis using an efficient simulation-driven variational quantum classifier with domain-aware feature encoding",
  "title_text": "Transformer fault diagnosis using an efficient simulation-driven variational quantum classifier with domain-aware feature encoding",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:19Z",
  "url": "https://research.draconitic.com/paper/4769kqkkmh2ax",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1016/j.aej.2026.04.032",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}