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@article{207173,
        author = {Damini Choukse},
        title = {predictive maintenance of power transformers using quantum machine learning algorithms : a frame work and literature- based Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {4124-4132},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207173},
        abstract = {Power transformers are among the most critical and capital-intensive assets in electrical transmission and distribution networks, and their unplanned failure can trigger cascading outages and substantial economic loss. Traditional maintenance philosophies, whether time-based or reactive, are increasingly being displaced by predictive maintenance (PdM) strategies that use condition-monitoring data, most notably dissolved gas analysis (DGA), to anticipate incipient faults before they escalate. Classical machine learning (ML) methods such as support vector machines, decision trees, and artificial neural networks have demonstrated strong results in this domain, yet they face scalability and feature-space limitations as sensor networks generate increasingly high-dimensional, noisy data streams. This paper surveys classical ML-based transformer PdM and examines how quantum machine learning (QML) algorithms, including quantum support vector machines, variational quantum classifiers, quantum neural networks, and quantum annealing-based optimization, could extend these capabilities by exploiting high-dimensional Hilbert spaces and quantum parallelism. Building on this literature, a hybrid quantum-classical framework for transformer health monitoring is proposed and illustrated with diagrams, comparative tables, and literature-reported performance figures. The paper concludes by outlining the hardware, noise, and scalability barriers that currently constrain quantum-enhanced PdM and identifies promising directions for near-term research.},
        keywords = {Predictive maintenance, power transformer, dissolved gas analysis, quantum machine learning, quantum support vector machine, variational quantum classifier, NISQ},
        month = {July},
        }