Abstract
<title>Abstract</title> <p>Dissolved Gas Analysis is a well-established technique for diagnosing incipient faults in oil-immersed power equipment by analysing the concentrations and patterns of gases generated under thermal and electrical stresses within insulating media. Ratio-based methods offer simplicity and standardization however their reliance on fixed thresholds often leads to ambiguous or conflicting interpretations, particularly at low gas concentrations. Graphical techniques are better at improving interpretability but suffer from the fixed boundaries that cannot represent the nonlinear behaviour of gases and may overlap under complex fault conditions. AI-based methods may improve the diagnostic capability by modeling nonlinear relationships, but rely heavily on training data, are not physically interpretable, and can give inconsistent results. To address these limitations, a Hybrid Confidence Fusion Model is proposed to integrate multiple diagnostic outputs via a relational database and then using a confidence fusion voting scheme to fuse multiple diagnostic outputs. This model assigns adaptive confidence scores and incorporates severity-based prioritization to resolve conflicts. The proposed approach will make diagnosis more consistent, robust under sparse and mixed fault conditions and provide more reliable maintenance for modern power equipment.</p>