Classification and Prediction of Incipient Faults in Transformer Oil by Supervised Machine Learning using Decision Tree

Raj, Raymon Antony; Sarathkumar, D; Venkatachary, Sampath Kumar; Andrews, Leo John Baptist

doi:10.1109/aisp57993.2023.10134566

Cited by 15 publications

(1 citation statement)

References 10 publications

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“…Extending the scope to gas type classification in transformer fault scenarios, Raj et al [80] employed the DT model with no comparison of the other model. Their classification efforts centered around fault types using features like H2, CH4, C2H6, C2H4, and C2H2, with the accuracy of the DT at 62.9%, emerging as the model based on accuracy and Area Under Curve (AUC).…”

Section: Application Of Decision Tree Random Forest and Hybrid Modelsmentioning

confidence: 99%

Predictive Analytics Performance on Oil and Gas: A Significant Review

R Azmi,

Yusoff,

Mohd Salledud-din

2024

Preprint

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Enhancing the management and monitoring of oil and gas processes demands developing precise predictive analytics techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern Machine Learning techniques. Several review articles detail the developments in predictive maintenance and technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sector. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modelling. The review covers forms of Machine Learning techniques used in predictive analytic modelling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, comprising of the model’s categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification or clustering or prediction), the models' input and output parameters, performance metrics, optimal model, and benefits and its drawbacks. Additionally, suggestions for future research directions are provided to raise the potential of the associated knowledge and increase the accuracy of oil and gas predictive analytics models.

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Section: Application Of Decision Tree Random Forest and Hybrid Modelsmentioning

confidence: 99%