A Custom Stacking-Based Ensemble Learning Approach to Predict Failure of Stripper Well

Kumbhani, Smit and Dharaiya, Vishesh (2022) A Custom Stacking-Based Ensemble Learning Approach to Predict Failure of Stripper Well. In: 2nd International Conference on Communication and Artificial Intelligence, ICCAI 2021, 19 November 2021 through 21 November 2021, Virtual, Online.

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Prediction of equipment failure has always been a challenging task. Analytical and statistical approaches for prediction of equipment failure have been employed for a long time. Analytical approach is based on criterion, while statistical approach is data driven. Despite its accuracy, statistical approaches fail with large data entries having high dimensionality. Advanced machine learning techniques come to rescue. In this study, an effort has been made to predict failure of stripper well with classical machine learning algorithms followed by a custom stacking-based ensemble learning approach. Classical machine learning algorithms like Support Vector Machine, K-Nearest Neighbour, Logistic Regression, Gradient boosting etc. have been applied to predict failure instance. Micro F1-score has been selected as a measure of prediction accuracy. A novel custom ensemble machine learning approach has been implemented to obtain better prediction accuracy compared to previously applied algorithms. Proposed novel approach has successfully predicted classification case of failure with micro F1-score of ~0.9887. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Ensemble learning; Failure prediction; Stripper well
Subjects: Computer science
Others > Metallurgy Metallurgical Engineering
Materials Engineering > Materials engineering
Divisions: Department of Computer Science & Engineering
Department of Material Science Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 19 Jul 2022 09:09
Last Modified: 19 Jul 2022 09:09
URI: http://raiith.iith.ac.in/id/eprint/9786
Publisher URL: http://doi.org/10.1007/978-981-19-0976-4_28
OA policy: https://v2.sherpa.ac.uk/id/publication/33093
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