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Enhancing predictive maintenance strategies for oil and gas
equipment through ensemble learning modeling
Abstract
In the field of oil and gas equipment management, frequent maintenance is conducted, resulting in unnecessary costs.
Relying solely on a single artificial intelligence model often leads to low predictive accuracy and inadequate robustness
because of the poor data quality. Therefore, a method based on ensemble learning modeling is proposed to accurately
assess the health status of industrial equipment and predict its remaining useful life. By conducting big data analysis on
the operational history data of all oil and gas equipment, various fault instances are extracted and grouped accordingly.
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