The Role of AI in Revolutionizing Predictive Maintenance Systems: A Focus on Oil and Gas Industry

This research paper explores the potential of revolutionizing Predictive Maintenance in Bahrain’s oil and gas industry by integrating artificial intelligence technologies into traditional maintenance approaches. Traditional Maintenance approaches primarily rely on reactive and time-based maintenance approaches, often resulting in inefficient workforce utilization, increased operational cost and unwanted equipment downtime due to unexpected failures. To overcome such challenges, the study explores the application of machine learning techniques to assess equipment health and support data driven maintenance decisions.Using MATLAB software, five machine learning algorithms were selected to be developed, trained, tested and evaluated using pump dataset. Decision Tree, Naïve Bayes, Support Vector Machine, Artificial Neural Network and K Nearest Neighbor, all of these models were assessed using three key evaluation metrics. Which is Confusion Matrix, Minimum Classification Error and Area Under the Receiver Operating Characteristic Curve. All models demonstrated exceptional classification accuracy, exceeding 99%. Specifically, the Decision Tree and ANN models achieved 99.3% accuracy, were SVM reached 99.2%, and both NB and KNN attained 99.0%. while MCE scores of all models remained low, with Decision Tree at 0.67%, ANN at 0.7%, SVM at 0.8%, NB at 0.9% and lastly KNN at 1.0%. Among all models Decision Tree algorithm emerged as the most suitable machine learning model for industries deployment due to its great results additional to interpretability and decision-making transparency.Furthermore, SWOT analysis was conducted to assess the strategic implications of integrating AI technologies into industrial predictive maintenance. The analysis highlighted the strengths, opportunities, weaknesses and threats associated with such integration. Overall, the study concludes that the adoption of AI driven machine learning tools holds significant promise for enhancing and modernizing predictive maintenance in Bahrain’s oil and gas sector.