Enhancing Unplanned Maintenance Priorities Using Artificial Neural Networks: Case Study From Oman Oil & Gas Industry

The energy sector is an exciting area of research and study due to the global demand for oil and gas, improving and optimizing related costs and risks, essentially maximizing the business output. Unplanned corrective maintenance can lead to unscheduled downtime if not attended effectively and efficiently. Maintenance can restore system healthiness and avoid catastrophic failures. This paper explores the prioritization of unplanned work orders (WO) for corrective maintenance using an Artificial Neural Network. A case study from Oil and Gas in Oman was investigated to check the actual practice. The paper proposes a new approach to prioritize unplanned maintenance work orders, considering a classification of three correlated features: failure severity, asset criticality, and reliability. The proposed method shows the needs of such correlated of these features. The Artificial Neural Network-based multi-layer perceptrons method is applied; 82.7% of work orders being tested from the case study shows the low probability of less than 50% on initial priority. The investigation reveals the effectiveness of the suggested method to be applied to get more priority insights. We recommend industrial practitioners to use the approach that supports prioritization of recourses and scheduling activities better, saving cost and avoiding system functional failures.