Supporting the Selection of Prognostic-based Decision Support Methods in Manufacturing

In manufacturing enterprises, maintenance is a significant contributor to the total company’s cost. Condition Based Maintenance (CBM) relies on prognostic models and uses them to support maintenance decisions based on the current and predicted health state of equipment. Although decision support for CBM is not an extensively explored area, there exist methods which have been developed in order to deal with specific challenges such as the need to cope with real-time information, to prognose the health state of equipment and to continually update decision recommendations. We propose an approach for supporting analysts selecting the most suitable combination(s) of methods for prognostic-based maintenance decision support according to the requirements of a given maintenance application. Our approach is based on the ID3 decision tree learning algorithm and is applied in a maintenance scenario in the oil and gas industry.