Decision Framework for Predictive Maintenance Method Selection

Many asset owners and maintainers have the ambition to better predict future state of their equipment make timely better-informed maintenance decisions. Although many methods support high-level policy selection are available, practitioners still often follow a costly trial-and-error process in selecting most suitable predictive method. To address lack decision this process, paper proposes framework optimal method for situation. The is developed using design science process. After exploring common difficulties, set solutions proposed these identified problems, including classification various methods, guideline defining level available data types. These elements then integrated into that assists approach. Finally, successfully tested demonstrated four industrial case studies. It can be concluded classifications levels, types clarify accelerate complex considerably.