# Decision Framework for Predictive Maintenance Method Selection
###### Abstract  
Many asset owners and maintainers have the ambition to better predict the future state of their equipment to make timely and better-informed maintenance decisions. Although many methods to support high-level maintenance policy selection are available, practitioners still often follow a costly trial-and-error process in selecting the most suitable predictive maintenance method. To address the lack of decision support in this process, this paper proposes a framework to support asset owners in selecting the optimal predictive maintenance method for their situation. The selection framework is developed using a design science process. After exploring common difficulties, a set of solutions is proposed for these identified problems, including a classification of the various maintenance methods, a guideline for defining the ambition level for the maintenance process, and a classification of the available data types. These elements are then integrated into a framework that assists practitioners in selecting the optimal maintenance approach. Finally, the proposed framework is successfully tested and demonstrated using four industrial case studies. It can be concluded that the proposed classifications of ambition levels, data types and types of predictive maintenance methods clarify and accelerate the complex selection process considerably. decision support; prognostics; predictive maintenance; condition-based maintenance Article Decision Framework for Predictive Maintenance Method Selection  
[PERSON], [PERSON], [PERSON]. policy). As modern systems are, in most cases, critical to operations, proactive maintenance policies have become common practice. The second step in selecting the most suitable approach is to determine how this policy must be executed. If, for example, a preventive maintenance _policy_ has been selected, how can the optimal moment of replacement be determined? The methods and techniques that support the execution of a specific policy typically estimate the mean time between failures (MTBF), assess the system condition [5] or predict the remaining useful life (RUL). Since real-time logging of various parameters, sensors and microprocessors is feasible at present, this type of (big) data can assist in the execution of the selected maintenance policy [6]. However, the number of available methods and techniques is enormous (see, e.g., the recent review in [7]), while at the same time, limited support for this second-level decision is available in the scientific literature. About a decade ago, a study by [PERSON] and [PERSON] [8] already showed that companies experience a gap between the potential and realised benefits of advanced maintenance methods and techniques. This is confirmed by more recent studies conducted by the authors [9; 10], demonstrating that practitioners still find it difficult to select and effectively apply these techniques in practice. The third and final step is to make detailed decisions on the implementation of the selected method. This concerns, for example, the selection of the sensors or the condition monitoring technique [11] to be used, determining the critical parts in the system [12], or the algorithm [13] and feature selection [14] in data-driven approaches. For all these detailed decisions directly related to the selected method, despite some remaining challenges on application in a real industrial setting (e.g., limited data) [15], quite some decision support methods are already available in the literature. It can therefore be concluded that mainly the selection of a suitable maintenance method or technique (step 2) is not well-supported yet. These methods and techniques to operationalise the policy are denoted as _maintenance methods_ (MMs) in this paper. For a reactive (corrective) policy, the operationalisation is rather trivial, as it just requires a repair or replacement as soon as a failure occurs.