Framework for predictive maintenance method selection

Wieger Willem Tiddens, Jan Braaksma, Tiedo Tinga

Research output: Working paper

41 Downloads (Pure)


Purpose: Many asset owners and maintainers have the ambition to better predict the future state of their equipment to take 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.
Design/methodology/approach: 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.
Findings: the proposed classifications of ambition levels, data types and types of predictive maintenance methods prove to clarify the complex selection process considerably. This is confirmed by the case studies, which demonstrate that the proposed framework accelerates this process in practical cases.
Originality: The main contribution of this work is the insight in the relation (and interdependence) between predictive maintenance methods, ambition level and available data. Additional contributions are the proposed classifications / definitions of these three aspects.
Original languageEnglish
PublisherUniversity of Twente
Number of pages17
Publication statusPublished - Sept 2022


Dive into the research topics of 'Framework for predictive maintenance method selection'. Together they form a unique fingerprint.

Cite this