Quantifying the suitability and feasibility of predictive maintenance approaches

Predictive maintenance is a promising concept for maintenance optimization which requires reliable and accurate predictions of a system's lifetime. Despite the many studies on this topic, selecting the best approach is still a topic of debate. In general, predictive maintenance approaches can be roughly classified as knowledge-based, data analytics and physics-based models. However, this classification does not provide maintainers clear guidance on how to select the most suitable approach for a specific case. This work, therefore, presents a selection method for this process. For that purpose, a list of selection criteria was established, and six predictive maintenance approaches were analysed. The proposed selection process is based on two main groups of criteria: the suitability criteria check the match with the desired ambition level of predictive maintenance, while the feasibility criteria identify whether this can be realized, given the labour, models and data available. Finally, three case studies are presented, demonstrating that the tool effectively guides to an optimal approach.