Practical Application of Predictive Maintenance - Solving the Challenges

Industry is eager to harvest the potential of predictive maintenance (PdM). At the same time, only a fraction of the methods proposed in the past decade is actually applied in practice. This paper will identify the most prominent barriers for practical application of PdM, which are mainly related to the quality, relevance and availability of data. After that, three solution directions will be presented. The first solution aims to properly match ambition level with the available data and knowledge. The second set of solutions circumvents the lack of data by using physical models in addition to data. The third set of solutions addresses alternative ways of collecting data, including experimental test benches, experiments on fielded systems, and using numerical (simulation) models to generate data. Finally, the required standardized registration of (failure) data will be addressed.

Keywords: Predictive maintenance, Data quality, Diagnostics, Prognostics, Physics-of-failure, Hybrid methods.