Machine learning-based predictive maintenance: empirical insights of challenges and countermeasures

Predictive Maintenance (PdM) has gained attention to reduce production-related costs and downtime, with Machine Learning (ML) emerging as a prominent technique. However, ML benefits are often achieved using laboratory or reference datasets. These may differ from real-world industrial data, raising doubts about ML applicability in real-world settings. This work addresses this issue, showing that ML adoption for PdM in industry is low. Furthermore, using a Delphi study, key challenges hindering ML adoption are identified and prioritised. Interestingly, some relevant challenges (e.g. the need for training employees) are overlooked by the literature. Furthermore, to boost PdM adoption, we identified and prioritized potential countermeasures based on practitioner insights. It emerged that some countermeasures can tackle multiple challenges (e.g. training programs). Our findings benefit both scholars and practitioners. Scholars may focus on relevant challenges to facilitate ML adoption for PdM. Practitioners are provided with a set of effective countermeasures to cope with relevant challenges.