首页|New Machine Learning Findings from Tilburg University Reported (Transition Paths for Condition-based Maintenance-driven Smart Services)
New Machine Learning Findings from Tilburg University Reported (Transition Paths for Condition-based Maintenance-driven Smart Services)
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NETL
NSTL
Wiley
New research on Machine Learning is the subject of a report. According to news origi- nating from Tilburg, Netherlands, by NewsRx correspondents, research stated, "This research investigates growth inhibitors for smart services driven by condition-based maintenance (CBM). Despite the fast rise of Industry 4.0 technologies, such as smart sensoring, internet of things, and machine learning (ML), smart services have failed to keep pace." Financial supporters for this research include Netherlands Organization for Scientific Research (NWO), Dutch Institute for Advanced Logistics (DINALOG). Our news journalists obtained a quote from the research from Tilburg University, "Combined, these technologies enable CBM to achieve the lean goal of high reliability and low waste for industrial equipment. Equipment located at customers throughout the world can be monitored and maintained by manufacturers and service providers, but so far industry uptake has been slow. The contributions of this study are twofold. First, it uncovers industry settings that impede the use of equipment failure data needed to train ML algorithms to predict failures and use these predictions to trigger maintenance. These empirical settings, drawn from four global machine equipment manufacturers, include either under- or over-maintenance (i.e., either too much or too little periodic maintenance). Second, formal analysis of a system dynamics model based on these empirical settings reveals a sweet spot of industry settings in which such inhibitors are absent. Companies that fall outside this sweet spot need to follow specific transition paths to reach it. This research discusses these paths, from both a research and practice perspective. Condition-based maintenance (CBM)-driven smart services have become technically much more feasible and affordable through the fast rise of Industry 4.0 technologies, such as smart sensoring, internet of things, and machine learning (ML), and yet such smart services have failed to keep pace with this rise. A key CBM-specific complication for service growth is that, in order to reach high-quality performance of such smart services, the ML-algorithms that drive them need significant numbers of equipment failures to learn from. Such failure data may be hard to obtain for OEMs, either because certain components fail to often (and hence are replaced, making the failure data obsolete) or too rarely (making the time needed to collect failure data very long). Also, customers may be very much reliability focused (leading to much preventive maintenance and so few failures) or not sufficiently reliability focused (leading to much unmonitored corrective maintenance and low interest in advanced services such as CBM)."
TilburgNetherlandsEuropeCyborgsEmerging TechnologiesMachine LearningTechnologyTilburg University
NETL
NSTL
Wiley
