Abstract:Predictive maintenance approaches leveraging integrated knowledge, fleet-wide data and machine-learning techniques allow for earlier warnings on impeding failures and for higher accuracy in remaining useful life predictions compared with traditional prognostics. However, in case relative to correctly predicted maintenance needs, missed detections or false alarms occur too often, follow-up costs, e.g. due to cascading effects or unnecessary inspection effort, can outweigh the advantages. Here, we show that a ge… Show more
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