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- W4285143039 abstract "Smart Maintenance is an important approach for dealing with reliability and maintenance issues, driven by the need to reduce downtime and costs. One of the challenges, especially for large systems, is the number of monitored features, and their impact on the system when the features are correlated or have casual relations. The proposed Predictive Maintenance methodology allows automatic unsupervised feature selection without prior knowledge, enabling early detection of faults and improving system reliability. The model developed here uses a causality approach, to select the dominant features by their causality score. The effectiveness of the methodology is demonstrated using real data collect by the Israeli Air Force (IAF) helicopter squadron" @default.
- W4285143039 created "2022-07-14" @default.
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- W4285143039 date "2022-01-01" @default.
- W4285143039 modified "2023-09-26" @default.
- W4285143039 title "Temporal causality-based feature selection for fault prediction in rotorcraft flight controls" @default.
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- W4285143039 doi "https://doi.org/10.1016/j.ifacol.2022.04.199" @default.
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