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- W4223956017 abstract "Compression-ignition (CI) engines, aka diesel engines, are responsible for an essential percentage of the world-polluting emissions. Moreover, bearings installed in industrial machinery constitute the most common failure affecting global energy consumption. Since industries’ energy demand has a growing tendency, efficient maintenance is a must. Maintenance requires a fast and accurate diagnosis, commonly based on an intrusive or expensive sensor to capture monitoring signals, i.e. pressure, emissions, temperature, fuel consumption and rotational speed. Here, a vibration signal-based approach is introduced to combustion engines and bearings diagnosis. Namely, a multi-scale permutation entropy (MPE)-based feature extraction is conducted within a variability-based relevance analysis (VRA) stage to feed a straightforward classifier, the K-nearest neighbours (KNN). Accuracy was validated using a signals’ database from a single-cylinder engine under multiple work conditions. Also, the methodology is compared through classification accuracy of a widely known bearing vibration signal database obtaining an outstanding performance." @default.
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- W4223956017 date "2022-04-14" @default.
- W4223956017 modified "2023-09-27" @default.
- W4223956017 title "Diesel engine diagnosis based on entropy of vibration signals and machine learning techniques" @default.
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- W4223956017 doi "https://doi.org/10.1049/ell2.12490" @default.
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