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- W2326048563 abstract "Abstract Maintenance is essential to prevent catastrophic failures in rotating machinery. A crack can cause a failure with costly processes of reparation, especially in a rotating shaft. In this study, the Wavelet Packets transform energy combined with Artificial Neural Networks with Radial Basis Function architecture (RBF-ANN) are applied to vibration signals to detect cracks in a rotating shaft. Data were obtained from a rig where the shaft rotates under its own weight, at steady state at different crack conditions. Nine defect conditions were induced in the shaft (with depths from 4% to 50% of the shaft diameter). The parameters for Wavelet Packets transform and RBF-ANN are selected to optimize its success rates results. Moreover, ‘Probability of Detection’ curves were calculated showing probabilities of detection close to 100% of the cases tested from the smallest crack size with a 1.77% of false alarms." @default.
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- W2326048563 date "2016-08-01" @default.
- W2326048563 modified "2023-10-03" @default.
- W2326048563 title "Automatic condition monitoring system for crack detection in rotating machinery" @default.
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- W2326048563 doi "https://doi.org/10.1016/j.ress.2016.03.013" @default.
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