Publication: Automatic condition monitoring system for crack detection in rotating machinery
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2016-08
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Elsevier
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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.
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Keywords
Cracked shaft detection, Wavelet transform, Intelligent classification systems, Condition monitoring, Artificial neural networks
Bibliographic citation
Gómez, M., Castejón, C. & García-Prada, J. (2016). Automatic condition monitoring system for crack detection in rotating machinery. Reliability Engineering & System Safety, vol. 152, pp. 239–247.