Automatic condition monitoring system for crack detection in rotating machinery Articles uri icon

publication date

  • August 2016

start page

  • 239

end page

  • 247

volume

  • 152

International Standard Serial Number (ISSN)

  • 0951-8320

Electronic International Standard Serial Number (EISSN)

  • 1879-0836

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. (C) 2016 Elsevier Ltd. All rights reserved.

keywords

  • cracked shaft detection; wavelet transform; intelligent classification systems; condition monitoring; artificial neural networks; parametric instability; speech secognition; vibration vnalysis; fault detection; rotor; identification; energy; reliability