On-line vibration monitoring of bearing faults in induction machine usin Cyclic Spectral Analysis

Abstract

Abstract.A robust signal processing method for developing an on-line bearing fault detection based on Cyclic Spectral Analysis is presented in this paper. The exploitation of cyclostationarity has recently been proved extremely fruitful to conceive high accuracy diagnostics tools. Cyclostationarity encompasses a subclass of nonstationary signals which exhibit some cyclical behaviour; it ideally fits the property of many rotating and reciprocating machine vibrations, due to the inherent periodic modulations that these sustain during operation. The traditional spectral analysis is not dedicated for non-stationary signals and for an on-line diagnosis. The proposed technique is tested with different bearing conditions, the results show that this diagnosis. The proposed technique is tested with different bearing conditions, the results show that this technique is especially useful for non-stationary features extraction; it gives a good basis for an integrated induction motor condition monitor.

Comparative study between two artificial intelligence techniques (NN and SVM) applied in fault diagnosis of Wind Turbine

“…The ball defects default can be seen as an inner and outer race default since the defective ball can touch the inner or outer ring. The characteristic frequencies for defects are given by [2], [3], [4]: For a ball fault:…”