Effect of cascade methods on vibration defects detection

Abstract

Within the framework of monitoring rotating machines, vibration analysis remains an effective tool for fault detection. This analysis generally consists of measuring acceleration signals from critical and judiciously chosen points of a machine with the help of piezoelectric sensors. However, relevant information concerning the machine health can be masked by disturbances such as noise. The detection reliability will then be conditioned directly by the quality of the collected signal. Signal preprocessing methods, in particular denoising methods, can significantly improve the detection quality in terms of reliability. In this paper we aim to compare two methods of denoising based on signal spectral content analysis: discrete wavelet transform and empirical mode decomposition. A first study is carried out in order to optimize specific parameters related to each of the two methods, starting from experimental data obtained on degraded bearings. In fact for each parameter, one has to define conditions which allow the best detection of periodic pulses in vibration signals thanks to indicators such as kurtosis and crest factor. The second study consists of assessing the effectiveness of each denoising method on a vibration signal measured on a failed bearing. This signal is then disturbed by various noises simulated with variable levels. This study aims to show the effectiveness of each of these two methods on the early detection of impulse defects.

Fusion of Vibration and Current Signatures for the Fault Diagnosis of Induction Machines

“…A very narrow pulse-like antisymmetric wavelet, such as the Daubechies 10 wavelet or any higher-order wavelet of the Daubechies family, was found to perform well for the WPD. In this study, the kurtosis and crest factors of the wavelet coefficients regarding multiple wavelet functions and decomposition levels were calculated [50]. e kurtosis and crest factors of the 1 st level WPD coefficients using several wavelet functions such as Daubechies (db), symlets (sym), Coiflets (coif ), biorthogonal (bior), and discrete Meyer (dmey) are compared in Figure 7.…”

Hybrid data fusion approach for fault diagnosis of fixed-axis gearbox

“…Both techniques were found effective and suggested to be used in condition monitoring. 13 In a recent study, tooth fault in a gearbox based on cyclostationarity and EMD has been proposed. 14…”

Analysis of hydropower unit vibration signals based on variational mode decomposition

“…During the late 1990s, Huang et al (1998) introduced the method called empirical mode decomposition (EMD), which has a high timefrequency resolution and adaptive decomposition characteristics. There are many publications (Chiementin et al, 2011;An et al, 2012;An and Pan, 2015;Georgoulas et al, 2013;Shao et al, 2015) from Huang, the inventor of EMD. However, the EMD method has problems such as mode mixing and end effects.…”