scholarly journals Detecting Punctual Damage to Gears through the Continuous Morlet Wavelet Transform

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Andre Luis Vinagre Pereira ◽  
Aparecido Carlos Gonçalves ◽  
Rubens Ribeiro ◽  
Fábio Roberto Chavarette ◽  
Roberto Outa
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Morlet Wavelet ◽  
Gear Pair ◽  
Continuous Wavelet Transforms ◽  
Gear Teeth ◽  
Morlet Wavelet Transform ◽  
Processing Techniques

In predictive maintenance, vibration signal analyses are frequently used to diagnose reducer failures because these analyses contain information about the conditions of the mechanical components. Reducer vibration signals are very noisy and the signal-to-noise ratio is so low that extracting information from the signal components is complex, especially in practical situations. Therefore, signal processing techniques are used to solve this problem and facilitate the retrieval of information. In this work, the adopted technique included noise-canceling technique, synchronous temporal mean (TSA), and continuous Morlet wavelet transform (CWT), designed to extract resources and diagnose local gear damage. These techniques are used in measured signals in an experimental workbench consisting of the gear pair coupled to a motor and a generator. The experiment was monitored according to the conditions of a gear pair throughout its useful life. The continuous wavelet transforms accurately identified faults in the gear teeth, and it was possible to detect in which tooth the fault was occurring.

Time-Frequency Spectral Power Assessment of Rolling Element Bearing Faults Using Adaptive Modified Morlet Wavelet Transform

2020 ◽  
Vol 13 ◽  
Author(s):  
O. P. Yadav ◽  
G.L. Pahuja
Keyword(s):  
Wavelet Transform ◽  
Vibration Signal ◽  
Morlet Wavelet ◽  
Rolling Element Bearing ◽  
Time Frequency ◽  
Bearing Defects ◽  
S Transform ◽  
Rolling Element ◽  
Element Bearing ◽  
Morlet Wavelet Transform

Objectives: The main objectives of this manuscript are to investigate and diagnose rolling element bearing defects in its inception time. Methods: Vibration signal generated by induction motor contains series of frequency components that have rich and viable information about bearing health conditions. Recently, maximum energy concentration (MEC) measure of time-frequency spectrum has been employed to investigate the small variations in low frequency biomedical signal spectrum. In this paper, the above technique has been modified and applied to study the bearing defects of induction motor using vibration signal and it is termed as adaptive modified Morlet wavelet (AMMW) transform. Initially, this proposed method was validated on two medium frequency synthetic time series signals in terms of MEC measurement at different signal to noise ratio (SNR). Results: The simulated results have depicted that AMMW method provides excellent time-frequency localization capability over other time-frequency methods like Morlet wavelet transform, modified Morlet wavelet transform, adaptive S-transform and adaptive modified S-transform. Then this method has been applied on standard database of vibration signal to determine of interquartile power for fault detection purpose and also fault index parameter termed as has been analyzed to detect small variation in vibration signals.

339 Morlet wavelet transform in EEG during perceptuo-motor tasks

1998 ◽  
Vol 30 (1-2) ◽  
pp. 131-132
Author(s):  
S. Slobounovl ◽  
R. Tutwiler ◽  
E. Slobounova
Keyword(s):  
Wavelet Transform ◽  
Morlet Wavelet ◽  
Motor Tasks ◽  
Morlet Wavelet Transform

Pattern Changes of Time-Shifted Vibration Signals on Wavelet Time-Scale Maps

1995 ◽  
Author(s):  
Da Jun Chen ◽  
Wei Ji Wang
Keyword(s):  
Wavelet Transform ◽  
Time Scale ◽  
Wavelet Transforms ◽  
Vibration Signal ◽  
Continuous Wavelet ◽  
Time Axis ◽  
Orthogonal Wavelet ◽  
Signal Component ◽  
Analysis Technique ◽  
Map Analysis

Abstract As a multi-resolution signal decomposition and analysis technique, the wavelet transforms have been already introduced to vibration signal processing. In this paper, a comparison on the time-scale map analysis is made between the discrete and the continuous wavelet transform. The orthogonal wavelet transform decomposes the vibration signal onto a series of orthogonal wavelet functions and the number of wavelets on one wavelet level is different from those on the other levels. Since the grids are unevenly distributed on the time-scale map, it is shown that a representation pattern of a vibration component on the map may be significantly altered or even be broken down into pieces when the signal has a shift along the time axis. On contrary, there is no such uneven distribution of grids on the continuous wavelet time-scale map, so that the representation pattern of a vibration signal component will not change its shape when the signal component shifts along the time axis. Therefore, the patterns in the continuous wavelet time-scale map are more easily recognised by human visual inspection or computerised automatic diagnosis systems. Using a Gaussian enveloped oscillation wavelet, the wavelet transform is capable of retaining the frequency meaning used in the spectral analysis, while making the interpretation of patterns on the time-scale maps easier.

scholarly journals A study on Electron Oscillations in the Magnetosheath of Mars with Mars Express observations

2016 ◽  
Vol 12 (S328) ◽  
pp. 230-232
Author(s):  
Adriane M. de Souza ◽  
Ezequiel Echer ◽  
Mauricio J. A. Bolzam ◽  
Markus Fränz
Keyword(s):  
Wavelet Transform ◽  
Electron Density ◽  
Low Frequency ◽  
Morlet Wavelet ◽  
Space Plasmas ◽  
Density Data ◽  
Mars Express ◽  
Preliminary Study ◽  
Morlet Wavelet Transform ◽  
Low Frequency Waves

AbstractWavelet analysis was employed to identify the major frequencies of low-frequency waves present in the Martian magnetosheath. The Morlet wavelet transform was selected and applied to the electron density data, obtained from the Analyzer of Space Plasmas and Energetic Atoms experiment (ASPERA-3), onboard the Mars Express (MEX) spacecraft. We have selected magnetosheath crossings and analyzed electron density data. From a preliminary study of 502 magnetosheath crossings (observed during the year of 2005), we have found 1409 periods between 0.005 and 0.06Hz. The major frequencies observed were in the range 0.005-0.02 Hz with 58.5% of the 1409 frequencies identified.

Bearing Fault Detection Based on Order Tracking and Complex Morlet Wavelet Transform

2011 ◽  
Vol 474-476 ◽  
pp. 639-644 ◽  
Author(s):  
Hui Li
Keyword(s):  
Wavelet Transform ◽  
Morlet Wavelet ◽  
Experimental Result ◽  
Bearing Fault ◽  
Multi Scale ◽  
Order Tracking ◽  
Continuous Complex ◽  
Complex Morlet Wavelet ◽  
Morlet Wavelet Transform ◽  
Envelope Spectrum

A new approach to bearing fault diagnosis under run-up based on order tracking and continuous complex Morlet wavelet transform demodulation technique is presented. The non-stationary vibration signal is first transformed from the time domain transient signal to angle domain stationary one using order tracking technique. Then the continuous complex Morlet wavelet transform is applied to the angle domain re-sampled signal and the complex Morlet wavelet transform based multi-scale envelope spectrum is obtained. The experimental result shows that order tracking and complex Morlet wavelet transform based multi-scale envelope spectrum can effectively diagnosis bearing localized fault.

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