Use of autocorrelation of wavelet coefficients for fault diagnosis

This paper presents a novel time–frequency-based feature recognition system for gear fault diagnosis using autocorrelation of continuous wavelet coefficients (CWC). Furthermore, it introduces an original mathematical approximation of gearbox vibration signals which approximates sinusoidal components...

Full description

Saved in:
Bibliographic Details
Published in:Mechanical systems and signal processing 2009-07, Vol.23 (5), p.1554-1572
Main Authors: Rafiee, J., Tse, P.W.
Format: Article
Language:English
Subjects:
Applied sciences
Autocorrelation
Condition monitoring
Daubechies
db44
Drives
Exact sciences and technology
Fault detection and diagnosis
Fracture mechanics (crack, fatigue, damage...)
Fundamental areas of phenomenology (including applications)
Gearbox
Gears
Industrial metrology. Testing
Mechanical engineering. Machine design
Mother wavelet
Pattern recognition
Physics
Sinusoidal approximation
Solid mechanics
Structural and continuum mechanics
Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...)
Wavelet
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a novel time–frequency-based feature recognition system for gear fault diagnosis using autocorrelation of continuous wavelet coefficients (CWC). Furthermore, it introduces an original mathematical approximation of gearbox vibration signals which approximates sinusoidal components of noisy vibration signals generated from gearboxes, including incipient and serious gear failures using autocorrelation of CWC. First, the drawbacks of the continuous wavelet transform (CWT) have been eliminated using autocorrelation function. Secondly, the autocorrelation of CWC is introduced as an original pattern for fault identification in machine condition monitoring. Thirdly, a sinusoidal summation function consisting of eight terms was used to approximate the periodic waveforms generated by autocorrelation of CWC for normal gearboxes (NGs) as well as occurrences of incipient and severe gear fault (e.g. slight-worn, medium-worn, and broken-tooth gears). In other words, the size of vibration signals can be reduced with minimal loss of significant frequency content by means of the sinusoidal approximation of generated autocorrelation waveforms of CWC as reported in this paper.
ISSN:0888-3270
1096-1216
DOI:10.1016/j.ymssp.2009.02.008