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Journal ArticleDOI

Gearbox fault detection using Hilbert and wavelet packet transform

Xianfeng Fan, +1 more
- 01 May 2006 - 
- Vol. 20, Iss: 4, pp 966-982
TLDR
In this article, the authors proposed a new fault detection method that combines Hilbert transform and wavelet packet transform for gearbox demodulation, which can extract modulating signal and help to detect the early gear fault.
About
This article is published in Mechanical Systems and Signal Processing.The article was published on 2006-05-01. It has received 308 citations till now. The article focuses on the topics: Wavelet packet decomposition & Second-generation wavelet transform.

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Citations
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Journal ArticleDOI

Condition monitoring and fault diagnosis of planetary gearboxes: A review

TL;DR: This paper aims to review and summarize publications on condition monitoring and fault diagnosis of planetary gearboxes and provide comprehensive references for researchers interested in this topic.
Journal ArticleDOI

Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection

TL;DR: In this paper, a new deconvolution method is presented for the detection of gear and bearing faults from vibration data, which takes advantage of the periodic nature of the faults as well as the impulse-like vibration behaviour associated with most types of faults.
Journal ArticleDOI

Vibration signal models for fault diagnosis of planetary gearboxes

TL;DR: In this paper, the spectral structure of planetary gear system vibration signals is used to diagnose planetary gearboxes with respect to the frequency of local and distributed gear faults. And the theoretical derivations are validated using both experimental and industrial signals.
Journal ArticleDOI

Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

TL;DR: In this article, the inner product operation of wavelet transform (WT) is verified by simulation and field experiments and the development process of WT based on inner product is concluded and the applications of major developments in rotating machinery fault diagnosis are also summarized.
Journal ArticleDOI

Application of mother wavelet functions for automatic gear and bearing fault diagnosis

TL;DR: An automatic feature extraction system for gear and bearing fault diagnosis using wavelet-based signal processing and shows that although Daubechies 44 is the most similar mother wavelet function across the vibration signals, it is not the proper function for all wavelets-based processing.
References
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Journal ArticleDOI

Ideal spatial adaptation by wavelet shrinkage

TL;DR: In this article, the authors developed a spatially adaptive method, RiskShrink, which works by shrinkage of empirical wavelet coefficients, and achieved a performance within a factor log 2 n of the ideal performance of piecewise polynomial and variable-knot spline methods.
Journal ArticleDOI

Wavelet packet feature extraction for vibration monitoring

TL;DR: The wavelet packet transform (WPT) is introduced as an alternative means of extracting time-frequency information from vibration signatures and significantly reduces the long training time that is often associated with the neural network classifier and improves its generalization capability.
Journal ArticleDOI

Application of wavelets to gearbox vibration signals for fault detection

TL;DR: In this paper, the authors used the wavelet transform to represent all possible types of transients in vibration signals generated by faults in a gearbox and demonstrated the application of the suggested wavelet by a simple computer algorithm.
Book

Essential Wavelets for Statistical Applications and Data Analysis

TL;DR: A brief introduction to wavelets can be found in this article, where basic smoothing techniques elementary statistical applications wavelet features and examples wavelet-based diagnostics some practical issues other applications data adaptive wavelet thresholding generalizations and extensions.
Journal ArticleDOI

Detecting Fatigue Cracks in Gears by Amplitude and Phase Demodulation of the Meshing Vibration

TL;DR: Selection des fissures de fatigue dans les engrenages by demodulation de l'amplitude and de la phase des vibrations au cours de lengrenement as discussed by the authors.
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