Journal ArticleDOI
Hybrid time–frequency methods for non-stationary mechanical signal analysis
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TLDR
In this paper, a family of hybrid time-frequency methods for transient signal analysis is presented, which are based on autoregressive models of the signal, and on the maximum likelihood method.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2004-09-01. It has received 39 citations till now. The article focuses on the topics: Bilinear time–frequency distribution & Time–frequency representation.read more
Citations
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Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples
TL;DR: A systematic review of over 20 major time-frequency analysis methods reported in more than 100 representative articles published since 1990 can be found in this article, where their fundamental principles, advantages and disadvantages, and applications to fault diagnosis of machinery have been examined.
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Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution
Baoping Tang,Wenyi Liu,Tao Song +2 more
TL;DR: In this paper, the authors used the continuous wavelet transformation (CWT) to filter useless noise in raw vibration signals, and auto terms window (ATW) function is used to suppress the cross terms in WVD, which can not only remove cross terms faraway from the auto terms, but also keep high energy close to every instantaneous frequency.
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A generalized synchrosqueezing transform for enhancing signal time-frequency representation
Chuan Li,Ming Liang +1 more
TL;DR: A generalized synchrosqueezing transform (GST) approach to deal with the diffusions in both time and frequency dimensions is proposed for signal TFR enhancement and it is shown that the wavelet diffusion only occurs at frequency dimension.
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Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses
TL;DR: In this paper, signal processing and pattern recognition techniques are combined to diagnose the severity of bearing faults, and four different levels of bearing fault severities together with a standard no-fault class were studied and compared.
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Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM
TL;DR: A wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree Support Vector Machines (SVM) can effectively extract features from nonstationary signals, and can obtain excellent results despite of less training samples.
References
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Journal ArticleDOI
On the use of windows for harmonic analysis with the discrete Fourier transform
TL;DR: A comprehensive catalog of data windows along with their significant performance parameters from which the different windows can be compared is included, and an example demonstrates the use and value of windows to resolve closely spaced harmonic signals characterized by large differences in amplitude.
Book
Digital Signal Processing: Principles, Algorithms, and Applications
TL;DR: This paper presents a meta-analysis of the Z-Transform and its application to the Analysis of LTI Systems, and its properties and applications, as well as some of the algorithms used in this analysis.
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Time-frequency distributions-a review
TL;DR: A review and tutorial of the fundamental ideas and methods of joint time-frequency distributions is presented with emphasis on the diversity of concepts and motivations that have gone into the formation of the field.
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Time-dependent ARMA modeling of nonstationary signals
TL;DR: The Prony-Pisarenko estimator is adapted to this nonstationary context, the signal considered in this case being the output of a zero-input time-varying system corrupted by an additive white noise.
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Time–frequency analysis in gearbox fault detection using the wigner–ville distribution and pattern recognition
TL;DR: Two pattern recognition procedures are applied to the detection of a broken tooth in a spur gear to detect tooth faults reliably based on statistical and neural pattern recognition.