Journal ArticleDOI
Wavelets for fault diagnosis of rotary machines: A review with applications
TLDR
Current applications of wavelets in rotary machine fault diagnosis are summarized and some new research trends, including wavelet finite element method, dual-tree complex wavelet transform, wavelet function selection, newWavelet function design, and multi-wavelets that advance the development of wavelet-based fault diagnosed are discussed.About:
This article is published in Signal Processing.The article was published on 2014-03-01. It has received 1087 citations till now. The article focuses on the topics: Wavelet packet decomposition & Discrete wavelet transform.read more
Citations
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Journal ArticleDOI
Artificial intelligence for fault diagnosis of rotating machinery: A review
TL;DR: This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications.
Journal ArticleDOI
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
TL;DR: A fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body is proposed.
Journal ArticleDOI
Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis
TL;DR: A novel hierarchical learning rate adaptive deep convolution neural network based on an improved algorithm that is well suited to the fault-diagnosis model and superior to other existing methods is proposed.
Journal ArticleDOI
Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification
TL;DR: An effective and reliable deep learning method known as stacked denoising autoencoder (SDA), which is shown to be suitable for certain health state identifications for signals containing ambient noise and working condition fluctuations, is investigated.
Journal ArticleDOI
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
TL;DR: Experimental results and comprehensive comparison analysis have demonstrated the superiority of the proposed MSCNN approach, thus providing an end-to-end learning-based fault diagnosis system for WT gearbox without additional signal processing and diagnostic expertise.
References
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Journal ArticleDOI
A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Journal ArticleDOI
Decomposition of Hardy functions into square integrable wavelets of constant shape
A. Grossmann,J. Morlet +1 more
TL;DR: In this article, the authors studied square integrable coefficients of an irreducible representation of the non-unimodular $ax + b$-group and obtained explicit expressions in the case of a particular analyzing family that plays a role analogous to coherent states (Gabor wavelets) in the usual $L_2 $ -theory.
Journal ArticleDOI
Entropy-based algorithms for best basis selection
TL;DR: Adapted waveform analysis uses a library of orthonormal bases and an efficiency functional to match a basis to a given signal or family of signals, and relies heavily on the remarkable orthogonality properties of the new libraries.
Journal ArticleDOI
Wavelets and signal processing
Olivier Rioul,Martin Vetterli +1 more
TL;DR: A simple, nonrigorous, synthetic view of wavelet theory is presented for both review and tutorial purposes, which includes nonstationary signal analysis, scale versus frequency,Wavelet analysis and synthesis, scalograms, wavelet frames and orthonormal bases, the discrete-time case, and applications of wavelets in signal processing.
Journal ArticleDOI
The dual-tree complex wavelet transform
TL;DR: Several methods for filter design are described for dual-tree CWT that demonstrates with relatively short filters, an effective invertible approximately analytic wavelet transform can indeed be implemented using the dual- tree approach.