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Journal ArticleDOI

Sparse representation based on local time–frequency template matching for bearing transient fault feature extraction

TLDR
In this article, an iterative transient feature extraction approach is proposed based on time-frequency domain sparse representation, where the TF atoms are constructed based on the TF distribution (TFD) of the Morlet wavelet bases and local TF templates are formulated from TF atoms for the matching process.
About
This article is published in Journal of Sound and Vibration.The article was published on 2016-05-26. It has received 61 citations till now. The article focuses on the topics: Template matching & Time–frequency analysis.

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Citations
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Fault diagnosis of rolling element bearings using basis pursuit

TL;DR: In this paper, a new time-frequency technique, known as basis pursuit, was developed for detecting inner race and outer race faults in a rolling bearing with inner and outer races.
Journal ArticleDOI

Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks

TL;DR: A novel application of 1D Convolutional Neural Networks (1D CNNs) on WSNs for SDD purposes and the method operates directly on the raw ambient vibration condition signals without any filtering or preprocessing, requiring minimal computational time and power.
Journal ArticleDOI

An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder

TL;DR: A method based on stacked CAE for automatic robust features extraction and fault diagnosis of rotating machinery is proposed and results show that the diagnosis accuracies of the proposed method are higher than those of the stacked autoencoder (AE) network under each SNR.
Journal ArticleDOI

A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural network.

TL;DR: A new intelligent fault diagnosis framework inspired by the infinitesimal method is proposed that has higher accuracy with simpler structure, and is superior to the traditional method in bearing fault diagnosis.
Journal ArticleDOI

Basic research on machinery fault diagnostics: Past, present, and future trends

TL;DR: The recent R&D trends in the basic research field of machinery fault diagnosis is reviewed in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics.
References
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Book

A wavelet tour of signal processing

TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Journal ArticleDOI

A review on machinery diagnostics and prognostics implementing condition-based maintenance

TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.
Journal ArticleDOI

Sparse Approximate Solutions to Linear Systems

TL;DR: It is shown that the problem is NP-hard, but that the well-known greedy heuristic is good in that it computes a solution with at most at most $\left\lceil 18 \mbox{ Opt} ({\bf \epsilon}/2) \|{\bf A}^+\|^2_2 \ln(\|b\|_2/{\bf
Book

Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing

Michael Elad
TL;DR: This textbook introduces sparse and redundant representations with a focus on applications in signal and image processing and how to use the proper model for tasks such as denoising, restoration, separation, interpolation and extrapolation, compression, sampling, analysis and synthesis, detection, recognition, and more.
Journal ArticleDOI

Rolling element bearing diagnostics—A tutorial

TL;DR: This tutorial is intended to guide the reader in the diagnostic analysis of acceleration signals from rolling element bearings, in particular in the presence of strong masking signals from other machine components such as gears.
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