Envelope analysis of rotating machine vibrations in variable speed conditions: A comprehensive treatment
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TLDR
A model of rotating machine signals is introduced which sheds light on the various components to be expected in the squared envelope spectrum, and a critical comparison is made of three sophisticated methods, namely, the improved synchronous average, the cepstrum prewhitening, and the generalized synchronousaverage, used for suppressing the deterministic part.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2017-02-01 and is currently open access. It has received 125 citations till now. The article focuses on the topics: Cepstrum & Cyclostationary process.read more
Citations
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Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review
TL;DR: A systemic and pertinent state-of-art review on WT planetary gearbox condition monitoring techniques on the topics of fundamental analysis, signal processing, feature extraction, and fault detection is provided.
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A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery
Ming Zhao,Ming Zhao,Xiaodong Jia +2 more
TL;DR: In this paper, a reweighted singular value decomposition (RSVD) strategy is proposed for signal denoising and weak feature enhancement in a two-stage gearbox as well as train bearings.
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Matching synchrosqueezing transform: A useful tool for characterizing signals with fast varying instantaneous frequency and application to machine fault diagnosis
TL;DR: In this paper, a matching synchrosqueezing transform (MSST) was proposed to improve the readability of the TF representation of nonstationary signals composed of multiple components with slow varying instantaneous frequency (IF).
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A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis
TL;DR: A two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type and demonstrates that it outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).
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An underdamped stochastic resonance method with stable-state matching for incipient fault diagnosis of rolling element bearings
TL;DR: Wang et al. as discussed by the authors proposed an underdamped multistable stochastic resonance (SR) method with stable-state matching for bearing fault diagnosis, which is able to suppress the multiscale noise.
References
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Journal ArticleDOI
Rolling element bearing diagnostics—A tutorial
Robert B. Randall,Jérôme Antoni +1 more
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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Fast computation of the kurtogram for the detection of transient faults
TL;DR: This communication describes a fast algorithm for computing the kurtogram over a grid that finely samples the ( f, Δ f ) plane and the efficiency of the algorithm is illustrated on several industrial cases concerned with the detection of incipient transient faults.
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The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines
Jérôme Antoni,Robert B. Randall +1 more
TL;DR: In this article, the spectral kurtosis (SK) was used to detect and characterize nonstationary signals in the presence of strong masking noise and to detect incipient faults in rotating machines.
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The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals
TL;DR: In this article, it was shown that the spectral correlation can be interpreted as a Fourier transform of the average squared envelope of the signal, which is much easier to obtain directly.
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The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines
Jérôme Antoni,Robert B. Randall +1 more