Journal ArticleDOI
Advanced bearing diagnostics: A comparative study of two powerful approaches
TLDR
This paper investigates and compares two emerging approaches to vibration-based fault detection based on a cyclostationary modeling of the bearing signal and addresses the extension of these approaches to the nonstationary operating regime.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2019-01-01. It has received 101 citations till now. The article focuses on the topics: Fault detection and isolation & Cyclostationary process.read more
Citations
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Journal ArticleDOI
An optimal filter length selection method for MED based on autocorrelation energy and genetic algorithms.
TL;DR: The proposed method for exact selecting the optimal filter length of minimal entropy deconvolution to solve it recovering a single random pulse when the filter length is not improper has better performance in detecting the weak fault signal.
Journal ArticleDOI
Reassigned second-order Synchrosqueezing Transform and its application to wind turbine fault diagnosis
TL;DR: The analysis result obtained by comparing different TF methods shows that the method put forward by this paper can effectively extract time-varying fault characteristics under non-stationary condition.
Journal ArticleDOI
Health indicators construction for damage level assessment in bearing diagnostics: A proposal of an energetic approach based on envelope analysis
TL;DR: This study discloses a diagnostic investigation performed both on the vibration signal and on the contact pressure signal that is supposed to be one of main forcing terms in the dynamic equilibrium of the damaged bearing.
Journal ArticleDOI
Fractional frequency band entropy for bearing fault diagnosis under varying speed conditions
TL;DR: Fractional frequency band entropy (FrFBE) is proposed to solve fault diagnosis under varying speed conditions, constructing optimized FrFT filter to extract time-varying fault characteristics.
Journal ArticleDOI
CEEMD-assisted bearing degradation assessment using tight clustering
TL;DR: A tight Gaussian mixture clustering unsupervised learning algorithm is implemented with the assistance of an optimized complementary ensemble empirical mode decomposition (CEEMD) to diagnose the damage severity of rolling element bearings and provides more accurate diagnosis information of the current conditions of bearings.
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.
Journal ArticleDOI
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.
Journal ArticleDOI
Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics
TL;DR: In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter based denoising methods are compared based on signals from mechanical defects, and the comparison result reveals that wavelet filters are more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals, whereas the wavelet transform has a better performance on smooth signal detection.
Journal ArticleDOI
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.
Journal ArticleDOI
The spectral kurtosis: a useful tool for characterising non-stationary signals
TL;DR: A formalisation of the spectral kurtosis by means of the Wold–Cramer decomposition of “conditionally non-stationary” processes is proposed, which engenders many useful properties enjoyed by the SK.
Related Papers (5)
Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study
Wade A. Smith,Robert B. Randall +1 more