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
Fault Diagnosis of Rotating Machinery Based on Combination of Deep Belief Network and One-dimensional Convolutional Neural Network
TL;DR: The results show that the proposed precise diagnosis method based on the combination of DBN and 1D-CNN has higher efficiency and accuracy than the state-of-the art fault diagnosis methods.
Journal ArticleDOI
Compound Bearing Fault Detection Under Varying Speed Conditions With Virtual Multichannel Signals in Angle Domain
TL;DR: A novel compound fault detection method with virtual multichannel signals in the angle domain with computed order tracking to eliminate speed fluctuations is proposed to solve problems for rolling bearing monitoring under varying speed conditions.
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SAND: streaming subsequence anomaly detection
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Maximum average kurtosis deconvolution and its application for the impulsive fault feature enhancement of rotating machinery
TL;DR: The maximization of a new index named average kurtosis (AK) is treated as the objective function in this paper for deconvolution, which contributes to improving both the efficiency and performance of MAKD.
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Novel method of informative frequency band selection for vibration signal using Nonnegative Matrix Factorization of spectrogram matrix
TL;DR: A novel method of informative frequency band selection is proposed that utilizes the approach of Non-negative Matrix Factorization applied to time-frequency signal representation to filter particular components out of the original signal.
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.
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.
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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.
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.
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Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study
Wade A. Smith,Robert B. Randall +1 more