Bearing Fault Detection Using Hilbert and High Frequency Resolution Techniques

Agrawal, Sanjay; Mohanty, Soumya R.; Agarwal, Vineeta

doi:10.1080/03772063.2015.1009398

Cited by 17 publications

(6 citation statements)

References 22 publications

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“…However, the background noise makes it difficult to identify valid frequency component. To weaken the noise level and strengthen the signal to noise ratio, researchers have adopted some new approaches, like amplitude spectrum, power spectrum, cepstrum, and Hilbert demodulation [10][11][12][13], for bearing detection. However, the accuracy of these methods highly depends on the bearing dimensions and rotational speed [6].…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive FuzzyC-Means Clustering

et al. 2016

Shock and Vibration

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Vibration signal analysis is one of the most effective methods for mechanical fault diagnosis. Available part of the information is always concealed in component noise, which makes it much more difficult to detect the defection, especially at early stage of the development. This paper presents a new approach for mechanical fault diagnosis based on time domain analysis and adaptive fuzzyC-means clustering. By analyzing vibration signal collected, nine common time domain parameters are calculated. This lot of data constitutes data matrix as characteristic vectors to be detected. And using adaptive fuzzyC-means clustering, the optimal clustering number can be gotten then to recognize different fault types. Moreover, five parameters, including variance, RMS, kurtosis, skewness, and crest factor, of the nine are selected as the new eigenvector matrix to be clustered for more optimal clustering performance. The test results demonstrate that the proposed approach has a sensitive reflection towards fault identifications, including slight fault.

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Section: Introductionmentioning

confidence: 99%

Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive FuzzyC-Means Clustering

et al. 2016

Shock and Vibration

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“…The ESPRIT technique performs exploitation of subspace signals and estimates frequency present in sinusoids using rational variance in the staggered subspaces (Agrawal et al , 2015). The equation (37) and equation (38) defines the subspace matrices (M sub ).…”

Section: Proposed Methodologymentioning

confidence: 99%

Steel truss bridge vibration-based condition monitoring using Savitzky-Golay filter, Hilbert transform, MUSIC and ESPRIT

Sharma

Kumar

Vinayak

et al. 2021

JEDT

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Purpose This study aims to perform the experimental work on a laboratory-constructed steel truss bridge model on which hammer blows are applied for excitation. The vibration response signals of the bridge structure are collected using sensors placed at different nodes. The different damaged states such as no damage, single damage, double damage and triple damage are introduced by cutting members of the bridge. The masked noise with recorded vibration responses generates challenge to properly analyze the health of bridge structure. Design/methodology/approach The analytical modal properties are obtained from finite element model (FEM) developed using SAP2000 software. The response signals are analyzed in frequency domain by power spectrum and in time-frequency domain using spectrogram and Stockwell transform. Various low pass signal-filtering techniques such as variational filter, lowpass sparse banded (AB) filter and Savitzky–Golay (SG) differentiator filter are also applied to refine vibration signals. The proposed methodology further comprises application of Hilbert transform in combination with MUSIC and ESPRIT techniques. Findings The outcomes of SG filter provided the denoised signals using appropriate polynomial degree with proper selected window length. However, certain unwanted frequency peaks still appeared in the outcomes of SG filter. The SG-filtered signals are further analyzed using fused methodology of Hilbert transform-ESPRIT, which shows high accuracy in identifying modal frequencies at different states of the steel truss bridge. Originality/value The sequence of proposed methodology for denoising vibration response signals using SG filter with Hilbert transform-ESPRIT is a novel approach. The outcomes of proposed methodology are much refined and take less computational time.

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“…To ensure that there exists partial flexibility in the joints at these locations further denoising of the vibration signals is required. There are various studies showing a broader range of areas of application of Hilbert transform on noisy vibration signals for obtaining better visualization of hidden frequencies (Kunwar et al , 2013; Sabbaghian-Bidgoli and Poshtan, 2018; Liu et al , 2014; Agrawal et al , 2015; Chiang et al , 2010; Hsu et al , 2013; Feng et al , 2015; Yang et al , 2004). In the present study, the efficiency of fused Hilbert transform with spectral kurtosis and bandpass filter in obtaining the modal frequency of a steel truss bridge is shown.…”

Section: Diagnostic Techniquesmentioning

confidence: 99%

Hilbert transform and spectral kurtosis based approach in identifying the health state of retrofitted old steel truss bridge

Sharma

Kumar

Vinayak

et al. 2021

WJE

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Purpose This study aims to include the diagnosis of an old concrete deck steel truss rural road bridge in the damaged and retrofitted state through vibration response signals. Design/methodology/approach The analysis of the vibration response signals is performed in time and time-frequency domains using statistical features-root mean square, impulse factor, crest factor, kurtosis, peak2peak and Stockwell transform. The proposed methodology uses the Hilbert transform in combination with spectral kurtosis and bandpass filtering technique for obtaining robust outcomes of modal frequencies. Findings The absence or low amplitude of considered mode shape frequencies is observed both before and after retrofitting of bridge indicates the deficient nodes. The kurtosis feature among all statistical approaches is able to reflect significant variation in the amplitude of different nodes of the bridge. The Stockwell transform showed better resolution of present modal frequencies but due to the yield of additional frequency peaks in the vicinity of the first three analytical modal frequencies no decisive conclusions are achieved. The methodology shows promising outcomes in eliminating noise and visualizing distinct modal frequencies of a steel truss bridge. Social implications The findings of the present study help in analyzing noisy vibration signals obtained from various structures (civil or mechanical) and determine vulnerable locations of the structure using mode shape frequencies. Originality/value The literature review gave an insight into few experimental investigations related to the combined application of Hilbert transform with spectral kurtosis and bandpass filtering technique in determining mode frequencies of a steel truss bridge.

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Bearing Fault Detection Using Hilbert and High Frequency Resolution Techniques

Cited by 17 publications

References 22 publications

Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive FuzzyC-Means Clustering

Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive FuzzyC-Means Clustering

Steel truss bridge vibration-based condition monitoring using Savitzky-Golay filter, Hilbert transform, MUSIC and ESPRIT

Hilbert transform and spectral kurtosis based approach in identifying the health state of retrofitted old steel truss bridge

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