Improvement of the EMD method and applications in defect diagnosis of ball bearings

Du, Qiuhua; Yang, Shaorui

doi:10.1117/12.716691

Cited by 5 publications

(5 citation statements)

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“…Specifically, where F[·] denotes the FT. In implementation, the spectra obtained by equation (13) from P segments ofmeasured signals ( P = 5 in this case) should be normalized and averaged to reduce the effects of random noise, where f u is the observation upper-bound frequency that should be larger than the maximum bearing characteristic frequency (Du and Yang, 2006; McFadden and Smith, 1984), and f u = 300 Hz in this case. Bearing health conditions are estimated by analyzing the related characteristic frequency components (i.e., f id , f od , and f ed ) in the resulting spectra.…”

Section: The Wavelet Cross-spectrum (Wcs) Techniquementioning

confidence: 99%

Bearing system health condition monitoring using a wavelet cross-spectrum analysis technique

Liu

Wang

2011

Journal of Vibration and Control

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Rolling-element bearings are widely used in rotary machinery systems. Accordingly, a reliable bearing fault detection technique is critically needed in industries to prevent the machinery system's performance degradation, malfunction, or even catastrophic failures. Bearing fault detection, however, still remains a very challenging task because most of the bearing fault related signatures are non-stationary. In this paper, a wavelet cross-spectrum (WCS) technique is proposed to tackle the challenge of feature extraction from these non-stationary signatures for bearing fault detection. The vibration signals are first analyzed by a wavelet transform to demodulate primary representative features; the periodic features are then enhanced by cross-correlating the resulting wavelet coefficient functions over several contributive neighboring wavelet bands. A Jarque-Bera statistic index is suggested for the bandwidth selection. The effectiveness ofthe proposed technique is examined by a series of experimental tests corresponding to different bearing conditions. Test results show that the developed WCS technique is an effective signal processing approach for not only stationary but also non-stationary feature extraction and analysis, and it can be applied effectively for bearing fault detection.

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Section: The Wavelet Cross-spectrum (Wcs) Techniquementioning

confidence: 99%

Bearing system health condition monitoring using a wavelet cross-spectrum analysis technique

Liu

Wang

2011

Journal of Vibration and Control

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“…The analytical background of the HHT is introduced on the basis of a synthetic analytical signal, experimentally evaluated using vibration signals measured on a test bearing. Du and Yang [12] presented an improved arithmetic for local mean as obtained by connecting all window means of the signals between two successive extrema with the cubic spline interpolation. The arithmetic is more time-saving than that used in the conventional empirical mode decomposition (EMD) method.…”

Section: Introductionmentioning

confidence: 99%

Innovative method of empirical mode decomposition as spatial tool for structural damage identification

Reddy

Krishna

Sathesa³

2014

Struct. Control Health Monit.

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Summary A new approach to sensitize detecting structural damage using empirical mode decomposition (EMD) of the spatial signals is proposed. The difference of undamaged and damaged spatial strain and strain energy data for the beam and bridge modes is processed through the EMD, which yields the respective IMF1, which shows that damage location can be identified distinctively for as low as 0.01% reduction in elemental stiffness of beam discretized with 30 elements. An analysis was also carried out for a two‐dimensional bridge model (I‐40). It can detect damage sensitivity up to 0.5% loss in stiffness of the bridge. EMD as a spatial analysis tool seems to be the most sensitive among all methods currently available. Copyright © 2014 John Wiley & Sons, Ltd.

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“…This technique allows for decomposing any complex signal into a collection of intrinsic mode functions (I.M.Fs). E.M.D has attracted the attention of researchers and has been widely used in the fields of monitoring and fault detections of machinery [10][11][12]. Another new adaptive analysis technique called local mean decomposition (L.M.D) was initially developed by Smith [13].…”

Section: Introductionmentioning

confidence: 99%

Comparison between the efficiency of L.M.D and E.M.D algorithms for early detection of gear defects

Kidar

Thomas

Guilbault

et al. 2013

Mechanics & Industry

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In recent years, the adaptive decomposition methods have become the center of interest of researchers in many fields and especially in the vibration diagnosis of rotating machines. This paper compares the sensitivity of defect detection of two adaptive methods: local mean decomposition (L.M.D) and empirical mode decomposition (E.M.D). The efficiency of L.M.D and E.M.D methods for detecting defects is investigated for two cases, one from numerical signals and the other from signals recorded during a fatigue test on gear bench. The time descriptors Kurtosis, Thalaf and Thikat are applied on the signal and on its Hilbert transform. The results reveal that both techniques seem to be suitable and have good efficiency for the fault detection. From experimental signals, the comparative results reveal that both methods allow for monitoring wear, but that L.M.D is more sensitive to detect rapid changes of degradation than E.M.D method for the considered case. Consequently these features have potential to become powerful tools for the monitoring of rotating machinery.

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Improvement of the EMD method and applications in defect diagnosis of ball bearings

Cited by 5 publications

References 0 publications

Bearing system health condition monitoring using a wavelet cross-spectrum analysis technique

Bearing system health condition monitoring using a wavelet cross-spectrum analysis technique

Innovative method of empirical mode decomposition as spatial tool for structural damage identification

Comparison between the efficiency of L.M.D and E.M.D algorithms for early detection of gear defects

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