Demodulation of Vibration Signals Generated by Defects in Rolling Element Bearings Using Complex Shifted Morlet Wavelets

Nikolaou, Nikolaos; Antoniadis, Ioannis

doi:10.1006/mssp.2001.1459

Cited by 221 publications

(120 citation statements)

References 12 publications

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“…A larger value of CFIC implies a better fault detection performance. Other statistic criterion, such as kurtosis [21], smoothness index [22], crest factor [23], peak energy [24] and fusion criterion of kurtosis, smoothness index and crest factor [25], can also be used as a guide to select the fault relevant information. In this study, CFIC is employed.…”

Section: The Selection Of the Threshold Tmentioning

confidence: 99%

Detection of Bearing Faults Using a Novel Adaptive Morphological Update Lifting Wavelet

Zuo

Feng

et al. 2017

Chin. J. Mech. Eng.

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The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper presents a novel signal processing scheme, adaptive morphological update lifting wavelet (AMULW), for rolling element bearing fault detection. In contrast with the widely used morphological wavelet, the filters in AMULW are no longer fixed. Instead, the AMULW adaptively uses a morphological dilation-erosion filter or an average filter as the update lifting filter to modify the approximation signal. Moreover, the nonlinear morphological filter is utilized to substitute the traditional linear filter in AMULW. The effectiveness of the proposed AMULW is evaluated using a simulated vibration signal and experimental vibration signals collected from a bearing test rig. Results show that the proposed method has a superior performance in extracting fault features of defective rolling element bearings.

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Section: The Selection Of the Threshold Tmentioning

confidence: 99%

Detection of Bearing Faults Using a Novel Adaptive Morphological Update Lifting Wavelet

Zuo

Feng

et al. 2017

Chin. J. Mech. Eng.

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“…The characteristic value selected should be able to reflect the complexity degree of the signals since the complexity refers to signal complexity. Therefore, the commonly used indices are entropy and kurtosis [7][8][9][10][17][18][19].…”

Section: Selection Of Optimal Area For Analysismentioning

confidence: 99%

“…The initial values of and in the experiment are set as 0.1. A group of overall Lempel-Ziv complexity values is calculated by (17) and (18). Then the overall Lempel-Ziv complexity value is obtained repeatedly by varying the values of and at the step length of 0.1.…”

Section: The Selection Of the Value Of The Coefficientsmentioning

confidence: 99%

Improved Complexity Based on Time-Frequency Analysis in Bearing Quantitative Diagnosis

Wang

Cui

Wang

et al. 2013

Advances in Mechanical Engineering

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This paper compares and analyzes the quantitative diagnosis methods based on Lempel-Ziv complexity for bearing fault, using continuous wavelet transform (CWT), Empirical Mode Decomposition (EMD) method, and wavelet packet method for decomposition of vibration signal measured by acceleration sensors, respectively. The kurtosis and entropy indices are also analyzed in order to select the optimum analysis area from the vibration signal. The variation trend of vibration signal Lempel-Ziv complexity of bearing inner race and outer race with the varying fault severity is analyzed and predicted based on the generation mechanism and characteristics of fault vibration signals. Experimental results show that it is suitable for observing the fault growing trend and severity by examining the value based on improved Lempel-Ziv complexity algorithm using continuous wavelet transform (CWT), Empirical Mode Decomposition (EMD) method, and wavelet packet method for signal decomposition, using kurtosis indices for optimal analysis area selection, and using the optimal weight coefficients for complexity calculation.

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“…They used the hybrid method to determine the defect in the early stages of the creation of these two methods simultaneously and they were sure of effectively of their own method. Nicolao and Anthonyadis [6] found a method for demodulation of the signal of a defective roller bearing or using a Morellet wavelet transform whose coefficients were determined by the minimum entropy method to obtain the optimal envelope coefficient. For these optimal coefficients to determine the Morelt wavelet transform, Qui et al [7] used the minimal Shannon entropy.…”

Section: Introductionmentioning

confidence: 99%

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2017

IJMECH

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Today the maintenance and repair based on condition monitoring techniques of rotating equipment is one of the most important tools to prevent stopping of production and reduce the cost of maintenance and repair and the vibration analysis according to the width of identifiable defects scope has a specific importance. In this paper, we discuss about rolling bearings defects detection with using of normalized Hilbert huang approach.

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Demodulation of Vibration Signals Generated by Defects in Rolling Element Bearings Using Complex Shifted Morlet Wavelets

Cited by 221 publications

References 12 publications

Detection of Bearing Faults Using a Novel Adaptive Morphological Update Lifting Wavelet

Detection of Bearing Faults Using a Novel Adaptive Morphological Update Lifting Wavelet

Improved Complexity Based on Time-Frequency Analysis in Bearing Quantitative Diagnosis

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