Bearing damage assessment using Jensen-Rényi Divergence based on EEMD

Singh, Jaskaran; Darpe, Ashish K.; Singh, Shivendra

doi:10.1016/j.ymssp.2016.10.028

Cited by 110 publications

(55 citation statements)

References 48 publications

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“…The test device is mainly composed of the AC motor, the drive shaft and the four rolling bearings on the shaft. Eight acceleration and four thermocouples sensors were placed on the respective rolling bearings for vibration signal and temperature signal monitoring, and 6000 pounds of radial load was added to bearings 2 and 3 [4,5]. The rolling bearings used in the experiment were all Rexnord ZA-2115 double row bearings.…”

Section: Rolling Element Bearing Experimentsmentioning

confidence: 99%

Rolling element bearings health status indictor analysis

Zhou¹,

Hu²

2018

Vib. proced.

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According to the vibration mechanism of ship gas turbine rolling element bearings common failure modes, the variation of the common indicators during the rolling element bearings health status degradation process is analyzed, and the reflection ability of the various indicators is studied based on the consistency and sensitivity. The results show that the Root-Mean-Square value, Peak-Peak value, Wavelet Energy Spectrum Entropy and Singular Spectrum Entropy can effectively reflect the health state change of rolling element bearings.

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Section: Rolling Element Bearing Experimentsmentioning

confidence: 99%

Rolling element bearings health status indictor analysis

Zhou¹,

Hu²

2018

Vib. proced.

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“…A variety of methods to decompose the fault signal for extracting the weak periodic impulse feature were proposed such as EEMD [12,13], LMD [14], intrinsic characteristic-scale decomposition (ICD) [29]. However, how to choose the sensitive feature components remains a problem to be solved.…”

Section: Autocorrelation Function Impulse Harmonic To Noise Ratio Indexmentioning

confidence: 99%

“…However, how to choose the sensitive feature components remains a problem to be solved. Many scholars have proposed a series of indexes such as kurtosis [22], approximate entropy [30], Pearson coefficient [13], or a fusion of these parameters [31] to measure the richness of fault feature information in the decomposed components. However, most of these indicators were originally developed in the field of statistics or information theory, thus they mainly focus on the general statistical distribution of a signal, such as its non-Gaussianity and peakedness, but may ignore the specific characteristics of mechanical signals [15].…”

Section: Autocorrelation Function Impulse Harmonic To Noise Ratio Indexmentioning

confidence: 99%

Incipient Fault Feature Extraction of Rolling Bearings Using Autocorrelation Function Impulse Harmonic to Noise Ratio Index Based SVD and Teager Energy Operator

Zheng

Zhang

et al. 2017

Applied Sciences

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Abstract:The periodic impulse feature is the most typical fault signature of the vibration signal from fault rolling element bearings (REBs). However, it is easily contaminated by noise and interference harmonics. In order to extract the incipient impulse feature from the fault vibration signal, this paper presented an autocorrelation function periodic impulse harmonic to noise ratio (ACFHNR) index based on the SVD-Teager energy operator (TEO) method. Firstly, the Hankel matrix is constructed based on the raw vibration fault signal of rolling bearing, and the SVD method is used to obtain the singular components. Afterwards, the ACFHNR index is employed to measure the abundance of the periodic impulse fault feature for the singular components, and the component with the largest ACFHNR index value is extracted. Moreover, the properties of the ACFHNR index are demonstrated by simulations and the full life cycle of the experiment, showing its superiority over the traditional kurtosis and root mean square (RMS) index for extracting and detecting incipient periodic impulse features. Finally, the Teager energy operator spectrum of the extracted informative signal is gained. The simulation and experimental results indicated that the proposed ACFHNR index based method can effectively detect the incipient fault feature of the rolling bearing, and it shows better performance than the kurtosis and RMS index based methods.Keywords: rolling element bearings (REBs); singular value decomposition (SVD); autocorrelation function impulse harmonic to noise ratio (ACFHNR); teager energy operator (TEO)

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“…Then EMD low-pass filter [5,6], db3 wavelet decomposition [10], and NIO EEMD are used, respectively, for noise reduction of one NO X mixed transmission spectrum (NO 2 -300 ppm/NO-786 ppm/100 cm) to further verify the Journal of Spectroscopy denoising effect of NIO EEMD method. As shown in Figure 5(b), EMD low-pass filtering method has been used for one decomposition, and then the first order IMF component has been removed, finally there still exists a lot of noise in the denoised spectrum, and the three NO absorption peaks in 200-230 nm band have a large deformation.…”

Section: Denoising Experiments Based On Eemd and Datamentioning

confidence: 99%

“…[2][3][4], with which one signal can be decomposed into a finite number of intrinsic mode functions (IMF). IMF can meet the global narrowband requirement and local zero-mean condition, but there exists mode mixing problems when it is used to decompose the signal [5,6]. Therefore, the ensemble empirical mode decomposition (EEMD) method is proposed by Huang et al [7], in which the Gauss white noise is added into the signal to be processed so as to provide enough extreme points for smoothing abnormal events, and then the IMF components obtained by multiple decompositions are overall averaged to overcome the mode mixing.…”

Section: Introductionmentioning

confidence: 99%

Study on the Noise Reduction of Vehicle Exhaust NO_X Spectra Based on Adaptive EEMD Algorithm

Zhang

You

et al. 2017

Journal of Spectroscopy

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It becomes a key technology to measure the concentration of the vehicle exhaust components with the transmission spectra. But in the conventional methods for noise reduction and baseline correction, such as wavelet transform, derivative, interpolation, polynomial fitting, and so forth, the basic functions of these algorithms, the number of decomposition layers, and the way to reconstruct the signal have to be adjusted according to the characteristics of different components in the transmission spectra. The parameter settings of the algorithms above are not transcendental, so with them, it is difficult to achieve the best noise reduction effect for the vehicle exhaust spectra which are sharp and drastic in the waveform. In this paper, an adaptive ensemble empirical mode decomposition (EEMD) denoising model based on a special normalized index optimization is proposed and used in the spectral noise reduction of vehicle exhaust NO X . It is shown with the experimental results that the method can effectively improve the accuracy of the spectral noise reduction and simplify the denoising process and its operation difficulty.

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Bearing damage assessment using Jensen-Rényi Divergence based on EEMD

Cited by 110 publications

References 48 publications

Rolling element bearings health status indictor analysis

Rolling element bearings health status indictor analysis

Incipient Fault Feature Extraction of Rolling Bearings Using Autocorrelation Function Impulse Harmonic to Noise Ratio Index Based SVD and Teager Energy Operator

Study on the Noise Reduction of Vehicle Exhaust NO_X Spectra Based on Adaptive EEMD Algorithm

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Bearing damage assessment using Jensen-Rényi Divergence based on EEMD

Cited by 110 publications

References 48 publications

Rolling element bearings health status indictor analysis

Rolling element bearings health status indictor analysis

Incipient Fault Feature Extraction of Rolling Bearings Using Autocorrelation Function Impulse Harmonic to Noise Ratio Index Based SVD and Teager Energy Operator

Study on the Noise Reduction of Vehicle Exhaust NOX Spectra Based on Adaptive EEMD Algorithm

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Study on the Noise Reduction of Vehicle Exhaust NO_X Spectra Based on Adaptive EEMD Algorithm