Multivariate and multiscale monitoring of large-size low-speed bearings using Ensemble Empirical Mode Decomposition method combined with Principal Component Analysis

Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

doi:10.1016/j.ymssp.2009.09.002

Cited by 108 publications

(61 citation statements)

References 60 publications

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“…For the intelligent fault diagnosis of induction motors, a method combined with ICA and support vector machines (SVM) was applied, and satisfactory results are obtained [37]. The multi-variate and multi-scale monitoring of large-size low-speed bearings using EEMD combined with principal component analysis was executed [38]. The proposed method can accurately identify the local bearing small defects.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of motorized spindle via modified empirical wavelet transform-kernel PCA and optimized support vector machine

Chen¹,

Ye²,

Chen³

et al. 2017

J. vibroeng.

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Abstract. The fault diagnosis of motorized spindle contributes to the improvement of the reliability of computer numerical control machine tools. Presently, numerous mechanical fault diagnosis technologies suffer from the drawbacks of mode mixing, non-adaptive analysis, and low efficiency. Therefore, adopting an effective signal processing method for fault diagnosis of motorized spindle is essential. A method based on modified empirical wavelet transform (EWT) and kernel principal component analysis (Kernel PCA) is proposed. A new method, which determines the proper number of the Fourier spectrum segments, is applied when using EWT. To improve computational efficiency, Kernel PCA is adopted to reduce dimension. The support vector machine optimized by genetic algorithm is introduced to accomplish fault identification. The performance of the proposed method is validated through single and compound fault experiments. Results show that the recognition rate using the proposed method reached 98.8095 % and 98.4375 % in terms of single and compound fault diagnoses, respectively. Moreover, compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), local mean decomposition (LMD) and EWT, the proposed method can save much computing time. The proposed method can be generalized to other mechanical fault diagnoses as well.

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Section: Introductionmentioning

confidence: 99%

Fault diagnosis of motorized spindle via modified empirical wavelet transform-kernel PCA and optimized support vector machine

Chen¹,

Ye²,

Chen³

et al. 2017

J. vibroeng.

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“…Chiementin et al [10] used time domain indicators: RMS, Kurtosis, and proposed to improve the signal-to-noise ratio by applying denoising techniques (wavelet, spectral subtraction, sanc) on experimental acquired AE data. Liao et al [11] used wavelet analysis and Zvokelj et al [12] were interested in the application of empirical mode decomposition (EMD) applied to AE signals. Recently, Kilundu et al [13] applied the cyclostationary on acoustic emission and showed the effectiveness of the spectral correlation and the ICS (integrated correlation spectral) indicator for monitoring bearing defect but with relatively big defect sizes.…”

Section: Introductionmentioning

confidence: 99%

Cyclostationarity applied to acoustic emission and development of a new indicator for monitoring bearing defects

Kedadouche

Thomas

Tahan

2014

Mechanics & Industry

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-The exploitation of cyclostationarity properties of vibratory signals is now more widely used for monitoring rotating machinery and especially for diagnosing bearing defects. The acoustic emission (AE) technology has also emerged as a reliable tool for preventive maintenance of rotating machines. In this study, we propose an experimental study that characterizes the cyclostationary aspect of acoustic emission (AE) signals recorded from a defective bearing (40 µm on the outer race) to see its efficiency to detect a defect at its very early stage of degradation. An industrial sensor (UE10 000) is used. An electrical circuit converts the high frequency signal into an audible signal by heterodyning. The cyclic spectral density, which is a tool dedicated that put into evidence the presence of cyclostationarity, is used for characterizing the cyclostationary. Two new indicators based on this cyclostationary technique are proposed and compared for early detection of defective bearings.

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“…A recent article about slewing bearings as related to vibration analysis is presented by Žvokelj (9,10) . The method used in Refs.…”

Section: Introductionmentioning

confidence: 99%

“…The method used in Refs. (9), (10) is the ensemble empirical mode decomposition (EEMD) combined with multi-scale principal component analysis(MSPCA). An artificial fault is introduced on the inner ring of the slewing bearing as presented in Refs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Condition Monitoring of Low Speed Slewing Bearings Based on Ensemble Empirical Mode Decomposition Method

Caesarendra

Park²,

Kosasih³

et al. 2013

Transactions of the Korean Society for Noise and Vibration Engi

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Vibration condition monitoring of low-speed rotational slewing bearings is essential ever since it became necessary for a proper maintenance schedule that replaces the slewing bearings installed in massive machinery in the steel industry, among other applications. So far, acoustic emission(AE) is still the primary technique used for dealing with low-speed bearing cases. Few studies employed vibration analysis because the signal generated as a result of the impact between the rolling element and the natural defect spots at low rotational speeds is generally weak and sometimes buried in noise and other interference frequencies. In order to increase the impact energy, some researchers generate artificial defects with a predetermined length, width, and depth of crack on the inner or outer race surfaces. Consequently, the fault frequency of a particular fault is easy to identify. This

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Multivariate and multiscale monitoring of large-size low-speed bearings using Ensemble Empirical Mode Decomposition method combined with Principal Component Analysis

Cited by 108 publications

References 60 publications

Fault diagnosis of motorized spindle via modified empirical wavelet transform-kernel PCA and optimized support vector machine

Fault diagnosis of motorized spindle via modified empirical wavelet transform-kernel PCA and optimized support vector machine

Cyclostationarity applied to acoustic emission and development of a new indicator for monitoring bearing defects

Condition Monitoring of Low Speed Slewing Bearings Based on Ensemble Empirical Mode Decomposition Method

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