Vibration Based Modal Parameters Identification and Wear Fault Diagnosis Using Laplace Wavelet

Ye, Zi; Chen, Xue Feng; He, Zhengjia; Chen, Peng

doi:10.4028/0-87849-976-8.183

Cited by 6 publications

(7 citation statements)

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“…In this case, the threshold is set to 1.7, which is calculated by Eq. (43). The initial frequency parameter f 0 is 3340 Hz, which is the frequency associated with the maximum energy in the spectrum.…”

Section: Case I: Preset Failure Experimentsmentioning

confidence: 99%

“…Matching pursuit [42] is a greedy algorithm that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of function, especially wavelet function. Laplace wavelet correlation filtering (LWCF) [43,44] was proposed based on matching pursuit to identify parameters of transients by calculating the correlation coefficient between the analyzed signal and the Laplace wavelet model of the transient. Reference [45] proposed a novel method as an extension of LWCF, which extends the parametric model of the Laplace wavelet to a parametric wavelet model dictionary that included the parametric models of the Morlet wavelet, the Laplace wavelet and the harmonic wavelet.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Transient signal analysis based on Levenberg–Marquardt method for fault feature extraction of rotating machines

Wang

Cai

Zhu

et al. 2015

Mechanical Systems and Signal Processing

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Section: Case I: Preset Failure Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transient signal analysis based on Levenberg–Marquardt method for fault feature extraction of rotating machines

Wang

Cai

Zhu

et al. 2015

Mechanical Systems and Signal Processing

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“…Such techniques and methods are not covered in this study (the reader is referred to Peng and Chu (2004) for a comprehensive survey). However, some wavelets-based fault detection and health monitoring methods search for changes in systems' parameters and hence perform systems identification (Hou and Hera, 2001a; Staszewski, 1997; Zi et al., 2005). These methods are covered within the systems identification section.…”

Section: Vibrations Analysismentioning

confidence: 99%

Wavelets as a tool for systems analysis and control

Abuhamdia

Taheri

2015

Journal of Vibration and Control

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This survey presents the broad range of research on using wavelets in the analysis and design of dynamic systems. Though wavelets have been used with all types of systems, the major focus of this survey is mechanical and electromechanical systems in addition to their controls. However, the techniques presented can be applied to any category of dynamic systems such as economic, biological, and social systems. Wavelets can be classified into three different types: orthogonal, biorthogonal, and pseudo, all of which are employed in dynamic systems engineering. Wavelets-based methods for dynamic systems applications can be divided into vibrations analysis and systems and control analysis. Wavelets applications in vibrations extend to oscillatory response solutions and vibrations-based systems identification. Also, their applications in systems and control extend to time–frequency representation and modeling, nonlinear systems linearization and model reduction, and control design and control law computation. There are serious efforts within systems and control theory to establish time–frequency and wavelets-based Frequency Response Functions (FRFs) parallel to the Fourier-based FRFs, which will pave the road for time-varying FRFs. Moreover, the natural similarity of wavelets to the representation of neural networks allows them to slip into neural-networks-based and fuzzy-neural-networks-based controllers. Additionally, wavelets have been considered for applications in feedforward and feedback control loops for computation, analysis, and synthesis of control laws.

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“…Correlation filtering, enlightened from matching pursuit, is used based on Laplace wavelet to identify the parameters of impulse response by calculating the maximal correlation value, which is employed by Freudinger et al to identify the modal parameters of a flutter for aerodynamic and structural testing [12]. Similar efforts were made by Zi et al for the identification of the natural frequency of a hydrogenerator shaft and the wear fault diagnosis of the intake valve of an internal combustion engine [13]. Qi et al employed Laplace wavelet correlation filtering together with empirical mode decomposition to identify modal parameters [14].…”

Section: Eurasip Journal On Advances In Signal Processingmentioning

confidence: 99%

Adaptive Parameter Identification Based on Morlet Wavelet and Application in Gearbox Fault Feature Detection

Wang

Zhu

et al. 2010

EURASIP J. Adv. Signal Process.

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Localized defects in rotating mechanical parts tend to result in impulse response in vibration signal, which contain important information about system dynamics being analyzed. Thus, parameter identification of impulse response provides a potential approach for localized fault diagnosis. A method combining the Morlet wavelet and correlation filtering, named Cyclic Morlet Wavelet Correlation Filtering (CMWCF), is proposed for identifying both parameters of impulse response and the cyclic period between adjacent impulses. Simulation study concerning cyclic impulse response signal with different SNR shows that CMWCF is effective in identifying the impulse response parameters and the cyclic period. Applications in parameter identification of gearbox vibration signal for localized fault diagnosis show that CMWCF is effective in identifying the parameters and thus provides a feature detection method for gearbox fault diagnosis.

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Vibration Based Modal Parameters Identification and Wear Fault Diagnosis Using Laplace Wavelet

Cited by 6 publications

References 0 publications

Transient signal analysis based on Levenberg–Marquardt method for fault feature extraction of rotating machines

Transient signal analysis based on Levenberg–Marquardt method for fault feature extraction of rotating machines

Wavelets as a tool for systems analysis and control

Adaptive Parameter Identification Based on Morlet Wavelet and Application in Gearbox Fault Feature Detection

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