A hybrid method combining Teager Kaiser energy operator, empirical mode decomposition and minimum entropy deconvolution for monitoring gears damages

Kedadouche, Mourad; Thomas, Marc; Tahan, Antoine

doi:10.1051/meca/2015030

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…The Teager energy operator (TEO) is a nonlinear operator proposed by Kaiser that can effectively extract the energy of the signal, which can effectively highlight the transient characteristics of the impact component [44]. The Teager energy operator is used to track the instantaneous energy of signal y(t), which is defined as follows [45]:…”

Section: The Index Teager Envelope Spectral Kurtosis (Tek)mentioning

confidence: 99%

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

Cui

Yang

et al. 2019

Applied Sciences

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Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and achallenge is how to accurately separate the inner and outer race fault features from noisy compoundfaults signals. Therefore, a novel compound fault separation algorithm based on parallel dual-Qfactorsand improved maximum correlation kurtosis deconvolution (IMCKD) is proposed. First, thecompound fault signal is sparse-decomposed by the parallel dual-Q-factor, and the low-resonancecomponents of the signal (compound fault impact component and small amount of noise) are obtained,but it can only highlight the impact of compound faults, and failed to separate the inner and outerrace compound fault signal. Then, the MCKD is improved (IMCKD) by optimizing the selection ofparameters (the shift order M and the filter length L) based on the iterative calculation method withthe Teager envelope spectral kurtosis (TEK) index. Finally, after the composite fault signal is filteredand de-noised by the proposed method, the inner and outer race fault signals are obtained respectively.The fault characteristic frequency is consistent with the theoretical calculation value. The results showthat the proposed method can efficiently separate the mixed fault information and avoid the mutualinterference between the components of the compound fault.

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Section: The Index Teager Envelope Spectral Kurtosis (Tek)mentioning

confidence: 99%

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

Cui

Yang

et al. 2019

Applied Sciences

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“…Since gear fault impulse train often shows cyclostationary characteristics, Marco et al 20 introduced the maximum second-order cyclostationarity blind deconvolution (CYCBD) which outperforms the MED in recovering the fault impulses. The blind filter method has attracted extensive attention in the field of gear damage detection in recent years; see references [21][22][23][24][25][26] and other references therein. For instance, Zhang et al 27 introduced the envelop harmonic product spectrum into CYCBD to estimate the cyclic frequency.…”

Section: Introductionmentioning

confidence: 99%

A fully blind and adaptive filter method to solve the inverse problem in vibration-based gear damage detection

Zhang

Yang

Wang

et al. 2022

Structural Health Monitoring

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In vibration-based gear damage detection, the fault signatures are usually corrupted by noise and convolution of vibration transmission channels. Deconvolution techniques have been widely studied to solve the inverse problem for restoring fault signatures. The mainstream methods, however, often fail due to the requirement of prior knowledge about fault signatures or the experience-based determination of filter parameters. In this paper, a fully blind and adaptive filter method is proposed. Specifically, a new criterion is defined, and the filter method is formulated as a maximization problem based on the criterion. The detailed deduction and pseudo-code for iteratively solving the problem are presented. Moreover, the filter method embeds an advanced swarm intelligence algorithm—that is, the grey wolf optimizer to achieve the adaptive determination of the optimal filter parameters according to the signals. In this context, it is a fully blind and adaptive filter method without requiring any prior knowledge and experiences about the target signatures to pre-determine filter parameters. Analysis results of a simulated signal and the comparisons with several state-of-the-art methods show the effectiveness of the proposed method for restoring the repetitive fault impulses from the mixed signal containing complex interferences. Fault-injection experiment and engineering application are carried out and the results demonstrate the performance of the proposed method for solving the inverse problem in gear damage detection.

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Fault diagnosis on railway vehicle bearing based on fast extended singular value decomposition packet

Huang

Ding

et al. 2020

Measurement

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A hybrid method combining Teager Kaiser energy operator, empirical mode decomposition and minimum entropy deconvolution for monitoring gears damages

Cited by 5 publications

References 30 publications

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

A fully blind and adaptive filter method to solve the inverse problem in vibration-based gear damage detection

Fault diagnosis on railway vehicle bearing based on fast extended singular value decomposition packet

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