Multivariate EMD and full spectrum based condition monitoring for rotating machinery

Zhao, Xuehua; Patel, Tejas; Zuo, Ming J.

doi:10.1016/j.ymssp.2011.08.001

Cited by 98 publications

(49 citation statements)

References 28 publications

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“…The full spectrum can fuse the information of vibration signals in two orthogonal directions and comprehensively express the intensity and spectrum structure of the rotor vibration [31,32].…”

Section: Full Spectrum Theorymentioning

confidence: 99%

Coupling Fault Feature Extraction Method Based on Bivariate Empirical Mode Decomposition and Full Spectrum for Rotating Machinery

Jia

Wang

et al. 2018

Mathematical Problems in Engineering

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To accurately extract the fault characteristics of vibration signals of rotating machinery is of great significance to the unit online monitoring and evaluation. However, because the current feature extraction methods are mainly for single channel, the results of feature extraction are often inaccurate. To this end, a coupling fault feature extraction method based on bivariate empirical mode decomposition (BEMD) and full spectrum is proposed for rotating machinery. Firstly, the two-dimensional orthogonal signal obtained by orthogonal sampling technique is decomposed by bivariate empirical mode decomposition to obtain the intrinsic mode function with phase information. In order to obtain the sensitive modal components, the sensitivity coefficients are constructed on the basis of mutual information. Then, the sensitivity coefficient of each intrinsic mode function is calculated, and the intrinsic mode function with the larger sensitive coefficient is selected as the sensitive component. Finally, the full spectrum of the sensitive component is obtained using the full vector envelope technique, so as to get a comprehensive and accurate characteristic component. The results of simulations experiment and an application example show that this method can extract the fault characteristic component of the rotating machinery comprehensively and accurately. It is of great significance to realize the accurate diagnosis of coupling faults of rotating machinery.

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Section: Full Spectrum Theorymentioning

confidence: 99%

Coupling Fault Feature Extraction Method Based on Bivariate Empirical Mode Decomposition and Full Spectrum for Rotating Machinery

Jia

Wang

et al. 2018

Mathematical Problems in Engineering

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“…Besides the field of machine monitoring, the Hilbert-Huang Transform and empirical mode analysis have been successfully applied for structural health monitoring [35], fault monitoring in rotating machinery [36], (spindle) bearings [37] and gearboxes [26].…”

Section: Hilbert-huang Transformmentioning

confidence: 99%

Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

Bakker

Gibson

Wilson

et al. 2015

Mechanical Systems and Signal Processing

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Due to its inherent advantages, linear friction welding is a solid-state joining process of increasing importance to the aerospace, automotive, medical and power generation equipment industries. Tangential oscillations and forge stroke during the burn-off phase of the joining process introduce essential dynamic forces, which can also be detrimental to the welding process. Since burn-off is a critical phase in the manufacturing stage, process monitoring is fundamental for quality and stability control purposes. This study aims to improve workholding stability through the analysis of fixture cassette deformations. Methods and procedures for process monitoring are developed and implemented in a fail-or-pass assessment system for fixture cassette deformations during the burn-off phase. Additionally, the denoised signals are compared to results from previous production runs. The observed deformations as a consequence of the forces acting on the fixture cassette are measured directly during the welding process. Data on the linear friction-welding machine are acquired and de-noised using empirical mode decomposition, before the burn-off phase is extracted. This approach enables a direct, objective comparison of the signal features with trends from previous successful welds. The capacity of the whole process monitoring system is validated and demonstrated through the analysis of a large number of signals obtained from welding experiments.

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“…There are three kinds of typical time-frequency methods including empirical mode decomposition (EMD), local mean decomposition (LMD), and local characteristicscale decomposition (LCD). The EMD method proposed by Huang et al is an adaptive decomposition method [5]. This method can effectively decompose the local feature of the signal into several components, and has the ability to process complex nonlinear and nonstationary signal.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Degradation State Identification Based on LCD Relative Spectral Entropy

Bao

2016

J Fail. Anal. and Preven.

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In the interest of obtaining an effective bearing degradation feature from complex, nonlinear, and nonstationary vibration signals, a new analytical methodology based on local characteristic-scale decomposition (LCD) and relative entropy theory is proposed. On the one hand, LCD is a new and relatively excellent time-frequency analysis method to analyze practical vibration signals polluted by noise. On the other hand, relative entropy theory is a good way to characterize different degradation states by calculating the probability distribution difference between the degradation signals and the normal signal. Combining the above two theories, two new degradation features named LRNE and LRQE are extracted to indicate the bearing degradation trend from normal state to even failure state. The noise resistance ability and extensive applicability of both the features are verified by simulation signal. For further analysis of experimental vibration signals, the two features have a satisfying performance to characterize different bearing degradation states. With the help of gray relational analysis and fuzzy C-means clustering, the proposed two characteristics can identify different bearing degradation states of inner ring fault mode with high accuracy. In the end, the two features are applied to doing bearing failure analysis with the full-life bearing data. The results show that the LRNE and LRQE are sensitive to bearing degradation trend in the whole life of bearing.

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Multivariate EMD and full spectrum based condition monitoring for rotating machinery

Cited by 98 publications

References 28 publications

Coupling Fault Feature Extraction Method Based on Bivariate Empirical Mode Decomposition and Full Spectrum for Rotating Machinery

Coupling Fault Feature Extraction Method Based on Bivariate Empirical Mode Decomposition and Full Spectrum for Rotating Machinery

Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

Rolling Bearing Degradation State Identification Based on LCD Relative Spectral Entropy

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