Cosine window-based boundary processing method for EMD and its application in rubbing fault diagnosis

Qi, Keyu; He, Zhengjia; Ye, Zi

doi:10.1016/j.ymssp.2007.04.007

Cited by 73 publications

(20 citation statements)

References 11 publications

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“…Theoretically, HHT technique is adaptive and able to provide equal resolution at all frequencies and time instants, which makes use of HHT more meaningful for transient vibration signals such as rotor coast up vibration response. As a powerful tool for analysis of nonlinear and non-stationary signal, the HHT technique has successfully been used in signal analysis of faulted rotor system [15][16][17].…”

Section: Hhtmentioning

confidence: 99%

Research on vibration response of a multi-faulted rotor system using LMD-based time-frequency representation

Jiao

Suxiang

Chang

et al. 2012

EURASIP J. Adv. Signal Process.

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Unbalance, fatigue crack and rotor-stator rub are the three common and important faults in a rotor-bearing system. They are originally interconnected each other, and their vibration behaviors do often show strong nonlinear and transient characteristic, especially when more than one of them coexist in the system. This article is aimed to study the vibration response of the rotor system in presence of multiple rotor faults such as unbalance, crack, and rotor-stator rub, using local mean decomposition-based time-frequency representation. Equations of motion of the multi-faulted Jeffcott rotor, including unbalance, crack, and rub, are presented. By solving the motion equations, steady-state vibration response is obtained in presence of multiple rotor faults. As a comparison, Hilbert-Huang transformation, based on empirical mode decomposition, is also applied to analyze the multi-faults data. By the study some diagnostic recommendations are derived.

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Section: Hhtmentioning

confidence: 99%

Research on vibration response of a multi-faulted rotor system using LMD-based time-frequency representation

Jiao

Suxiang

Chang

et al. 2012

EURASIP J. Adv. Signal Process.

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“…EMD not only has a wide applicability and a high signal-to-noise ratio, but also does not need basis functions; therefore, it has been applied in many engineering fields [5][6][7][8]. The original signal is decomposed into a series of intrinsic mode functions (IMFs) according to the signal features with EMD.…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers have attempted to solve this problem in the past. They often focus on eliminating mode mixing and calculating sensitive features [5,9,10]. With the development of science and technology, there are higher requirements for the efficiency and accuracy in the industrial application.…”

Section: Introductionmentioning

confidence: 99%

A Research on Maximum Symbolic Entropy from Intrinsic Mode Function and Its Application in Fault Diagnosis

Zhang

Liu

et al. 2017

Mathematical Problems in Engineering

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Empirical mode decomposition (EMD) is a self-adaptive analysis method for nonlinear and nonstationary signals. It has been widely applied to machinery fault diagnosis and structural damage detection. A novel feature, maximum symbolic entropy of intrinsic mode function based on EMD, is proposed to enhance the ability of recognition of EMD in this paper. First, a signal is decomposed into a collection of intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal, and then IMFs are transformed into a serious of symbolic sequence with different parameters. Second, it can be found that the entropies of symbolic IMFs are quite different. However, there is always a maximum value for a certain symbolic IMF. Third, take the maximum symbolic entropy as features to describe IMFs from a signal. Finally, the proposed features are applied to evaluate the effect of maximum symbolic entropy in fault diagnosis of rolling bearing, and then the maximum symbolic entropy is compared with other standard time analysis features in a contrast experiment. Although maximum symbolic entropy is only a time domain feature, it can reveal the signal characteristic information accurately. It can also be used in other fields related to EMD method.

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“…Peng et al (2005a and2005b) proposed a signal preprocessing method for EMD by using WT, but the introduction of the linear operation of WT will compromise the adaptive property of EMD. Until now, researches on EMD are mainly limited to its applications and the improvements of its techniques such as stopping criterion of sifting process (Cheng et al 2006), end effect (Cheng et al 2007;Qi et al 2007), fitting method (Chen et al 2006), mode mixing (Li et al 2005). The researches on the theoretical foundation of the EMD are still very much lacking.…”

Section: Introductionmentioning

confidence: 99%

Time-frequency representation based on time-varying autoregressive model with applications to non-stationary rotor vibration analysis

Zhang

Xiong

Liu

et al. 2010

Sadhana

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A parametric time-frequency representation is presented based on timevarying autoregressive model (TVAR), followed by applications to non-stationary vibration signal processing. The identification of time-varying model coefficients and the determination of model order, are addressed by means of neural networks and genetic algorithms, respectively. Firstly, a simulated signal which mimic the rotor vibration during run-up stages was processed for a comparative study on TVAR and other non-parametric time-frequency representations such as Short Time Fourier Transform, Continuous Wavelet Transform, Empirical Mode Decomposition, Wigner-Ville Distribution and Choi-Williams Distribution, in terms of their resolutions, accuracy, cross term suppression as well as noise resistance. Secondly, TVAR was applied to analyse non-stationary vibration signals collected from a rotor test rig during run-up stages, with an aim to extract fault symptoms under non-stationary operating conditions. Simulation and experimental results demonstrate that TVAR is an effective solution to non-stationary signal analysis and has strong capability in signal time-frequency feature extraction.

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Cosine window-based boundary processing method for EMD and its application in rubbing fault diagnosis

Cited by 73 publications

References 11 publications

Research on vibration response of a multi-faulted rotor system using LMD-based time-frequency representation

Research on vibration response of a multi-faulted rotor system using LMD-based time-frequency representation

A Research on Maximum Symbolic Entropy from Intrinsic Mode Function and Its Application in Fault Diagnosis

Time-frequency representation based on time-varying autoregressive model with applications to non-stationary rotor vibration analysis

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