Full Vector Envelope Technique Based on Complex Local Mean Decomposition and Its Application in Fault Feature Extraction for Rotor System

Huang, Chuanjin

doi:10.3901/jme.2016.07.069

Cited by 7 publications

(3 citation statements)

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“…us, LMD has potential application in bearing signal processing, becoming the research hotspot of the existing adaptive time-frequency analysis methods [26][27][28][29][30].…”

Section: Nonstationary Signal Processingmentioning

confidence: 99%

A New Feature Extraction Method for Bearing Faults in Impulsive Noise Using Fractional Lower‐Order Statistics

2019

Shock and Vibration

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According to the performance degradation problem of feature extraction from higher-order statistics in the context of alpha-stable noise, a new feature extraction method is proposed. Firstly, the nonstationary vibration signal of rolling bearings is decomposed into several product functions by LMD to realize signal stability. Then, the distribution properties of product functions in the time domain are discussed by the comparison of heavy tails and characteristic exponent estimation. Fractional lower-order p-function optimization is obtained by the calculation of the distance ratio based on K-means algorithms. Finally, a fault feature dataset is established by the optimal FLOS and lower-dimensional mapping matrix of covariation to accurately and intuitively describe various bearing faults. Since the alpha-stable noise is effectively suppressed and state described precisely, the presented method has shown better performance than the traditional methods in bearing experiments via fractional lower-order feature extraction.

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“…us, LMD has potential application in bearing signal processing, becoming the research hotspot of the existing adaptive time-frequency analysis methods [26][27][28][29][30].…”

Section: Nonstationary Signal Processingmentioning

confidence: 99%

A New Feature Extraction Method for Bearing Faults in Impulsive Noise Using Fractional Lower‐Order Statistics

2019

Shock and Vibration

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“…The unary signal analysis method can be used to analyze signals from multiple channels separately. However, this method cannot guarantee that the signal of each channel has the same decomposition scale, which causes the information fusion to become increasingly challenging [16].…”

Section: Introductionmentioning

confidence: 99%

Fault feature extraction method for rolling bearing based on MVMD and complex Fourier transform

Huang¹,

Song²

2022

J. vibroeng.

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The vibration signals caused by rolling bearing defects in different directions may be different, and the fault diagnosis based on single channel vibration signals may be made incorrectly, and the observation results may be understood wrong. To avoid it, a new rolling bearing fault feature extraction method based on multivariate variational mode decomposition (MVMD) and complex Fourier transform (CFT) were proposed. First, the orthogonally sampled vibration signals were combined into a multivariate signal, and the multivariate signal was decomposed into several intrinsic mode functions (IMFs) using the MVMD. As per this method, a unified mathematical model was used to model vibration signals in two directions, ensuring that fault features were decomposed to the same level. Finally, the CFT was applied to fuse the envelope signals in two directions in order to obtain a clearer and comprehensive amplitude-frequency feature. Simulation and test results verify the feasibility and superiority of the proposed method.

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“…However, the joint information between multiple sensors was not considered in [5,17]. is method, combined with the CLMD or MEMD and full spectrum, has been proposed to obtain joint information among multichannel signals [12,18]. e methods based on CLMD or BEMD simultaneously decompose the signals into multichannels, thus ensuring that the number of decomposition results is the same and is readily incorporated.…”

Section: Introductionmentioning

confidence: 99%

Instantaneous Amplitude‐Frequency Feature Extraction for Rotor Fault Based on BEMD and Hilbert Transform

Huang

Song

Lei

et al. 2019

Shock and Vibration

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The vibration signals propagating in different directions from rotating machines can contain a variety of characteristic information. A novel feature extraction method based on bivariate empirical mode decomposition (BEMD) for rotor is proposed to comprehensively extract the fault features. In this work, the number of signal projection directions is determined through simulation, and the energy end condition based on the energy threshold is increased using BEMD to enhance the decomposition quality. Mixed vibration signals are generated along two orthogonal directions. Then, the acquired vibration signal can be decomposed into several intrinsic mode functions (IMFs) at the rotational speed using the BEMD method. Furthermore, the instantaneous frequency and instantaneous amplitude of the real signals and the imaginary part of the IMF signals are obtained using the Hilbert transform. The fault features along two and three dimensions can be investigated, providing more comprehensive information to aid in the fault diagnosis of rotor. Experimental results on oil film oscillation, the oil whirl, the bistability of the rotor, and looseness and rotor rubbing composite fault indicate the effectiveness of the proposed method.

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Full Vector Envelope Technique Based on Complex Local Mean Decomposition and Its Application in Fault Feature Extraction for Rotor System

Cited by 7 publications

References 0 publications

A New Feature Extraction Method for Bearing Faults in Impulsive Noise Using Fractional Lower‐Order Statistics

A New Feature Extraction Method for Bearing Faults in Impulsive Noise Using Fractional Lower‐Order Statistics

Fault feature extraction method for rolling bearing based on MVMD and complex Fourier transform

Instantaneous Amplitude‐Frequency Feature Extraction for Rotor Fault Based on BEMD and Hilbert Transform

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