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

Huang, Chuanjin; Song, Haijun; Lei, Wenping; Niu, Zhanya; Meng, Yajun

doi:10.1155/2019/1639139

Cited by 10 publications

(5 citation statements)

References 18 publications

(25 reference statements)

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“…In this section, the REB data of Case Western Reserve University (CWRU) is utilized to verify the validity and practicability of the proposed method. A detailed description of the experimental apparatus can be found in reference (Huang et al, 2019). The test bearing type is SKF6205-2RS.…”

Section: The Actual Signal Analysismentioning

confidence: 99%

A new scheme for extracting fault features of rolling element bearings

Liu

2022

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PurposeThe purpose of this paper is to propose a new fault feature extraction scheme for the rolling element bearing.Design/methodology/approachThe generalized Stockwell transform (GST) and the singular value ratio spectrum (SVRS) methods are combined. A time-frequency distribution measurement criterion named the energy concentration measurement (ECM) is initially used to determine the parameter of the optimal GST method. Then, the optimal GST is applied to conduct a time-frequency transformation for a raw signal. Subsequently, the two-dimensional time-frequency matrix is obtained. Finally, the improved singular value decomposition (SVD) analysis is used to conduct a noise reduction of the time-frequency matrix. The SVRS is proposed to select the effective singular values. Furthermore, the time-domain feature of the impact signal is obtained by taking the inverse GST transform.FindingsThe simulated and experimental signals are used to verify the superiority of the proposed method over conventional methods. The obtained results show that the proposed method can effectively extract fault features of the rolling element bearing.Research limitations/implicationsThis paper mainly discusses the application of GST and SVRS methods to analyze the weak fault feature extraction problem. The next research direction is to explore the application of the Hilbert Huang transform (HHT) and variational modal decomposition (VMD) in the impact feature extraction of rolling bearing.Originality/valueIn the present study, a new SVRS method is proposed to select the number of effective singular values. This paper proposed an effective way to obtain the fault feature in monitoring of rotating machinery.

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Section: The Actual Signal Analysismentioning

confidence: 99%

A new scheme for extracting fault features of rolling element bearings

Liu

2022

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“…Hilbert envelope is a classical method, which has been widely used in fault feature extraction [29]. The fault characteristic frequency can be obtained from Hilbert envelopes of the sub-bands with fault features.…”

Section: Improved Tqwt Based Compound Fault Diagnosis Of Rolling Bear...mentioning

confidence: 99%

Compound fault diagnosis of rolling bearings based on improved tunable Q-factor wavelet transform

Zhou²,

Gao

et al. 2021

Meas. Sci. Technol.

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In order to solve the difficulty of compound fault diagnosis of rolling bearings, a novel rolling bearings fault diagnosis method based on improved tunable Q-factor wavelet transform (TQWT) is proposed in this paper. Firstly, a new evaluation index of signal decomposition called KR is defined by summing kurtosis and root mean square (RMS) with weight. KR is the compromise between impulse factor and energy factor, which can better represent the fault characteristics of sub-bands obtained by TQWT. Secondly, the KR is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Thirdly, the bearing vibration signal is decomposed by the improved TQWT and the sub-bands are sorted descending according to the KR. Finally, the Hilbert envelope analysis is carried out and the fault types are determined by comparing the fault characteristic frequencies obtained from the Hilbert envelopes to the fault characteristic frequencies calculated by formula. The proposed fault diagnosis method is fully evaluated by simulation and experiments. The results demonstrate that the KR takes advantage of kurtosis and RMS and can be better used to optimize the parameter of TQWT. And the compound fault features of rolling bearings can be accurately separated into different sub-bands by the improved TQWT, which is helpful to improve the accuracy of compound fault diagnosis of rolling bearings.

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“…Lu proposed a new method for the fault feature extraction of rolling bearings based on MEMD and correlation analysis to calculate fault correlation factors to select effective intrinsic mode functions (IMFs) [26]. Huang applied the BEMD, CLMD and HT to extract rotor fault features [27][28]. BEMD or CLMD ensures that the signals of two channels have the same decomposition scale.…”

Section: Introductionmentioning

confidence: 99%

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

Huang¹,

Song²

2022

J. vibroeng.

Self Cite

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

Cited by 10 publications

References 18 publications

A new scheme for extracting fault features of rolling element bearings

A new scheme for extracting fault features of rolling element bearings

Compound fault diagnosis of rolling bearings based on improved tunable Q-factor wavelet transform

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

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