An improved local characteristic-scale decomposition to restrict end effects, mode mixing and its application to extract incipient bearing fault signal

Wang, Lei; Liu, Zhiwen

doi:10.1016/j.ymssp.2021.107657

Cited by 26 publications

(13 citation statements)

References 27 publications

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“…4. Define t = 1 to enter main loop of MOGWO, update the position of current search agent by Equations ( 16)- (18).…”

Section: Adaptive Parameter Selection Scheme For Vme Based On Mogwomentioning

confidence: 99%

“…This means that the detection of periodic impulses related to bearing faults is an effective way right now to achieve reliable fault diagnosis of rolling element bearing, which are usually divided into two categories (i.e., the resonance demodulation-based and signal decomposition-based method). The existing resonance demodulation-based methods mainly contain the indicator-assisted deconvolution techniques (e.g., minimum entropy deconvolution (MED), 6 maximum correlated kurtosis deconvolution (MCKD), 7 maximum average kurtosis deconvolution (MAKD), 8 sparse maximum harmonics-to-noise-ratio deconvolution (SMHD), 9 maximum second-order cyclostationarity blind deconvolution (CYCBD) 10 ) and the kurtogram tools (e.g., spectral kurtosis (SK), 11 infogram, 12 autogram, 13 and accugram 14 ), while the current published signal decomposition-based methods have wavelet packet decomposition (WPT), 15 empirical mode decomposition (EMD), 16 local mean decomposition (LMD), 17 local characteristic-scale decomposition (LCD), 18 ensemble empirical mode decomposition (EEMD), 19–21 empirical wavelet transform (EWT), 22 adaptive local iterative filtering (ALIF), 23 symplectic geometry mode decomposition (SGMD), 24 optimal swarm decomposition (OSD), 25 adaptive chirp mode decomposition (ACMD), 26 singular spectrum decomposition (SSD), 27 variational mode decomposition (VMD), 28–32 and its lately variants named variational mode extraction (VME) 33 and successive variational mode decomposition (SVMD) 34 with the similar theory basis of VMD. Among these methods, whether they be the deconvolution techniques or signal decomposition methods, the effectiveness of both the corresponding original algorithm and its improved version has been experimentally demonstrated in extracting the periodic impulse signatures.…”

Section: Introductionmentioning

confidence: 99%

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An adaptive variational mode extraction method based on multi-domain and multi-objective optimization for bearing fault diagnosis

Yan

Yuen

et al. 2022

Structural Health Monitoring

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When a local fault in the rolling element bearing appears during operational running, the induced bearing vibration signal is essentially equivalent to a mixed multi-component signal, which usually includes multiple frequency components (e.g., periodic impulses, random impulses, discrete harmonics, and heavy environmental noise). This indicates that it is difficult to detect the weak periodic impulses related to bearing faults by traditional signal processing methods. Variational mode extraction (VME) as a new signal processing method with band-pass filtering properties can separate the desired periodic impulse component from the mixed multi-component signal due to its similar basis as the popular variational mode decomposition (VMD). However, the filtering performance of VME is significantly influenced by two parameters (i.e., penalty factor and center-frequency). To address this issue, all the previous work focused on single-domain and single-objective parameter optimization from the filtering signal or its envelope, whereas the objective function with a single-criterion cannot depict bearing fault signatures from the impulsiveness and cyclostationarity simultaneously in general cases. Therefore, to improve the bearing fault feature extraction ability and avoid the problem of artificial parameter selection of VME, this paper proposes an adaptive variational mode extraction method based on multi-domain and multi-objective optimization (AVME-MDMO), in which two key parameters of VME are automatically determined by multi-objective gray wolf optimizer (MOGWO) with a novel multi-objective function named integrated measure of sparsity-impact (IMSI) of square envelope (SE) and square envelope spectrum (SES). As a result, the optimal VME is employed to obtain the specific mode component and its corresponding SES for achieving bearing fault diagnosis. The effectiveness of the proposed method has been validated on simulation signal and experiment data of rolling element bearing. Besides, the comparison results demonstrate its superiority and robustness in excavating fault signatures over the VME or VMD with single-objective function, and the fast kurtogram.

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“…4. Define t = 1 to enter main loop of MOGWO, update the position of current search agent by Equations ( 16)- (18).…”

Section: Adaptive Parameter Selection Scheme For Vme Based On Mogwomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An adaptive variational mode extraction method based on multi-domain and multi-objective optimization for bearing fault diagnosis

Yan

Yuen

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…The TFA can concentrate the energy related to fault characteristics. Wang [6] improved the local characteristic-scale decomposition to suppress the end effect. In order to improve the time and frequency resolution of time-frequency representation, Hou [7] optimized the sparse time-frequency representation.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis based on improved cepstrum under variable speed condition

Wang

Sun

2023

Eng. Res. Express

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The speed of rolling bearing often varies in actual operation process, the fault diagnosis of variable speed bearing is particularly important. In this paper, the deﬁnition of local maximum peak value of cepstrum is proposed, which can be used to extract variable speed features. And a variable speed bearing fault diagnosis method based on improved cepstrum is proposed. The proposed method could extract fault feature without tachometer and resampling. At ﬁrst, the Local maximum peaks of the original cepstrum are extracted. Then, the corresponding thresholds of the peaks are calculated and determined one by one. Finally, the feature extraction is completed according to the thresholds. At the same time, this paper veriﬁed the feature extraction results under four diﬀerent variable speed conditions, including acceleration, deceleration, acceleration before deceleration and deceleration before acceleration. The experiments results show that the proposed method has higher accuracy (99.08%) and less running time than the other spectral analysis methods, which demonstrates the accuracy, rapidity and eﬀectiveness. This proposed method provides a novel idea for fault diagnosis under condition of variable speed.

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“…Zheng [19] proposed local characteristicscale decomposition (LCD), a nonstationary signal analysis method that adaptively decomposes a signal to a series of intrinsic scale components in different scales. With good compatibility, LCD methods have seen new applications, such as the local characteristic-scale decomposition-Teager energy operator (LCD-TEO) [20], improved local characteristic-scale decomposition (ILCD) [21], and piecewise cubic Hermite interpolating polynomial-local characteristicscale decomposition (PCHIP-LCD) [22].…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Component Screening Vector Local Characteristic-Scale Decomposition

Guan

Liu

et al. 2022

Shock and Vibration

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The fault vibration signal of a bearing has nonstationary and nonlinear characteristics and can be regarded as the combination of multiple amplitude- and frequency-modulation components. The envelope of a single component contains the fault characteristics of a bearing. Local characteristic-scale decomposition (LCD) can decompose the vibration signal into a series of multiple intrinsic scale components. Some components can clearly reflect the running state of a bearing, and fault diagnosis is conducted according to the envelope spectrum. However, the conventional LCD takes a single-channel signal as the research object, which cannot fully reflect the characteristic information of the rotor, and the analysis results based on different channel signals of the same section will be inconsistent. To solve this problem, based on full vector spectrum technology, the homologous dual-channel information is fused. A vector LCD method based on cross-correlation coefficient component selection is given, and a simulation analysis is completed. The effectiveness of the proposed method is verified by simulated signals and experimental signals of a bearing, which provides a method for bearing feature extraction and fault diagnosis.

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An improved local characteristic-scale decomposition to restrict end effects, mode mixing and its application to extract incipient bearing fault signal

Cited by 26 publications

References 27 publications

An adaptive variational mode extraction method based on multi-domain and multi-objective optimization for bearing fault diagnosis

An adaptive variational mode extraction method based on multi-domain and multi-objective optimization for bearing fault diagnosis

Bearing fault diagnosis based on improved cepstrum under variable speed condition

Rolling Bearing Fault Diagnosis Based on Component Screening Vector Local Characteristic-Scale Decomposition

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