Restoring cyclostationarity of rolling element bearing signals from the instantaneous phase of their envelope

Marsick, Adrien; André, Hugo; Khelf, Ilyes; Leclère, Quentin; Antoni, Jérôme

doi:10.1016/j.ymssp.2023.110264

Cited by 14 publications

(7 citation statements)

References 53 publications

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“…The existing literatures have conducted in-depth research on the bearing fault mechanism [1][2][3][4][5]. The results confirm that as the fault size increases, the rolling elements passing through the fault area is prolonged, and the sharpness of transient signals decreases; on the contrary, the oscillation fluctuation continue to increase.…”

Section: Ifmmentioning

confidence: 60%

“…It is of great significance to conduct fault diagnosis research on rolling bearings. In theory, vibration diagnosis can provide reliable and precise expected results [1][2][3][4][5][6][7]. In many applications, however, the mechanical structure and operating conditions affect the vibration source and change the vibration frequency and amplitude characteristics, making them non-stationary [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…In theory, vibration diagnosis can provide reliable and precise expected results [1][2][3][4][5][6][7]. In many applications, however, the mechanical structure and operating conditions affect the vibration source and change the vibration frequency and amplitude characteristics, making them non-stationary [1][2][3][4][5][6][7]. In addition, most of the measured fault signals obtained from rotating machinery contain sparsely distributed impulses, which are often contaminated by noise components with white Gaussian noise behavior from various sources.…”

Section: Introductionmentioning

confidence: 99%

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Symmetric circulant matrix decomposition-based multivariable group sparse coding for rolling bearing fault diagnosis

Yuan,

Liu,

Yang

et al. 2024

Meas. Sci. Technol.

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Singular value decomposition (SVD) technique proves its effectiveness in mechanical signal analysis by decomposing the test signal into a series of singular spectral components of different frequency bands. Nevertheless, how to adapt this technology to the needs of cyclo-nonstationary signal and how to set the decomposition number under strong noise background while maintaining detailed features to obtain the optimal component containing the most fault information, remains an important issue that needs to be addressed in the field of mechanical fault diagnosis. To overcome these disadvantages, the symmetric circulant matrix decomposition (SCMD) is presented. Two main ideas structure the present technique. Firstly, symmetric circulant matrix is used to generate eigenvectors, which will better adapt to the cyclo-nonstationary signal associated with the structural symmetry of rotating machinery. Then, an impulse fluctuation measure (IFM) is established to adaptively search for the decomposition number and extract the optimal component. Moreover, to better improve the impulse extraction effect of SCMD, the multivariate group sparse coding (MGSC) based on the multivariate correlation characteristics and intra group sparsity characteristics of impulse signals is proposed, which can enhance impulse features while preserving fault details as much as possible. The reliability and feasibility of the method are verified by the experiments of deep groove ball bearings and tapered roller bearings. The comparison with several classic methods shows that this method is more effective in weak feature extraction.

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Section: Ifmmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Symmetric circulant matrix decomposition-based multivariable group sparse coding for rolling bearing fault diagnosis

Yuan,

Liu,

Yang

et al. 2024

Meas. Sci. Technol.

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show abstract

“…The bearing fault diagnosis practice has shown that there is strong correlation between neighboring dates in the local FIGURE 1 The flow chart of the proposed method area of periodic impulse signals, thereby the sparse algorithms construction needs to consider the correlation between feature vectors within the group. Inspired by the idea of multi-wavelet incorporating neighboring coefficients, we set the neighboring groups of the data sequence asy j −1 ,y j andy j +1 ,thereupon the slip group sparse function of intra-group feature vectors is formally formulated asT 2 ∕y 2 j −1 + y 2 j + y 2 j +1 ,wherey 2 j −1 + y 2 j + y 2 j +1 represents group composed of neighboring feature vectors.…”

Section: Adaptive Group Sparse Codingmentioning

confidence: 99%

“…Rolling bearings are the fundamental components of major equipment, integrating many design theories and manufacturing technologies. In recent years, driven by the development of various fields such as rail transit, wind power equipment, oil mines, aviation, aerospace etc., the rolling bearings application has become increasingly widespread [1][2][3][4][5][6][7]. However, due to the coupling effect of various energies (mechanical energy, thermal energy, chemical energy etc.)…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing weak fault detection using transient structure‐optimal VMD and adaptive group sparse coding

Yuan,

Liu,

Zhang

2023

IET Science Measure & Tech

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Rolling bearings are essential parts in machine equipment and detecting damage in the early stage is crucial for ensuring the safe production and machine life. However, it is difficult to extract weak fault features under strong background noise, discrete harmonic frequency interference and non‐stationary service conditions. This investigation proposes a hybrid fault diagnosis approach utilizing transient structure‐optimal variational mode decomposition (TS‐OVMD) and adaptive group sparse coding (AGSC) for addressing the formidable problem. According to the singular value structure between transient signal and the interference signal, this work investigates the singular value shrinkage (SVS) technique to adaptively obtain the independent components number. Then, we present a transient structure measure (TSM) to adaptively optimize the balance factor. This measure index systematically quantifies the typical characteristics of the bearing fault signal, which can maximize the fault information representation and effectively reduces the useful information loss caused by improper selection of VMD parameters. Finally, a sparse coding model called AGSC is furthermore designed to enhance the fault impulses readability and suppress residual noise based on the sparsity within group property and the TSM. The proposed approach is verified using experimental data and is found to be superiority comparison with the state‐of‐the‐art method.

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Rhythm-adaptive statistical estimation methods of probabilistic characteristics of cyclic random processes

Lupenko

2024

Digital Signal Processing

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Restoring cyclostationarity of rolling element bearing signals from the instantaneous phase of their envelope

Cited by 14 publications

References 53 publications

Symmetric circulant matrix decomposition-based multivariable group sparse coding for rolling bearing fault diagnosis

Symmetric circulant matrix decomposition-based multivariable group sparse coding for rolling bearing fault diagnosis

Rolling bearing weak fault detection using transient structure‐optimal VMD and adaptive group sparse coding

Rhythm-adaptive statistical estimation methods of probabilistic characteristics of cyclic random processes

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