Rolling bearing faults identification based on multiscale singular value

Yu, Mingyue; Zhang, Yi; Yang, Changyuan

doi:10.1016/j.aei.2023.102040

Cited by 22 publications

(1 citation statement)

References 33 publications

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“…When reconstructing signals according to the Hankel matrix, equation ( 9) is the classical reconstruction method (the first row is connected to the last column or the first column is connected to the last row [29]):…”

Section: Signal Reconstructionmentioning

confidence: 99%

A new method of noise reduction grounded on the Hankel matrix and its application in rubbing fault diagnosis

Zhang,

Yu,

Feng

et al. 2024

Meas. Sci. Technol.

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In processing signals with singular value decomposition (SVD), one of the keys lies in building an appropriate Hankel matrix from signals. To address the difficulty in extracting the feature information of rubbing faults between rotor and stator, by taking advantage of the nature of rubbing fault information closely related to the rotation period of equipment, a new method of SVD is presented according to the Hankel matrix built from the periodicity of rotation machine. Firstly, with the periodicity of rub-impact fault as the basis, the interval step size between Hankel vectors was determined to self-adaptively build a Hankel matrix of signal. Secondly, the newly-built Hankel matrix was denoised through singular value differential spectrum (SVDS). Thirdly, to reduce the loss of data as much as possible, a strategy was proposed to rebuild signals according to the first and last rows of denoised signals. Fourthly, features of rubbing faults were extracted according to the frequency spectrum of reconstructed signals and faults were identified. To verify the applicability and effectiveness of presented algorithm, various types of simulation signals and tester signals from different states were brought in; meanwhile, the presented algorithm was compared with a variety of classical methods. The result proves that the proposed method not only can effectively restrain noise interference, but also highlight fault feature information and correctly identify rub-impact faults.

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Section: Signal Reconstructionmentioning

confidence: 99%