Simultaneous Sparse Representations with Partially Varying Support

Sathi, Lakshmi Madhuri; Juluri, Varsha; Tangudu, Santhoshini; Sreeram, Swathy; Sajan, Kavya Kuzhithara; Sandeep, P.

doi:10.1007/978-981-19-2281-7_72

Cited by 1 publication

(1 citation statement)

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“…Signals associated with vibration are widely employed in the area of weak fault diagnosis because of their inherent advantages such as rich signals and sensitivity to early faults. SR method can characterize the signal with fewer atoms, and it finds extensive application in fault diagnosis, due to its more efficient processing and noise reduction capability [2]. Martin et al used sparse coding to monitor wind turbine conditions, which is effective for gearbox and rolling bearings [3].…”

Section: Introductionmentioning

confidence: 99%

Weak faults diagnosis of traction motor bearings based on SR-MED-EEMD method

Liu,

Hu,

Hao

2024

J. Phys.: Conf. Ser.

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Rolling bearings play a crucial role in ensuring the security and reliable operation of high-speed trains (HST) as a vital component of their traction system. In response to the difficulty in extracting early fault features of high-speed train traction motor bearings under strong background noise, a method based on sparse representation (SR), minimum entropy deconvolution (MED), and ensemble empirical mode decomposition (EEMD) is proposed. The method reduces the dimensionality of complex signals through SR and then filters the sparse signals through MED to improve the fault signal pulse amplitude. The processed signals are decomposed by using EEMD to remove noise components, further improving the peak signal-to-noise ratio of fault characteristic frequency. The method’s efficacy in detecting faults within rolling bearings has been confirmed through simulation experiments and demonstrated its high precision.

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Section: Introductionmentioning

confidence: 99%