Recent Advancements in Fault Diagnosis of Spherical Roller Bearing: A Short Review

Senthilnathan, N.; Babu, T. Narendiranath; Varma, K. Sai Dileep; Rushmith, S.; Reddy, J. Akash; Kavitha, K. V. N.; Prabha, D. Rama

doi:10.1007/s42417-024-01293-4

Cited by 2 publications

(1 citation statement)

References 47 publications

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“…However, the fault pulse will be overwhelmed by the interference signals, so the critical fault features cannot be extracted accurately. Therefore, timely identification of rolling bearing failure to prevent accidents is a key issue of the current research [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

An adaptive feature mode decomposition-guided phase space feature extraction method for rolling bearing fault diagnosis

Xin,

Jiang,

Jiang

et al. 2024

Meas. Sci. Technol.

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The extraction of fault features from rolling bearings is a challenging and highly important task. Since they have complex operating conditions and are usually under a strong noise background. In this study, a novel approach termed phase space feature extraction guided by an adaptive feature mode decomposition (AFMDPSFE) is proposed to detect subtle faults in rolling bearings. Initially, a new method using Kullback-Leiber divergence is introduced to automatically select the optimal mode number and filter length for the decomposition of vibration signals, facilitating the automatic extraction of optimal components and ensuring efficient screening. This eliminates the need for manual configuration of feature mode decomposition parameters. Furthermore, a criterion that could determine two crucial parameters to capture system dynamics characteristics in phase space reconstruction is embedded into AFMDPSFE algorithm. Subsequently, a series of high-dimensional independent components is derived. The envelope spectrum of the principal component exhibiting the highest kurtosis value is computed to achieve fault identification, consequently enhancing the separation of signal from noise. Both simulations and experimental results confirm the effectiveness of AFMDPSFE approach. A comparison analysis shows the excellent performance of AFMDPSFE in extracting fault features from significant noise interference.

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Section: Introductionmentioning

confidence: 99%