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 3 publications

(1 citation statement)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.

View full text Add to dashboard Cite

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.

show abstract

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.

View full text Add to dashboard Cite

show abstract

Machine Learning Approach for Predicting the Solid Particle Lubricant Contamination in a Spherical Roller Bearing

Rameshkumar,

Nataraj,

Krishnakumar

et al. 2024

IEEE Access

View full text Add to dashboard Cite

A New Heavy-Duty Bearing Degradation Evaluation Method with Multi-Domain Features

Xiong,

Liu,

et al. 2024

Sensors

View full text Add to dashboard Cite

Under heavy load conditions, bearings are subjected to non-uniform and frequently changing loads, which leads to randomness in the spatial distribution of bearing degradation characteristics. Aiming at the problem that the traditional degradation index cannot accurately reflect the degradation state of heavy-duty bearings in the whole life cycle, a new degradation evaluation method based on multi-domain features is proposed in this paper, which aims to capture the early degradation point of heavy-duty bearings and characterize their degradation trend. Firstly, the energy entropy feature is obtained by improving the wavelet packet decomposition, and the original multi-domain feature set is constructed by combining the time domain and frequency domain features. Then, the optimal feature matrix is formed by using the comprehensive evaluation index. Finally, integrating probability and distance information, a comprehensive degradation index was constructed to evaluate the degradation, determine the initial degradation time, and quantitatively analyze the bearing degradation state. The validity of the proposed method is verified in two datasets. The proposed method can accurately identify the early degradation of bearings and track the state of bearing degradation, so as to realize the degradation assessment.

show abstract

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

Cited by 3 publications

References 47 publications

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

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

Machine Learning Approach for Predicting the Solid Particle Lubricant Contamination in a Spherical Roller Bearing

A New Heavy-Duty Bearing Degradation Evaluation Method with Multi-Domain Features

Contact Info

Product

Resources

About