AFSA-FastICA-CEEMD Rolling Bearing Fault Diagnosis Method Based on Acoustic Signals

Jin Yan, Fubing Zhou, Xu Zhu and Dapeng Zhang ()
Additional contact information
Jin Yan: Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Guangdong Ocean University, Zhanjiang 524088, China
Fubing Zhou: Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Guangdong Ocean University, Zhanjiang 524088, China
Xu Zhu: Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Guangdong Ocean University, Zhanjiang 524088, China
Dapeng Zhang: Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Guangdong Ocean University, Zhanjiang 524088, China

Mathematics, 2025, vol. 13, issue 5, 1-28

Abstract: As one of the key components in rotating machinery, rolling bearings have a crucial impact on the safety and efficiency of production. Acoustic signal is a commonly used method in the field of mechanical fault diagnosis, but an overlapping phenomenon occurs very easily, which affects the diagnostic accuracy. Therefore, effective blind source separation and noise reduction of the acoustic signals generated between different devices is the key to bearing fault diagnosis using acoustic signals. To this end, this paper proposes a blind source separation method based on an AFSA-FastICA (Artificial Fish Swarm Algorithm, AFSA). Firstly, the foraging and clustering characteristics of the AFSA algorithm are utilized to perform global optimization on the aliasing matrix W, and then inverse transformation is performed on the global optimal solution W, to obtain a preliminary estimate of the source signal. Secondly, the estimated source signal is subjected to CEEMD noise reduction, and after obtaining the modal components of each order, the number of interrelationships is used as a constraint on the modal components, and signal reconstruction is performed. Finally, the signal is subjected to frequency domain feature extraction and bearing fault diagnosis. The experimental results indicate that, the new method successfully captures three fault characteristic frequencies ( 1 f i , 2 f i , and 3 f i ), with their energy distribution concentrated in the range of 78.9 Hz to 228.7 Hz, indicative of inner race faults. Similarly, when comparing the different results with each other, the denoised source signal spectrum successfully captures the frequencies 1 f o , 2 f o , and 3 f o and their sideband components, which are characteristic of outer race faults. The sideband components generated in the above spectra are preliminarily judged to be caused by impacts between the fault location and nearby components, resulting in modulated frequency bands where the modulation frequency corresponds to the rotational frequency and its harmonics. Experiments show that the method can effectively diagnose the bearing faults.

Keywords: rolling bearing; fault diagnosis; acoustic signal; blind source separation; CEEMD (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/5/884/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/5/884/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:5:p:884-:d:1606945

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().