文献类型：期刊文献

英文题名：AFSA-FastICA-CEEMD Rolling Bearing Fault Diagnosis Method Based on Acoustic Signals

作者：Yan, Jin[1,2];Zhou, Fubing[1];Zhu, Xu[1];Zhang, Dapeng[1,2]

机构：[1]Guangdong Ocean Univ, Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Shenzhen Res Inst, Shenzhen 518120, Peoples R China

年份：2025

卷号：13

期号：5

外文期刊名：MATHEMATICS

收录：SCI-EXPANDED(收录号：WOS:001442642400001)、、Scopus(收录号：2-s2.0-86000506114)、WOS

基金：The authors gratefully acknowledge the support provided for this research by the Natural Science Foundation of Guangdong Province (2022A1515011562) and National Natural Science Foundation of China (52201355), by Guangdong Provincial Special Fund for promoting high quality economic development (Yuerong Office Letter [2020]161, GDNRC [2021]56), and Development of intelligent early warning system for regional equipment failure (CY-ZJ-19-ZC-005).

语种：英文

外文关键词：rolling bearing; fault diagnosis; acoustic signal; blind source separation; CEEMD

外文摘要：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 (1fi, 2fi, and 3fi), 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 1fo, 2fo, and 3fo 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.