Research on Feature Extraction Method of Fiber Bragg Grating Vibration Monitoring Based on FFT
by Mengxing Zhang, Youming Hua, Chunbin Chen, Chenkun Chu, Xiuli Zhang * 
Faculty of Civil Engineering and Mechamnics, Jiangsu University, Zhenjiang, 212013, China
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 1, Issue 7, Page # 44-47, 2022; DOI: 10.55708/js0107007
Keywords: Fiber grating, Vibration monitoring, FFT, Feature extraction
Received: 14 April 2022, Revised: 29 June 2022, Accepted: 08 July 2022, Published Online: 27 July 2022
APA Style
Zhang, M., Hua, Y., Chen, C., Chu, C., & Zhang, X. (2022). Research on Feature Extraction Method of Fiber Bragg Grating Vibration Monitoring Based on FFT. Journal of Engineering Research and Sciences, 1(7), 44–47. https://doi.org/10.55708/js0107007
Chicago/Turabian Style
Zhang, Mengxing, Youming Hua, Chunbin Chen, Chenkun Chu, and Xiuli Zhang. “Research on Feature Extraction Method of Fiber Bragg Grating Vibration Monitoring Based on FFT.” Journal of Engineering Research and Sciences 1, no. 7 (July 1, 2022): 44–47. https://doi.org/10.55708/js0107007.
IEEE Style
M. Zhang, Y. Hua, C. Chen, C. Chu, and X. Zhang, “Research on Feature Extraction Method of Fiber Bragg Grating Vibration Monitoring Based on FFT,” Journal of Engineering Research and Sciences, vol. 1, no. 7, pp. 44–47, Jul. 2022, doi: 10.55708/js0107007.
Optical fiber is used in various fields because of its advantages of large-capacity communication, long-distance transmission, low signal crosstalk, good confidentiality, anti-electromagnetic interference, good transmission quality, small size, light weight, and long life. In this paper, the latest research progress of optical fiber sensing technology and its application and development in the field of rotating parts are summarized, and the characteristics and working principles of optical fiber intelligent composite materials are introduced. Fast Fourier Transform (FFT) and Hilbert fringe spectra are then applied to frequency component analysis. Quantitative research is carried out on the variation of the frequency components in each frequency band of the vibration signal of the damaged and non-damaged rotating parts. The method can analyze the fault signal to achieve the purpose of accurately extracting the fault characteristics of the rolling bearing, which plays an important guiding role in the accurate diagnosis of the bearing fault.
- Shen H , Li S, Gu D, et al. Bearing defect inspection based on machine vision[J]. Measurement, 2012, 45(4):719-733.
- Xiong Q, Zhang W, Xu Y, et al. Diagnosing axle box bearings’ fault using a refined phase difference correction method[J]. Journal of Mechanical Science and Technology, 2019, 33(1):95-108.
- Georgeson J D. Inspection of Roller Bearing Surfaces with Laser Doppler Vibrometry[J]. Journal of Manufacturing Science & Engineering, 1992, 114(1):123-125.
- Sheen Y T. A complex filter for vibration signal demodulation in bearing defect diagnosis[J]. Journal of Sound & Vibration, 2004, 276(1-2):105-119.
- Fu Z, Brown D J, Haynes B P . A new method of non-stationary signal analysis for control motor bearing fault diagnosis[C]// IEEE International Symposium on Intelligent Signal Processing. IEEE, 2003.
- Meng Z, Li S S. Rolling bearing fault diagnosis based on improved wavelet threshold de-noising method and HHT[J]. Journal of Vibration and Shock, 2013, 32(14):204-208+214.
- Guo J, Liu X, Li S, et al. Bearing Intelligent Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network[J]. Shock and Vibration, 2020, 2020(19):1-14.
- Singh G, Kumar R , Singh M , et al. Detection of crack initiation in the ball bearing using FFT analysis. 2017.
- Xuan G , Xiao-Peng Z, Ning L , et al. Research on Fiber Bragg Grating Acoustic Emission Technology Applied in Helicopter Bearing Detection[J]. Procedia Engineering, 2015, 99:1203-1212.
- Zhou X, Zhang H, Hao X , et al. Investigation on thermal behavior and temperature distribution of bearing inner and outer rings[J]. Tribology International, 2018, 130:289-298.
- L. Chen, Y.S. Choy, T.G. Wang, YK. Chiang, Fault detection of the wheel in wheel/rail system using kurtosis beamforming method, Struct. Health Monitor.19 (2) (2020) 495- 509.
- X Liu, D. Pei, G. Lodewijks, Z. Zhao, J. Mei, Acoustic signal-based fault detection on belt conveyor idlers using machine learning. Adv. Powder Technol. 31 (7)(2020) 2689- 2698.
- T. Tran, J. Lundgren, Drill fault diagnosis based on the scalogram and melspectrogram of sound signals using artificial intelligence, IEEE Access 8 (2020)203655- -203666.
- LAL Janssen, I. Lopez Arteaga, Data processing and augmentation of acoustic array signals for fault detection with machine learning, J. Sound Vib. 483(2020) 115483, https://doi.org/10.1016/j.jsv.2020.1 15483.
