Journal of Northeastern University(Natural Science) ›› 2021, Vol. 42 ›› Issue (5): 673-678.DOI: 10.12068/j.issn.1005-3026.2021.05.010

• Mechanical Engineering • Previous Articles     Next Articles

Bearing Fault Detection Based on Improved CYCBD Method

LUO Zhong1,2, XU Di1,2, LI Lei1,2, MA Hui1,2   

  1. 1. School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China; 2. Key Laboratory of Vibration and Control of Aero-Propulsion System, Ministry of Education, Northeastern University, Shenyang 110819, China.
  • Revised:2020-10-13 Accepted:2020-10-13 Published:2021-05-20
  • Contact: LUO Zhong
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Abstract: Aiming at the problem that it is difficult to extract the fault features of the main shaft bearing of turboshaft engines, and that the background noise has a large interference, an improved second-order cyclostationary blind deconvolution (CYCBD) method is proposed to extract the fault feature frequency under strong noise background. In this method, particle swarm optimization (PSO) is used to optimize the filter length parameters for the CYCBD method. Firstly, the signal model is established based on the vibration characteristics of the fault bearing, and then the envelope spectrum fault feature ratio (FFR) is maximized by PSO algorithm. The optimal filter length parameter is input into the CYCBD method, and the envelope spectrum of the filtered signal is analyzed to extract the fault feature frequency. Finally, the proposed method is applied to the measured signal, and the efficiency and accuracy of fault feature extraction are improved compared with the traditional envelope spectrum analysis, which verifies the effectiveness of the method proposed.

Key words: fault diagnosis; spindle bearing of turboshaft engine; particle swarm optimization(PSO); second-order cyclostationarity blind deconvolution(CYCBD)

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