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Mechanical systems and signal processing v.103, 2018년, pp.76 - 88   SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Amplitude-cyclic frequency decomposition of vibration signals for bearing fault diagnosis based on phase editing

Barbini, L. (The University of Bath Department of Mechanical Engineering, Claverton Down, Bath BA2 7AY, UK ) ; Eltabach, M. (Centre Technique des Industries Mécaniques, Félix-Louât, Senlis 60300, France ) ; Hillis, A.J. (The University of Bath Department of Mechanical Engineering, Claverton Down, Bath BA2 7AY, UK ) ; du Bois, J.L. (The University of Bath Department of Mechanical Engineering, Claverton Down, Bath BA2 7AY, UK ) ;
  • 초록  

    Abstract In rotating machine diagnosis different spectral tools are used to analyse vibration signals. Despite the good diagnostic performance such tools are usually refined, computationally complex to implement and require oversight of an expert user. This paper introduces an intuitive and easy to implement method for vibration analysis: amplitude cyclic frequency decomposition. This method firstly separates vibration signals accordingly to their spectral amplitudes and secondly uses the squared envelope spectrum to reveal the presence of cyclostationarity in each amplitude level. The intuitive idea is that in a rotating machine different components contribute vibrations at different amplitudes, for instance defective bearings contribute a very weak signal in contrast to gears. This paper also introduces a new quantity, the decomposition squared envelope spectrum, which enables separation between the components of a rotating machine. The amplitude cyclic frequency decomposition and the decomposition squared envelope spectrum are tested on real word signals, both at stationary and varying speeds, using data from a wind turbine gearbox and an aircraft engine. In addition a benchmark comparison to the spectral correlation method is presented. Highlights Method for analysis of vibration data. Separation bearing/gear signals. Only one parameter has to be selected by the user and the algorithm is computationally fast. Good candidate for industrial applications. Tested on not trivial experimental data and compared with spectral correlation.


  • 주제어

    Phase editing .   Diagnostics of defective bearings .   Spectral correlation .   Enhanced squared envelope spectrum .   Variable speed.  

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