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

• Published in: Mechanical systems and signal processing Vol. 103; pp. 76 - 88
• Main Authors: Barbini, L., Eltabach, M., Hillis, A.J., du Bois, J.L.
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 15.03.2018; Elsevier BV
• Subjects: Aircraft engines; Airplane engines; Amplitudes; Bearing strength; Decomposition; Defects; Diagnostic software; Diagnostic systems; Diagnostics of defective bearings; Enhanced squared envelope spectrum; Fault diagnosis; Gears; Phase editing; Rotating machinery; Signaling; Spectra; Spectral correlation; Studies; Variable speed; Vibration; Vibration analysis; Wind turbines; Phase editing; Enhanced squared envelope spectrum; Spectral correlation; Variable speed; Diagnostics of defective bearings
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2017.09.044

•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. 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.