Cyclostationary modeling for local fault diagnosis of planetary gear vibration signals. (14th April 2020)

• Record Type: Journal Article
• Authors: Sun, Ruo-Bin; Yang, Zhi-Bo; Gryllias, Konstantinos; Chen, Xue-Feng
• Is Part Of: Journal of sound and vibration. Volume 471(2020)
• Publication Date: 2020-04-14
• Subjects: Cyclostationary modeling -- Self-adaptive noise cancellation -- Cyclic spectral coherence -- Planetary gear fault diagnosis; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2020.115175
• Languages: English
• ISSNs: 0022-460X

Abstract

Abstract: Owing to the rotation and reciprocation of mechanical equipment, their vibration signals inherently exhibit cyclic stationary characteristics. This paper provides a phenomenological signal model of the planetary gear vibrations under the cyclostationarity paradigm. The model is helpful to give comprehensive demodulation information which is the limit of classic spectral analysis of deterministic signals, so that the diagnosis of local faults in planetary gear sets can be more explicit. First, the effects of rotating speed fluctuation on periodic gear meshing vibrations are analyzed. It is shown that the stationary speed fluctuation will turn the periodicity of signals into second-order cyclostationarity. Next, an impulsive oscillation sequence with interval timing jitter is used to describe the dynamic response of the meshing stiffness change caused by local defects. Meanwhile, the influence of a vibration transfer path is also considered in the model. Based on the above analysis, the following procedure is utilized to analyze planetary gearbox vibrations: remove the periodic components by the self-adaptive noise cancellation method and then, apply the second-order frequency descriptor to reveal fault features of the signals. Both the simulation and experiment are used to validate the correctness of model derivation and the superiority of the cyclostationary analysis. Highlights: A cyclostationary phenomenological signal model of the planetary set is proposed. Periodic components are removed by the self-adaptive noise cancellation method. Modulation features are revealed by the second-order cyclostationary descriptor. The model is verified by the numerical and experimental analysis.