Vibration Based Diagnosis for Planetary Gearboxes Using an Analytical Model

Liu Hong, Yongzhi Qu, Yuegang Tan, Mingyao Liu, Zude Zhou

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


The application of conventional vibration based diagnostic techniques to planetary gearboxes is a challenge because of the complexity of frequency components in the measured spectrum, which is the result of relative motions between the rotary planets and the fixed accelerometer. In practice, since the fault signatures are usually contaminated by noises and vibrations from other mechanical components of gearboxes, the diagnostic efficacy may further deteriorate. Thus, it is essential to develop a novel vibration based scheme to diagnose gear failures for planetary gearboxes. Following a brief literature review, the paper begins with the introduction of an analytical model of planetary gear-sets developed by the authors in previous works, which can predict the distinct behaviors of fault introduced sidebands. This analytical model is easy to implement because the only prerequisite information is the basic geometry of the planetary gear-set. Afterwards, an automated diagnostic scheme is proposed to cope with the challenges associated with the characteristic configuration of planetary gearboxes. The proposed vibration based scheme integrates the analytical model, a denoising algorithm, and frequency domain indicators into one synergistic system for the detection and identification of damaged gear teeth in planetary gearboxes. Its performance is validated with the dynamic simulations and the experimental data from a planetary gearbox test rig.

Original languageEnglish (US)
Article number2620518
JournalShock and Vibration
StatePublished - 2016
Externally publishedYes

Bibliographical note

Funding Information:
The authors are pleased to acknowledge the financial support of National Natural Science Foundation of China (Grant no. 51505353) and the Fundamental Research Funds for the Central Universities (WUT: 163104002)

Publisher Copyright:
© 2016 Liu Hong et al.


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