Model-Based Fault Diagnosis of a Planetary Gear Using Transmission Error

Jungho Park, Jong Moon Ha, Byeng D. Youn, Sang Hyuck Leem, Joo-Ho Choi, and Nam Ho Kim
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_14_051.pdf268.53 KBSeptember 17, 2014 - 9:42am

A Planetary gear can transmit high torque ratio stably and, therefore, the gear is widely used in industrial applications, i.e., wind turbines, automobiles, helicopters. Unexpected failure of the planetary gear results in substantial economic loss and human casualties. Extensive efforts have been made to develop the fault diagnostic techniques of gears; however, the techniques are mostly concerned about spur gears. This is mainly because understanding of complex dynamic behaviors of a planetary gear is lacking, such as multiple gear contacts, non-stationary axis of rotation, etc. This study thus proposes model-based fault diagnostics for a planetary gear that is based upon its dynamic analysis. Instead of vibration signals, this study uses transmission error (TE) signals for fault diagnostics of the planetary gear because TE signals (a) are directly related to the dynamic behaviors of gear mesh stiffness and (b) increase as damages on a gear mesh reduce the gear mesh stiffness. A lumped parameter model was used for modeling dynamic behaviors of the planetary gear. For more precise modeling, mesh phase difference โ€“ between sun, ring, and planet gear โ€“ and contact ratio were taken into account in the lumped parameter model. After acquiring TE signals from the model, order analysis and data processing were executed to generate health related data for the planetary gear. Consequently, it is concluded that the use of TE signals helps gain understanding of complex dynamic behaviors of the planetary gear and diagnose its potential faults.

Publication Year: 
2014
Publication Volume: 
5
Publication Control Number: 
051
Submission Keywords: 
fault diagnostics
Planetary Gear
Transmission Error
signal processing
Lumped Parameter Model
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
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