Vibration based diagnosis to detect gear tooth damage in gearboxes has been studied widely and it can assist in scheduling maintenance and reducing capital losses that may result from gearbox failures. However, such vibration based techniques are difficult to implement in planetary gearboxes due to the complex nature of measured vibration spectrum resulting from rotating planets with respect to the stationary transducer mounted on the gearbox housing. Motor current signal analysis (MCSA) provides an alternative and non-intrusive way to detect mechanical faults through electrical signatures. So far, no investigation has been reported in literature to monitor a planetary gearbox in an electromechanical drive-train using MCSA because of the difficulties in modeling the planetary gear-set such as a large number of degrees of freedom and nonlinearity associated with tooth separations. In this paper, a lumped parameter model of an electro-mechanical drive-train has been developed, which consists of a permanent magnet synchronous machine (PMSM) connected to a load through a planetary gearbox. Afterwards, a seeded tooth defect is introduced into the electro-mechanical model to show that MCSA can successfully provide valuable diagnostic information regarding the planetary gearbox failure. Finally, the time waveform, as well as, the Fourier transform and Morlet wavelet transform of the PMSM stator current are presented to demonstrate the capability to detect the gear tooth fault and its severity in planetary gearbox using MCSA.
- Dynamic Systems and Control Division
Gear Fault Detection in Planetary Gearbox Using Stator Current Measurement of AC Motors
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Zhang, J, Hong, L, & Singh Dhupia, J. "Gear Fault Detection in Planetary Gearbox Using Stator Current Measurement of AC Motors." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperative and Decentralized Control; Dynamic System Modeling; Dynamical Modeling and Diagnostics in Biomedical Systems; Dynamics and Control in Medicine and Biology; Estimation and Fault Detection; Estimation and Fault Detection for Vehicle Applications; Fluid Power Systems; Human Assistive Systems and Wearable Robots; Human-in-the-Loop Systems; Intelligent Transportation Systems; Learning Control. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 673-680. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8609
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