dc.contributor.authorHong, Liu
dc.date.accessioned2014-08-12T01:40:32Z
dc.date.accessioned2017-07-23T08:41:03Z
dc.date.available2014-08-12T01:40:32Z
dc.date.available2017-07-23T08:41:03Z
dc.date.copyright2014en_US
dc.date.issued2014
dc.identifier.citationHong, L. (2014). Modeling and analysis based techniques for fault diagnosis of planetary gearboxes. Doctoral thesis, Nanyang Technological University, Singapore.
dc.identifier.urihttp://hdl.handle.net/10356/61679
dc.description.abstractThis thesis proposes analytical models and monitoring methods based on these models to carry out fault diagnosis of planetary gearboxes and assist in developing robust fault alarm framework for early detection of gear failures. The proposed techniques can be applied not only to planetary wind turbine gearboxes but also to other general planetary gearboxes in varied engineering applications. The equally-spaced planetary gearbox is an important power-train component employed in many electro-mechanical engineering systems whose failures can result in significant capital losses and pose safety concerns. For example, a modern wind turbine is usually designed for a life span of over 20 years. Failure of its drive train components, such as the planetary gearbox and the generator, can leave the wind turbine out of service for long periods and therefore contribute to majority of the downtime and incurred maintenance costs over the entire lifetime of the wind turbine. Vibration based diagnostic techniques to detect gear tooth damage in gearboxes haven been widely studied. However, the complex nature of measured vibration spectra, resulting from rotating planet pinions with respect to the stationary sensor mounted on the gearbox housing, makes an effective implementation of these techniques as a challenge. The research presented in this thesis addresses the challenges of vibration based monitoring of planetary gearbox by (1) understanding the dynamic behavior of planetary gears and the amplitude modulation (AM) phenomena due to the relative motions of the planet pinions with respect to the stationary sensor through a lumped parameter modeling based approach, (2) identifying the location of the additional frequency components in measured vibration spectra resulting due to general gear faults, which are shown to depend on the geometry of the planetary gear-set and the gear carrying the local fault and (3) developing a fault alarm algorithm to diagnose incipient gear failure for industrial applications. The results are validated through dynamic simulations and experimental data from two planetary gearbox test rigs: a 4KW laboratory planetary gearbox test rig with the artificial seeded gear failure and a 750 kW tested gearbox for wind turbine application installed in a 2.5 MW dynamometer test facility at the National Renewable Energy Laboratory (NREL) at Golden, Colorado, US. The second facility is capable of providing static, highly accelerated life and model-in-the-loop tests. Both experiments collected vibration data through stationary accelerometers attached to the housing of the gearbox. The original contribution of the thesis include: (1) A mathematical AM-FM (amplitude modulated/frequency modulated) signal model developed to simulate the distinct spectrum of planetary gearboxes operating under healthy and faulty conditions; (2) Fourier series analysis based on this mathematical signal model that identifies the additional frequency components appearing due to local faults and describe the relationship between the location of these frequency components and the geometry of the planetary gear-set and (3) a new time domain approach that combines dynamic time warping and correlated kurtosis to detect gear faults and identify their location, thus assisting in the development of a robust fault alarm system.en_US
dc.format.extent149 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Mechanical engineering::Mechanics and dynamicsen_US
dc.titleModeling and analysis based techniques for fault diagnosis of planetary gearboxesen_US
dc.typeThesis
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.contributor.supervisorJaspreet Singh Dhupia
dc.description.degreeDOCTOR OF PHILOSOPHY (MAE)en_US
dc.identifier.doihttps://doi.org/10.32657/10356/61679


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