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Durham e-Theses
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Exploiting Robust Multivariate Statistics and Data Driven Techniques for Prognosis and Health Management

GODWIN, JAMIE,LEIGH (2015) Exploiting Robust Multivariate Statistics and Data Driven Techniques for Prognosis and Health Management. Doctoral thesis, Durham University.

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This thesis explores state of the art robust multivariate statistical methods and data driven techniques to holistically perform prognostics and health management (PHM). This provides a means to enable the early detection, diagnosis and prognosis of future asset failures. In this thesis, the developed PHM methodology is applied to wind turbine drive train components, specifically focussed on planetary gearbox bearings and gears.

A novel methodology for the identification of relevant time-domain statistical features based upon robust statistical process control charts is presented for high frequency bearing accelerometer data. In total, 28 time-domain statistical features were evaluated for their capabilities as leading indicators of degradation. The results of this analysis describe the extensible multivariate “Moments’ model” for the encapsulation of bearing operational behaviour. This is presented, enabling the early degradation of detection, predictive diagnostics and estimation of remaining useful life (RUL).

Following this, an extended physics of failure model based upon low frequency SCADA data for the quantification of wind turbine gearbox condition is described. This extends the state of the art, whilst defining robust performance charts for quantifying component condition. Normalisation against loading of the turbine and transient states based upon empirical data is performed in the bivariate domain, with extensibility into the multivariate domain if necessary. Prognosis of asset condition is found to be possible with the assistance of artificial neural networks in order to provide business intelligence to the planning and scheduling of effective maintenance actions.

These multivariate condition models are explored with multivariate distance and similarity metrics for to exploit traditional data mining techniques for tacit knowledge extraction, ensemble diagnosis and prognosis. Estimation of bearing remaining useful life is found to be possible, with the derived technique correlating strongly to bearing life (r = .96)

Item Type:Thesis (Doctoral)
Award:Doctor of Philosophy
Keywords:PHM, Prognosis, Wind Turbine, Bearing, RUL, Diagnosis
Faculty and Department:Faculty of Science > Engineering and Computing Science, School of (2008-2017)
Thesis Date:2015
Copyright:Copyright of this thesis is held by the author
Deposited On:05 Jun 2015 09:41

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