A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based fra...

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Bibliographic Details
Main Author: Khawaja, Taimoor Saleem
Published: Georgia Institute of Technology 2010
Subjects:
Prognostics
PHM
CBM
Diagnostics
Classification
Anomaly detection
Regression
Support vector machines
Least squares
Fault location (Engineering)
Algorithms
Bayesian statistical decision theory
System failures (Engineering)
Online Access:http://hdl.handle.net/1853/34758

Internet

http://hdl.handle.net/1853/34758

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