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 framework for fault
diagnosis and failure prognosis in nonlinear, non-Gaussian systems. The methodology
assumes the availability of real-time process measurements, definition of a set
of fault indicators, and the existence of empirical knowledge (or historical data) to
characterize both nominal and abnormal operating conditions.
An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm,
set within a Bayesian Inference framework, not only allows for the development of
real-time algorithms for diagnosis and prognosis but also provides a solid theoretical
framework to address key concepts related to classication for diagnosis and regression
modeling for prognosis. SVM machines are founded on the principle of Structural
Risk Minimization (SRM) which tends to nd a good trade-o between low empirical
risk and small capacity. The key features in SVM are the use of non-linear kernels,
the absence of local minima, the sparseness of the solution and the capacity control
obtained by optimizing the margin. The Bayesian Inference framework linked with
LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis.
Additional levels of inference provide the much coveted features of adaptability
and tunability of the modeling parameters.
The two main modules considered in this research are fault diagnosis and failure
prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed
scheme uses only baseline data to construct a 1-class LS-SVM machine which,
when presented with online data, is able to distinguish between normal behavior and
any abnormal or novel data during real-time operation. The results of the scheme
are interpreted as a posterior probability of health (1 - probability of fault). As
shown through two case studies in Chapter 3, the scheme is well suited for diagnosing
imminent faults in dynamical non-linear systems.
Finally, the failure prognosis scheme is based on an incremental weighted Bayesian
LS-SVR machine. It is particularly suited for online deployment given the incremental
nature of the algorithm and the quick optimization problem solved in the LS-SVR
algorithm. By way of kernelization and a Gaussian Mixture Modeling (GMM)
scheme, the algorithm can estimate (possibly) non-Gaussian posterior distributions
for complex non-linear systems. An efficient regression scheme associated with the
more rigorous core algorithm allows for long-term predictions, fault growth estimation
with confidence bounds and remaining useful life (RUL) estimation after a fault
is detected.
The leading contributions of this thesis are (a) the development of a novel Bayesian
Anomaly Detector for efficient and reliable Fault Detection and Identification (FDI)
based on Least Squares Support Vector Machines , (b) the development of a data-driven
real-time architecture for long-term Failure Prognosis using Least Squares Support
Vector Machines,(c) Uncertainty representation and management using Bayesian
Inference for posterior distribution estimation and hyper-parameter tuning, and finally (d) the statistical characterization of the performance of diagnosis and prognosis
algorithms in order to relate the efficiency and reliability of the proposed schemes.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/34758 |
| Date | 21 July 2010 |
| Creators | Khawaja, Taimoor Saleem |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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