Abstract:
ABSTRACT
The deterioration of the condition of a physical system that produces output
with linear relationship with the input can manifest in the data generated by
such system via change-points. As a result, timely detection and analysis of a
change-point in such systems form a significant element in providing
pragmatic solutions towards the smooth operation of the system. In this
regard, the thesis considered novel Variational Bayes methods for modeling,
detection, and inference of change-point in linear systems. In particular,
Variational Lower Bound Difference(VLBD), Variational Bayes Information
Criteria (VBIC), and Variational Akaike Information Criteria (VAIC) ratio-
based change-point detectors are developed for a single change-point detection
in linear systems. The methods are assessed with linear change-point datasets
in both simulation and real data of a refinery process, and their utility is
soundly illustrated. Interestingly, the Variational lower bound difference-
based detector shows robustness over its VBIC and VAIC counterparts in
situations where there exist multiple change-points. This was evidenced by the
real-data application.
