Abstract:
Condition based maintenance of machinery is being much talked about in the
engineering sector of defense and commercial industry. A lot of expenditure is generally
incurred on condition monitoring of machinery to avoid unexpected downtimes and
failures vis-à-vis optimizing machinery operation. The concept is ever evolving due to
technological advancements as well as with the emergence of unique nature of defects
in complex systems. The features of machinery health extracted through modern
condition monitoring technologies helps in diagnostics of current health; however,
utilizing the current data for prediction of future machinery state i.e Prognostics is a
challenging task.
Prognostic is one of the key elements of modern maintenance philosophies. Effective
prognostic, from the machinery data, leads towards operational reliability, reduced
machinery downtime, cost savings, secondary/catastrophic failures etc. Machinery
health prognosis follows a sequential methodology inclusive of various processes
ranging from data acquisition till remaining useful life estimation. Every step depicts
distinct statistical features, which are helpful in estimating present and future health
state of a machine. Various methodologies have been adopted by the researchers in
an effort to precisely forecast/predict machinery health. Research in this area, where
stochastic models have been applied, revealed encouraging results.
In this thesis, we have presented three nonlinear stochastic models with their
application on bearing health prognosis. These include Markov Switching Auto
Regressive Model with Time Varying Regime Probabilities, Threshold Auto Regressive
Model and Structural Break Point Classifier Model. The results showed that the applied
models can be effectively utilized for data driven machinery health prognosis.
