Condition-Based Maintenance (CBM) technology increases system availability and safety while reducing costs, attributed to reduced maintenance and inventory, increased capacity, and enhanced logistics and supply chain performance. Employing effective generic process monitoring methods for abrupt failures and diagnostics and prognostics algorithms for incipient failures is an important prerequisite for widespread deployment of CBM. Diagnostics is the process of identifying, localizing and determining severity of a machine failure, whereas prognostics is the process of estimating the remaining-useful-life (RUL). This work presents methods based on support vector machines and hidden Markov models to diagnose abrupt and incipient failures and to estimate the RUL. The presented methods have the ability to handle non- stationary processes. There exist three major goals of this work: detecting abrupt failures (process monitoring), identifying the state of incipient failures (health state estimation) and estimating RUL of the machine (prognostics).

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