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For industrial processes high availability and efficiency are important goals in plant operation. This thesis presents studies and development of tools for on-line treatment of process deterioration and model and sensor errors in order to achieve these goals. Deterioration of measurement devices, process components and process models has caused economical losses, plant failure and human losses. The development of on-line methods to prevent such losses is of special interest and has been conducted at The Department of Energy Technology, Mälardalen University. Important technological obstacles to implementing automatic on-line methods have been identified, such as data selection for adaptation and adaptation of data-driven models to new states.

A new method has been developed for decision support by combining artificial intelligence methods and heat and mass balance models, and concepts are proposed for decision support in order to detect developing faults and to conduct appropriate maintenance actions. The methods have been implemented in simulation environment and evaluated on real process data when available. The results can be sumarised as successful development of a decision support method on a steam turbine by combining artificial neural networks and Bayesian networks, and identification of important obstacles for automation of methods for adaptation of heat and mass balance process models and data-driven models when they are subject to deterioration.