The objective of this thesis is to design and develop a knowledge base model to support the maintenance system structure. The aim of this model is to identify the failure modes which are the heart of maintenance system through the functional analysis and then serves as adecision support system to define the maintenance tasks and finally to implement a preventive maintenance task. This knowledge base management system is suitable to design and develop maintenance system since it encompasses all necessary steps in maintenance area. Moreover, it is capable of being integrated with other knowledge base systems. The structure and the environment of this knowledge base system is flexible to allow users to deploy different kinds of tools which they will. It is also a well structured approach to develop, debug, upgrade and trace. In this thesis, the CommonKADS methodology is used as the knowledge base methodology. At the first step, a knowledge base system is developed to create the maintenance system infrastructure. To implement this, Reliability-Centered Maintenance (RCM) has been chosen as the method to design a maintenance system. In order to make it more specified, a Spindlesubsystem is taken as a case study to make the model clearer. Secondly, another knowledge base system is developed for decision making process to select the suitable maintenance task and finally, a general knowledge base model is developed for condition-based monitoring onSpindle. In chapter 1, background, previous works and gap analysis have been surveyed. Then in chapter 2 the methodology and tools have been discussed and described. Design and development the knowledge base for maintenance system is described in detail in chapter 3 and finally in chapter 4, the conclusion and the future works are presented.

Video database applications call for flexible and powerful modeling and querying facilities, which require an integration or interaction between database and knowledge base technologies. It is also necessary for many real life video database applications to incorporate uncertainty, which naturally occurs due to the complex and subjective semantic content of video data. In this thesis study, firstly, a fuzzy conceptual data model is introduced to represent the semantic content of video data. UML (Unified Modeling Language) is utilized and extended to represent uncertain information along with video specific properties at the conceptual level. Secondly, an intelligent fuzzy object-oriented database framework is presented for video database applications. The introduced fuzzy conceptual model is mapped to the presented framework, which is an adaptation of the previously proposed IFOOD architecture. The framework provides modeling and querying of complex and rich semantic content and knowledge of video data including uncertainty. Moreover, it allows (fuzzy) semantic, temporal, (fuzzy) spatial, hierarchical, regional and trajectory queries, based on the video data model. We think that the presented conceptual data model and framework can be adapted to any application domain related to video databases.