| The traditional approaches of industrial modeling, control and fault diagnosis are mainly based on analytical models. Those models are suitable for system that we known clearly how they work. When facing to complex, dynamic and nonlinear systems, it is usually hard to analysis the mechanism of processes and to build up mathematic models, or it will take us unbearable costs. In most situations a nonlinear system has special properties that are different from the others, so we can't find out just one or few methods suitable to model and control all those nonlinear systems. An experienced human operator may have little knowledge about a complex system but can still doing good job in system control and fault diagnosis by observing signals of inputs and outputs. Therefore, the problem is, can we use some techniques of machine learning and artificial intelligent to mimic the human ability of "learn to control".Rough set theory is a new mathematical and AI technique in knowledge discovery. Rough set information system is a rule based knowledge system. A rule based system does not require a classical mathematical description of the process, but consist of sets of If....Then.... rules instead. By rule based system we can understand industrial processes in a visualized, simple, understandable, humanistic and intelligent way. The aims of this dissertation include: try to solve problems in rough set based knowledge discovery and machine learning; build up knowledge model for complex industrial processes; following the concepts and approaches of nonlinear system control, construct a control system framework based on rough state space; apply rough set theory to fault detection and diagnosis (FDD).The main contributions of the dissertation are as follows:1 Reviewed the developments and research situation of rough set theory. Discussed the common approaches in industrial modeling, control and FDD. Investigations about implemental system and achievements in industrial applications of rough set theory have been done. The problems of rough set theory in industrial modeling control and FDD are pointed .2, To be an improvement of discretization method based on rough set theory and Boolean reasoning, algorithms for discretization in mixed realand discrete attributes system are proposed, functions based on information entropy are presented to analysis the results of discretization quantitatively, and the effects of discretization for knowledge acquisition are discussed.3 The concept of equivalence matrix, which expresses equivalence relation in rough set information system, is introduced; the relations between equivalence matrix and equivalence classes are discussed. The algorithms for data cleaning and rules extraction in knowledge system based on matrix computation are proposed and their complexity of computation is analyzed. For online learning of a rule-based knowledge system, strategies of rule updating in rough set information system and matrix computation algorithms for dynamic modification of rules are given.4 Based on definitions of nonlinear system methodology, rough set based state space model is proposed. Definitions of rough set state space are given. Stability, reachability, consistency and completeness of this new state space model are discussed in detail, respectively. A rule-based structure of Internal Model Control (IMC) system is given, and a rule-based IMC controller is built up.5 Approach for rough set based FDD is proposed, and its applicability and computational complexity is discussed. Knowledge about the faults of a process control system is obtained automatically by rough set reduction. An entropy-based criterion is used to measure the uncertainty of it. Methods of forward and backward fault diagnosis and how to build up decision tables for each fault source are given. Further more, approach for FDD based on rough set theory and Logical Fault Tree ( LFT) is presented. The feasibility of forming a knowledge discovering and intelligent decision-making system for these a...
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