Random Set Of The Uncertainty Of Information Processing Methods And Applications In System Reliability Evaluation And Fault Diagnosis

Because of the complexity of environments, limitation of sensor performance and imperfection of information acquisition technique, multisoures information reflecting system reliability and faults is usually uncertain (such as random, imprecise, vague and incomplete). These uncertain information is described and modeled by the corresponding knew theories under certain assumptions. Moreover, the more diverse the ways and means of information acquisition are, the more complex and various the types of information are. In this case, the limitations of knew theories become more and more unnegligible. For several years, researchers have explored the unification of theories dealing with different uncertainties of information and have finally considered random set theory, because it can unifies several uncertain theories such as probability theory, fuzzy set theory, Dempster-Shafer evidence theory, possibility Theory, rough set Theory. This doctoral dissertation is devoted to study on the random set methods of unified describing and modeling different-types uncertain information. Based on which, for specific objects in application such as circuit system, electromechanical equipments, some new methods are generated using random set theory and knew theories comprehensively. They can be used in reliability evaluation and fault diagnosis of specific system objects and are better than knew methods.The main works in the thesis are introduced as follows:1. Firstly, the available information is classified according to different uncertain types. The methods of uncertain information processing are reviewed in reliability evaluation and fault diagnosis. Their shortcomings are analyzed. We introduce the random set theory as a possible framework for unification and detail how the individual theories can fit in this framework.Based on this research, other works are developed as follow2. Based on the random set description of random variables and the extension principles of random set, a simple and flexible method is given for evaluating reliability of circuit performance by virtue of the proposed probability model of circuit system performance evaluation. This method is alternative to Monte-Carlo analysis, but reduces the number of calculations required drastically.3. On the basis of random set representation of Dempster combination rule, the proposed extended set-valued mapping and joint conditional probability of random sets, a unified random set model of classical combination rules is presented. By use of this model, a new combination rule is constructed for overcoming a class of counterintuitive phenomena. The example of fault diagnosis using conflict evidence is given to show the effectiveness of new rule.4. Based on the random set description of fuzzy set and the likelihood function of random set, new method is proposed to obtain fuzzy evidence from fuzzy fault features, and then, Dempster combination rule are used to fusion several pieces of fuzzy evidence to get diagnosis results. The proposed method of fuzzy evidence extraction can reduces uncertainties in fusion-makings and improves fault identifications. It is better than the traditional single-feature fault diagnosis and fusion diagnosis method based on single observation data matching. Finally, the diagnosis results of machine rotor show that the proposed method can enhance diagnostic accuracy and reliability.