Research On Rough Set Theory & Application In Engineering And Medical Diagnosis

Up to now, fault diagnosis system is still facing various problems such as the limitation of supporting theories, the difficulties in obtaining and renewing the rules, non-synchronization of renewing processes of the diagnostic models. Therefore in this paper, we use the rough set theory as basic supporting theory and put forward a knowledge finding model. This model is then applied to engineering and medical diagnostic areas respectively, to find some meaningful diagnostic knowledge.To simplify the complexity of calculation on rough set theory, we bring forward a spatial-dimensions-reducing model named as mixed attribute whole distance (MAWD). This model is suitable for analysis of the data where both the continuous and discrete attributes are coexisted. We find that MAWD model can be used quantitatively for dealing with the complexity of calculation processes of rough set theory and improve the calculation efficiency of rough set theory.To solve the problem of obtaining fault diagnosis rules, the MAWD model, the variable precision rough set (VPRS) model, the advanced continuous attribute IMDV-SOM heuristic automatic clustering model and the decision matrix calculation model were integrated in this paper and a new knowledge discovery model called MMS-VPRS (MAWD-MDV-SOM-Variable Precision Rough Set) was presented for the first time. This MMS-VPRS model was programmed to provide a concise, high efficiency way for the mining of fault diagnosis rules.To validate the applicability of the MMS-VPRS knowledge discovery model in engineering, and to find out useful diagnosis rules for practical fault diagnosis, we took rotating mechanical devices fault diagnosis, injection molding quality control, and electron ballast fault diagnosis as practical application examples. All these application studies had a good result and some useful diagnostic rules in engineering fields are discovered.For studying the updating of fault diagnosis models, the MMS-VPRS knowledge discovery model was introduced into medical diagnosis field. This model was used to analyze all-around clinical information in 51 patients suspected of having coronary heart disease. The obtained diagnostic results were well matched with that made by medical experts, with high sensitivity and specificity. Thus, this model could be helpful to diagnose coronary heart disease at its early stage.In conclusion, this paper puts forward a spatial-dimensions-reducing model called MAWD, which was then combined with several other models to create MMS-VPRS, a new knowledge discovery model. The validity of MMS-VPRS model has been confirmed in engineering field and in medical diagnostic process.