Fault diagnosis model based on rough set theory and expert system

In order to improve diagnosis precision and decreasing misinformation diagnosis, according to the intelligence complementary strategy, a new intelligent fault diagnosis method based on rough sets theory and expert system is presented. Firstly, basis on data pretreatment, the fault diagnosis decision table is formed, and continuous datum are discredited by using clustering method. Rough sets theory as a new mathematical tool is used to deal with inexact and uncertain knowledge for pattern recognition. The target is mainly to remove redundant information and seek for reduced decision tables which to obtain the minimum fault feature subset. Expert system is that owns independent knowledge base to make knowledge maintenance more convenient and have easy reasoning process to explain.