A self-learning system and its application in fault diagnosis

In this paper, a self-learning system based on objectives used in fault diagnosis expert systems is presented. It depends on the deep knowledge model of the diagnosed system and can improve diagnostic capability by expanding and satisfying the shallow knowledge base. Algorithms and principle of the self-learning system are described in detail. As an application, the self-learning system has been embedded in a coal-cutter fault diagnosis expert system