A data mining approach for intelligent equipment fault diagnosis

Abstract

Mechanical and electrical equipments are widely used in industry. Existing electro-hydraulic mixing equipments mainly use expert systems for fault diagnasis. However, in order to increase the accuracy of diagnasis, the expert systems have to acquire more knowledge. And diagnosis system will bring great uncertainty due to limited knowledge. Furthermore, existing fault diagnosis system has the disadvantages of low efficiency of analyzing data, bad fault-tolerance, and this may lead to wrong diagnosis results. In this paper, Decision Tree algorithm of data mining technology is used in the area of equipment fault diagnosis, and discuss and study the method of equipment intelligent fault diagnosis based on data mining technology. As a result, this method can compensate for the limitations of knowledge acquisition of expert system and enhance the accuracy of fault diagnosis.