Analysis of Attribute Reduction of Rough Set and CNC Machine Fault Diagnosis Based on Particle Swarm

Abstract

After reasoning and calculation, fault diagnosis can automatically identify the causes of malfunction based on the fault symptoms, which is the core task of fault diagnosis. This paper applies the particle swarm algorithm and rough set to the fault diagnosis, and proposes fault diagnosis knowledge acquisition, rules optimization and fault identification based on rough set attribute reduction of particle swarm. Firstly, this paper introduces the rough set attribute reduction. Secondly, the particle swarm algorithm is applied to the rough set attribute reduction algorithm. Finally, the correctness and superiority of this algorithm are proved from the reduction experimental results of related data sets.