Railway Large Maintenance Machinery Failure Diagnosis Attributes Reduction Based on Rough Sets
Zhi Xiong Song, Hong Gang Zhu, Xing Xing Li · Applied Mechanics and Materials · 2013
A description of the Railway Large Maintenance Machinery (hereinafter referred to the "RMM") failure is presented and the basic principle of the rough sets and attributes reduction is analyzed. The long-distance clustering method is used to transform continuous attributes values to discrete attributes values. The constructor method of distinguish matrix, and the case study of the RMM diesel engine failure detection attributes reduction is given. Distinguish matrix is constructed to achieve attributes reduction, failure diagnosis based on rough set is completed successfully.