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针对数控机床旋转部件故障信息的特点,提出了系统仿真与数据挖掘相结合的综合故障诊断方法。文中将旋转部件故障归为4类,提出了一种改进的Apriori算法,结合粗糙集理论的数据挖掘方法,通过实例证明了两者应用在数控机床系统旋转部件故障诊断中,可以提高数据挖掘的速度。分析了基于仿真的故障诊断方法,在此基础上,结合仿真与数据挖掘各自在故障诊断方面的优势,进一步提出了基于仿真与数据挖掘的综合诊断方法,给出了诊断的方法流程图。
According to the characteristics of the fault information of the rotating parts of CNC machine tools, a comprehensive fault diagnosis method combining system simulation with data mining is proposed. In this paper, the rotating parts failures are classified into four categories. An improved Apriori algorithm is proposed. Combined with the data mining method of rough set theory, an example is given to prove that the two are applied in the fault diagnosis of rotating parts in CNC machine tools, which can improve the data mining speed. Based on the above analysis, the advantages of simulation and data mining in fault diagnosis are analyzed respectively. Based on the analysis, the integrated diagnosis method based on simulation and data mining is proposed and the flow chart of diagnosis method is given.