基于FP-Growth算法的数控机床故障特征分析

Fault Feature Analysis for CNC Machine Tools Based on FP-Growth Algorithm

数控机床故障问题的及时响应、故障原因精准判断以及解决方案的快速识别是实现制造单元智能化的关键。基于数控机床历史运维数据,采用关联规则挖掘FP-Growth算法实现数控机床故障模式、原因的关联规则挖掘。首先对运维数据进行故障特征分析,构建基于关联规则的故障诊断模型;其次,通过对历史故障模式及其相应故障原因进行挖掘,生成故障诊断关联规则;最后结合关联规则的支持度、置信度等评估指标进行分析,并推演出关联概率的大小,验证了FP-Growth算法用于数控机床故障特征分析的可行性和合理性。

The key to realize intelligent manufacturing cell is the timely response of CNC machine fault,accurate judgment of fault causes and rapid identification of solutions.Based on the historical operation and maintenance data of CNC machine tools,association rules mining FP-Growth algorithm was adopted,and association rules mining of fault modes and causes of CNC machine tools was realized.The fault characteristics of some historical operation and maintenance data were analyzed.A fault diagnosis model based on association rules was constructed.By mining the historical fault modes and corresponding fault causes,the specific fault diagnosis association rules were obtained.Finally,the feasibility and rationality of the FP-Growth algorithm for fault feature analysis of CNC machine tools were verified by analyzing the evaluation indexes such as support degree and confidence degree of association rules,and deducing the magnitude of association probability.