基于时序关联规则的设备故障预测方法研究  

Equipment Fault Prognosis Based on Temporal Association Rules

作　　者：甘超[1] 陆远[1] 李娟[2] 胡莹[1] 邹博宇[1] 

机构地区：[1]南昌大学机电工程学院,江西南昌330031 [2]中国北方车辆研究所车辆传动重点实验室,北京100072

出　　处：《机床与液压》2014年第11期167-171,166,共6页Machine Tool & Hydraulics

基　　金：国家自然科学基金(50905083)

摘　　要：设备故障预测对于保证设备安全运行、提高设备管理效率具有重要的现实意义。考虑设备故障数据的特点,利用Apriori传统关联规则算法的思想,建立了时序故障数据模型。将故障数据转换为时序项集矩阵,针对该矩阵,提出了Apriori改进算法和频繁时序关联规则查找算法。利用这两个算法对设备故障数据进行频繁时序关联规则挖掘,预测设备故障趋势,为设备管理提供有力支持。并通过实例验证该方法的可行性。Equipment fault prognosis is significant for safeguarding the safe operation of equipment and improving the efficiency of equipment management.Temporal fault data model was built by using Apriori traditional association rules algorithm based on the characteristics of fault data.Improved Apriori algorithm and frequent temporal association rules algorithm were proposed by converting fault data to temporal item sets matrix.Equipment fault trends were predicted by mining the frequent temporal association rules of fault data based on the algorithm,which provided strong support for equipment management.At last an example was given to prove the feasi-bility.

关 键 词：故障预测 故障诊断 时序关联规则 数据挖掘 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]