工业工程

工业工程 ›› 2013, Vol. 16 ›› Issue (2): 87-91.

• 专题论述 • 上一篇    下一篇

矩阵加权关联规则在故障诊断系统中的应用

  

  1. 1.燕山大学 经济管理学院，河北 秦皇岛 066004；2.燕山大学 图书馆，河北 秦皇岛 066004
  • 出版日期:2013-04-30 发布日期:2013-06-08
  • 作者简介:朱清香（1962-），女，浙江省人，教授，主要研究方向为设备故障诊断、管理信息系统及相关研究。
  • 基金资助:

    河北省自然科学基金资助项目(G2010001331)

Application of Matrix-Weighted Association Rule Mining  Algorithm to Fault Diagnosis

  1. 1. School of Economics and Management,  Yanshan University,  Qinhuangdao 066004, China;
    2. Library, Yanshan University, Qinhuangdao 066004, China
  • Online:2013-04-30 Published:2013-06-08

摘要： 关联规则挖掘算法实现了对复杂设备的通用、快速、脱离主观经验的故障诊断。经典的关联规则算法以各项目均匀分布为前提，而实际的故障诊断过程中不同的故障因素对故障诊断的贡献度不同。针对这种情况，将“最小支持期望”和矩阵引入关联规则，提出一种适用于设备故障诊断的基于矩阵的加权关联规则模型——MWARMA模型，实例证明该模型在提高挖掘效率的同时，明显提高了故障诊断的准确率。以该模型为基础设计并实现了一套设备故障诊断系统。

关键词: 故障诊断, 专家系统, 加权关联规则, 最小支持期望

Abstract: By the association rule mining algorithm, it can diagnose faults of complex equipment in a general and fast way without the need of subjective experience. The drawback is that the classical association rule algorithm requires that the frequency and importance of the items should be similar. However, in practical fault diagnosis applications, the contribution of each fault factor is different. To solve this problem, a new model called matrix-based weighted association rule mining algorithm suitable for equipment fault diagnosis is proposed by introducing minsupport expectation. Experiments show that the model improves the diagnostic efficiency while obviously increasing the accuracy of fault diagnosis. Then, an equipment fault diagnosis system is designed and implemented based on matrixbase weighted association rule mining algorithm (MWARMA) model.

Key words: equipment fault diagnosis, expert system, weighted association rule, minsupport expectation