| 改进多重最小支持度关联规则算法在故障诊断中的应用 |
| |
| 引用本文： | 刘晶,季海鹏,朱清香. 改进多重最小支持度关联规则算法在故障诊断中的应用[J]. 工业工程, 2010, 13(4): 108-111 |
| |
| 作者姓名： | 刘晶 季海鹏 朱清香 |
| |
| 作者单位： | 燕山大学,图书馆,河北,秦皇岛,066004;燕山大学,机械工程学院,河北,秦皇岛,066004;燕山大学,经管学院,河北,秦皇岛,066004 |
| |
| 基金项目： | 河北省自然科学基金资助项目 |
| |
| 摘 要： | Apriori算法的前提是数据库中各项目的频率和重要性是相同或者相似的,但在故障诊断的实际应用中并非如此。在Apriori算法的基础上进行改进,利用多重最小支持度解决了设备故障诊断中非频繁项目的挖掘;同时针对在实际的应用中项目集的重要程度不一致的问题,提出一种基于组件信誉值的加权多重最小支持度算法,并通过实际的例子证明了该算法在故障诊断中的正确性和有效性。
|
| 关 键 词： | 多重最小支持度 关联规则 组件信誉值 故障诊断 |
Revised Multiple Minimum Support Associative Rules for Fault Diagnosis |
| |
| Liu Jing,Ji Hai-peng,Zhu Qing-xiang. Revised Multiple Minimum Support Associative Rules for Fault Diagnosis[J]. Industrial Engineering Journal, 2010, 13(4): 108-111 |
| |
| Authors: | Liu Jing Ji Hai-peng Zhu Qing-xiang |
| |
| Affiliation: | Liu Jing1,Ji Hai-peng2,Zhu Qing-xiang3(1.Libary of YanShan University,Qinhuangdao 066004,China,2.College of mechanical engineering,YanShan University,3.School of Economics and Management,China) |
| |
| Abstract: | When Apriori algorithm is adopted in data mining,it requires that the frequency and importance of the items should be similar.This is not true in fault diagnosis applications.In this paper,the Apriori algorithm is revised for equipment fault diagnosis by using weighted multiple minimum support associative rules.An example is presented to show the correctness and effectiveness of the proposed algorithm. |
| |
| Keywords: | multiple minimum support associative rules credit component values fault diagnosis |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
