Dynamic Bayesian Network Based Prognosis in Machining Processes

被引：0
|
作者：
董明
杨志波
机构：
[1] Institute of Industrial Engineering and Management Shanghai Jiaotong University
[2] Institute of Industrial Engineering and Management Shanghai Jiaotong University
[3] Shanghai 200240 China
来源：
JournalofShanghaiJiaotongUniversity(Science) | 2008年 / 03期
关键词：
dynamic Bayesian network (DBN); prognosis; remaining useful life;
D O I：
暂无
中图分类号：
TP183 [人工神经网络与计算]; TP274 [数据处理、数据处理系统];
学科分类号：
0804 ; 080401 ; 080402 ; 081002 ; 0835 ;
摘要：
Condition based maintenance (CBM) is becoming more and more popular in equipment main-tenance. A prerequisite to widespread deployment of CBM technology and practice in industry is effective diagnostics and prognostics. A dynamic Bayesian network (DBN) based prognosis method was investigated to predict the remaining useful life (RUL) for an equipment. First, a DBN based prognosis framework and specific steps for building a DBN based prognosis model were presented. Then, the corresponding inference algorithms for DBN based prognosis were provided. Finally, a prognosis procedure based on particle filtering algorithms was used to predict the RUL of drill-bits of a vertical drilling machine, which is commonly used in industrial process. Preliminary experimental results are promising.
引用
收藏
页码：318 / 322
页数：5
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