Dynamic Bayesian network based prognosis in machining processes

被引：11

作者：

Dong M. ^{[1
]}

Yang Z.-B. ^{[1
]}

机构：

[1] Institute of Industrial Engineering and Management, Shanghai Jiaotong University

来源：

Journal of Shanghai Jiaotong University (Science) | 2008年 / 13 E卷 / 03期

关键词：

Dynamic Bayesian network (DBN); Prognosis; Remaining useful life;

D O I：

10.1007/s12204-008-0318-y

中图分类号：

学科分类号：

摘要：

Condition based maintenance (CBM) is becoming more and more popular in equipment maintenance. 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

页码：318 / 322

页数：4

共 11 条

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