Probabilistic Machine Learning Could Eliminate No Fault Found

被引：0

作者：

Teixeira, Rodrigo E. ^{[1
]}

Morris, Kari E. ^{[1
]}

Sautter, F. Christian ^{[1
]}

机构：

[1] UAH Res Inst, Reliabil & Failure Anal Lab, Huntsville, AL 35899 USA

来源：

PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON THROUGH-LIFE ENGINEERING SERVICES | 2015年 / 38卷

关键词：

Vibration Monitoring; Diagnostics; No Fault Found; Maintenance Support Tool; Probabilistic Inference;

D O I：

10.1016/j.procir.2015.08.092

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Many Condition Indicators have been implemented, yet success has been limited owing to their sensitivity to artifacts that invariably corrupt vibration measurements under real-life operations. Here we report a novel approach based on a stochastic non-linear fault evolution model. This probabilistic machine learning algorithm estimates fault magnitudes and probabilities, which were compared to component removals validated by tear down analyses, and achieved a 94% consistency rate over all available data thanks to excellent artifact rejection. This novel maintenance support tool can detect hidden conditions early while virtually eliminating NFF (false positives). Published by Elsevier B.V.

引用

页码：124 / 128

页数：5

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