Feature selection and sampling uncertainty analysis for variation sources identification in the assembly process online sensing

Feature selection and sampling uncertainty analysis for variation sources identification in the... The online sensing system provides the possibility for quick variation source identification of the assembly process. However, owing to the cost and time limit of the process, the sensor locations and sensor number for variation source identification are limited. The causal network method considering multi-source information is developed to assist identifying root causes of the dimension variation. Based on the proposed method, the diagnosis ability is evaluated, and then the minimal feature number and optimal measurement features are selected based on a sensor optimization algorithm. However, the random sampling uncertainty caused by insufficient sample size may affect the estimation accuracy of observation nodes and thus the misidentification rate of variation sources. By using Monte Carlo simulation, this paper evaluates sampling uncertainty of different sample size. Furthermore, by using probabilistic reasoning method with uncertain evidence, i.e., virtual evidence, the effect of sample size on the correct identification rate is analyzed. A dash panel case study is provided to illustrate the optimal feature selection procedures and the robustness to the sample uncertainty. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Advanced Manufacturing Technology Springer Journals

Feature selection and sampling uncertainty analysis for variation sources identification in the assembly process online sensing

Loading next page...
 
/lp/springer_journal/feature-selection-and-sampling-uncertainty-analysis-for-variation-R0EuizXClc
Publisher
Springer London
Copyright
Copyright © 2017 by Springer-Verlag London
Subject
Engineering; Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design
ISSN
0268-3768
eISSN
1433-3015
D.O.I.
10.1007/s00170-017-0361-7
Publisher site
See Article on Publisher Site

Abstract

The online sensing system provides the possibility for quick variation source identification of the assembly process. However, owing to the cost and time limit of the process, the sensor locations and sensor number for variation source identification are limited. The causal network method considering multi-source information is developed to assist identifying root causes of the dimension variation. Based on the proposed method, the diagnosis ability is evaluated, and then the minimal feature number and optimal measurement features are selected based on a sensor optimization algorithm. However, the random sampling uncertainty caused by insufficient sample size may affect the estimation accuracy of observation nodes and thus the misidentification rate of variation sources. By using Monte Carlo simulation, this paper evaluates sampling uncertainty of different sample size. Furthermore, by using probabilistic reasoning method with uncertain evidence, i.e., virtual evidence, the effect of sample size on the correct identification rate is analyzed. A dash panel case study is provided to illustrate the optimal feature selection procedures and the robustness to the sample uncertainty.

Journal

The International Journal of Advanced Manufacturing TechnologySpringer Journals

Published: Apr 10, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off