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

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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

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