Journal article
A Bayesian variable selection method for joint diagnosis of manufacturing process and sensor faults
IIE transactions, Vol.48(4), pp.313-323
04/02/2016
DOI: 10.1080/0740817X.2015.1109739
Abstract
This article presents a Bayesian variable selection-based diagnosis approach to simultaneously identify both process mean shift faults and sensor mean shift faults in manufacturing processes. The proposed method directly models the probability of fault occurrence and can easily incorporate prior knowledge on the probability of a fault occurrence. Important concepts are introduced to understand the diagnosability of the proposed method. A guideline on how to select the values of hyper-parameters is given. A conditional maximum likelihood method is proposed as an alternative method to provide robustness to the selection of some key model parameters. Systematic simulation studies are used to provide insights on the relationship between the success of the diagnosis method and related system structure characteristics. A real assembly example is used to demonstrate the effectiveness of the proposed diagnosis method.
Details
- Title: Subtitle
- A Bayesian variable selection method for joint diagnosis of manufacturing process and sensor faults
- Creators
- Shan Li - University of IowaYong Chen - University of Iowa
- Resource Type
- Journal article
- Publication Details
- IIE transactions, Vol.48(4), pp.313-323
- Publisher
- Taylor & Francis
- DOI
- 10.1080/0740817X.2015.1109739
- ISSN
- 0740-817X
- eISSN
- 1545-8830
- Language
- English
- Date published
- 04/02/2016
- Academic Unit
- Industrial and Systems Engineering
- Record Identifier
- 9984186951502771
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