Abstract
A fault-tolerant monitoring system for manufacturing process control is defined with sensor fusion for process-feature preparation and neural networks for process-feature analysis. Considering the complexity of operation mechanisms and the variability of process parameters in typical manufacturing environments, the author develops an automated fault-diagnosis method for the fault-tolerant monitoring system itself, called adaptive feature scaling, which is a modified version of the input feature scaling algorithm. In conjunction with clustering-type neural networks, adaptive feature scaling performs not only feature evaluation for self-diagnosis of process features, but also feature adjustment for improvement of the monitoring performance. In experimental evaluation, adaptive feature scaling proves superior to conventional approaches from the implementation point of view.
| Original language | English |
|---|---|
| Pages (from-to) | 200-209 |
| Number of pages | 10 |
| Journal | International Journal of Modelling and Simulation |
| Volume | 16 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 1996 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Modelling and Simulation
- Mechanics of Materials
- Hardware and Architecture
- Electrical and Electronic Engineering
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An automated fault diagnosis for manufacturing process monitoring and control. / Leem, Choon Seong.
In: International Journal of Modelling and Simulation, Vol. 16, No. 4, 01.01.1996, p. 200-209.Research output: Contribution to journal › Article
TY - JOUR
T1 - An automated fault diagnosis for manufacturing process monitoring and control
AU - Leem, Choon Seong
PY - 1996/1/1
Y1 - 1996/1/1
N2 - A fault-tolerant monitoring system for manufacturing process control is defined with sensor fusion for process-feature preparation and neural networks for process-feature analysis. Considering the complexity of operation mechanisms and the variability of process parameters in typical manufacturing environments, the author develops an automated fault-diagnosis method for the fault-tolerant monitoring system itself, called adaptive feature scaling, which is a modified version of the input feature scaling algorithm. In conjunction with clustering-type neural networks, adaptive feature scaling performs not only feature evaluation for self-diagnosis of process features, but also feature adjustment for improvement of the monitoring performance. In experimental evaluation, adaptive feature scaling proves superior to conventional approaches from the implementation point of view.
AB - A fault-tolerant monitoring system for manufacturing process control is defined with sensor fusion for process-feature preparation and neural networks for process-feature analysis. Considering the complexity of operation mechanisms and the variability of process parameters in typical manufacturing environments, the author develops an automated fault-diagnosis method for the fault-tolerant monitoring system itself, called adaptive feature scaling, which is a modified version of the input feature scaling algorithm. In conjunction with clustering-type neural networks, adaptive feature scaling performs not only feature evaluation for self-diagnosis of process features, but also feature adjustment for improvement of the monitoring performance. In experimental evaluation, adaptive feature scaling proves superior to conventional approaches from the implementation point of view.
UR - http://www.scopus.com/inward/record.url?scp=2942686556&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=2942686556&partnerID=8YFLogxK
U2 - 10.1080/02286203.1996.11760300
DO - 10.1080/02286203.1996.11760300
M3 - Article
VL - 16
SP - 200
EP - 209
JO - International Journal of Modelling and Simulation
JF - International Journal of Modelling and Simulation
SN - 0228-6203
IS - 4
ER -