An automated fault diagnosis for manufacturing process monitoring and control

Research output: Contribution to journalArticle

4 Citations (Scopus)

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 languageEnglish
Pages (from-to)200-209
Number of pages10
JournalInternational Journal of Modelling and Simulation
Volume16
Issue number4
DOIs
Publication statusPublished - 1996 Jan 1

Fingerprint

Process Monitoring
Process monitoring
Fault Diagnosis
Process Control
Failure analysis
Process control
Manufacturing
Scaling
Fault-tolerant Systems
Monitoring
Monitoring System
Neural networks
Neural Networks
Performance Monitoring
Sensor Fusion
Fusion reactions
Process Parameters
Experimental Evaluation
Preparation
Adjustment

All Science Journal Classification (ASJC) codes

  • Software
  • Modelling and Simulation
  • Mechanics of Materials
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

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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 journalArticle

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