A model for reasoning about persistence and causation
Computational Intelligence
Machine Learning - Special issue on learning with probabilistic representations
An architecture for control and monitoring of discrete events systems
Computers in Industry - Special issue: ASI'96: life cycle approaches to production systems: management, control and supervision
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
A Comparison of Bayesian and Belief Function Reasoning
Information Systems Frontiers
Naive Bayes models for probability estimation
ICML '05 Proceedings of the 22nd international conference on Machine learning
Bayesian Networks Implementation of the Dempster Shafer Theory to Model Reliability Uncertainty
ARES '06 Proceedings of the First International Conference on Availability, Reliability and Security
Bayesian Network Learning with Parameter Constraints
The Journal of Machine Learning Research
Naive Bayes Classification of Uncertain Data
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Learning Naive Bayes Classifiers with Incomplete Data
AICI '09 Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence - Volume 04
Distributed diagnosis of discrete-event systems using Petri nets
ICATPN'03 Proceedings of the 24th international conference on Applications and theory of Petri nets
Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas
Engineering Applications of Artificial Intelligence
Logic control law design for automated manufacturing systems
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
This paper proposes an estimation method for the confidence level of feedback information (CLFI), namely the confidence level of reported information in computer integrated manufacturing (CIM) architecture for logic diagnosis. This confidence estimation provides a diagnosis module with precise reported information to quickly identify the origin of equipment failure. We studied the factors affecting CLFI, such as measurement system reliability, production context, position of sensors in the acquisition chains, type of products, reference metrology, preventive maintenance and corrective maintenance based on historical data and feedback information generated by production equipments. We introduced the new 'CLFI' concept based on the Dynamic Bayesian Network approach and Tree Augmented Naive Bayes model. Our contribution includes an on-line confidence computation module for production equipment data, and an algorithm to compute CLFI. We suggest it to be applied to the semiconductor manufacturing industry.