Confidence estimation of feedback information using dynamic bayesian networks

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. We studied the factors affecting CLFI, such as the 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(DBN) approach, Naïve Bayes model and Tree Augmented Naïve Bayes model. Our contribution includes an online confidence computation module for production equipments data and an algorithm to compute CLFI.