Quality Monitoring and Fault Detection in an Automated Manufacturing System - a Soft Computing Approach

Quality monitoring and fault detection are essential parts in automated electronics manufacturing systems. Information about process conditions enables operations to improve quality and increase throughput. This report presents a general quality monitoring framework and method for a manufacturing system. Proposed monitoring approach is an integration of model-based methods with systematically collected expert knowledge and data. A model bank is constructed to reproduce behaviour of the normal and fault states. The data driven normal condition model contains linguistic equation non-linear scaling method for model variables, and a recursive gradient algorithm. Fuzzy reasoning and basic statistical methods are combined to identify changes in normal model residuals. Fault models are fuzzy rules for detecting abnormalities in selected time series signal. Analysed model outputs are then applied to monitoring task. Principles of the monitoring method are briefly discussed and demonstrated with a simulation example. Modelling results indicate that the proposed method can handle noise in simulation data. Generalisation ability of the normal model was also notified. Based on simulations, presented monitoring approach was verified to have potential features to be implemented as a real time application. The work is part of the INTELE Intelligent Methods in Electronics Manufacturing research project, financially supported by Tekes, Filtronic LK Oy, JOT Automation Oy and PKC Group Oyj. INTELE project belongs to Tekes technology programme ÄLY Intelligent Automation Systems 2001-2004.