Product defects diagnosis approach by integrating product / process unitary traceability data and expert knowledge

PhD student: 
Starting date: 
January 2013
Defense date: 
Thursday 10 December 2015

This thesis, which is part of the Traçaverre Project, aims to optimize the recall when the production process is not batch type with a unit traceability of produced items. The objective is to minimize the number of recalled items while ensuring that all items with defect are recalled. We propose an efficient recall procedure that incorporates possibilities offered by the unitary traceability and uses a diagnostic function. For complex industrial systems for which human expertise is not sufficient and for which we do not have a physical model, the unitary traceability provides opportunities to better understand and analyse the manufacturing process by a re-enactment of the life of the product through the traceability data. The integration of product and process unitary traceability data represents a potential source of knowledge to be implemented and operate. This thesis propose a data model for the coupling of these data. This data model is based on two standards, one dedicated to the production and the other dealing with the traceability. We developed a diagnostic function based on data after having identified and integrated the necessary data. The construction of this diagnosis function was performed by a learning approach and comprises the integration of knowledge on the system to reduce the complexity of the learning algorithm. In the proposed recall procedure, when the equipment causing the fault is identified, the health status of this equipment in the neighbourhood of the manufacturing time of the defective product is evaluated in order to identify other products likely to present the same defect. The global proposed approach was applied to two case studies. The first study focuses on the glass industry. The second case of application deals with the benchmark Tennessee Eastman process


KEYWORDS: Traceability, Diagnosis, Product recall, Bayesian Networks