The paper proposes a diagnosis approach corresponding to the specific MES level to provide information on the origins of a performance indicator degradation. Our key distribution is the proposal of a set of potential causes that may impact the successful completion of production operations, such as the operator stress, quality of material, equipment or recipe change and their characteristic parameters by exploiting MES historical database. We use Bayesian Network model to diagnose the potential failure causes and support effective human decisions on corrective actions (maintenance, human resource planning, recipe re-qualification, etc) by computing conditional probabilities for each suspected proposed causes.
How to Cite
Manufacturing Execution System, Fault Diagnosis, Bayesian Network, OEE, decision support
Ashori, A., & Nourbakhsh, A. (2008). Effect of press cycle time and resin content on physical and mechanical properties of particleboard panels made from the underutilized low-quality raw materials. Industrial crops and products, 28(2), 225–230.
Bouaziz, M. F., Zamai, E., Duvivier, F., & Hubac, S. (2011). Dependability of complex semiconductor systems: Learning bayesian networks for decision support. In Dependable control of discrete systems (dcds), 2011 3rd international workshop on (pp. 7–12).
Cacciabue, P. C. (2004). Human error risk management for engineering systems: a methodology for design, safety assessment, accident investigation and training. Reliability Engineering & System Safety, 83(2), 229–240.
Chen, A., & Wu, G. (2007). Real-time health prognosis and dynamic preventive maintenance policy for equipment under aging markovian deterioration. International Journal of Production Research, 45(15), 3351–3379.
Duong, Q.-B., Zamai, E., & Tran-Dinh, K.-Q. (2013). Confidence estimation of feedback information for logicdiagnosis. Engineering Applications of Artificial Intelligence, 26(3), 1149–1161.
Genrich, H. J., Hanisch, H.-M., & W¨ollhaf, K. (1994). Verification of recipe-based control procedures by means of predicate/transition nets. In Application and theory of petri nets 1994 (pp. 278–297). Springer.
Hohmann, C. (2011). Techniques de productivité: Comment gagner des points de performance-pour les managers et les encadrants. Editions Eyrolles.
Hubac, S., & Zamai, E. (2013). Politiques de maintenance equipment en flux de production stressantequipment maintenance policy in stressed manufacturing flow (technology or product). Edition TI (Technique de lingenieur)[AG 3535].
IEC, N. (2003). 62264-1. Intégration du syst`eme de conduite d’entreprise-Partie, 1. International Labour Conference, r. s. (2005). Hours of work: from fixed to flexible? International Labour Office.
Lan, P., Ji, Q., & Looney, C. G. (2003). Non-intrusive real time human fatigue modelling and monitoring. In Proceedings of the human factors and ergonomics society annual meeting (Vol. 47, pp. 311–315).
McCulloch, K., Baker, A., Ferguson, S., Fletcher, A., & Dawson, D. (2007). Developing and implementing a fatigue risk management system. Transport Canada: Canada.
Meyer, H., Fuchs, F., & Thiel, K. (2009). Manufacturing execution systems (mes): Optimal design, planning, and deployment: Optimal design, planning, and deployment. McGraw Hill Professional.
Nguyen, D. T., Duong, Q. B., Zamai, E., & Shahzad, M. K. (2016). Fault diagnosis for the complex manufacturing system. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 1748006X15623089.
Oliver, N., & Horvitz, E. (2005). A comparison of hmms and dynamic bayesian networks for recognizing office activities. In User modeling 2005 (pp. 199–209). Springer.
Piétrac, L., Lelevé, A., & Henry, S. (2011). On the use of sysml for manufacturing execution system design. In Etfa 2011 (pp. 1–8).
Said, A. B., Shahzad, M. K., Zamai, E., Hubac, S., & Tollenaere, M. (2016). Experts knowledge renewal and maintenance actions effectiveness in high-mix lowvolume industries, using bayesian approach. Cognition, Technology & Work, 18(1), 193–213.
Vemer, L., Oleynikov, D., Holtmann, S., Haider, H., & Zhukov, L. (2003). Measurements of the level of surgical expertise using flight path analysis from da vinci robotic surgical system. Medicine Meets Virtual Reality 11: NextMed: Health Horizon, 94, 373.
Weber, P., Medina-Oliva, G., Simon, C., & Iung, B. (2012). Overview on bayesian networks applications for dependability, risk analysis and maintenance areas. Engineering Applications of Artificial Intelligence, 25(4), 671–682.
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