A Bayesian network based approach to improve the effectiveness of maintenance actions in Semiconductor Industry
The Semiconductor Industry (SI) is facing the challenge of high-mix low-volume production due to increasing diversity in customer demands. This has increased unscheduled equipment breakdowns followed by delays in diagnosis and ineffective maintenance actions that reduce the production capacities. At present, these challenges are addressed with mathematical approaches to optimize maintenance actions and their times of intervention. However, few studies take into account the ineffectiveness of maintenance actions, which is the key source for subsequent breakdowns. Hence, in this paper, we present a methodology to detect poorly executed maintenance actions and predict their consequences on the product quality and/or equipment as the feedback for technicians. It is based on the definition of maintenance objectives and criteria by experts to capture information on the extent to which the objective is fulfilled. Data collected from maintenance actions is then used to formulate Bayesian Network (BN) to model the causality between defined criteria and effectiveness of maintenance actions. This is further used in the respective FMECA defined for each equipment, to unify the maintenance knowledge. The key advantages from the proposed approach are (i) dynamic FMECA with unified and updated maintenance knowledge and (ii) real time feedback for technicians on poor maintenance actions.
How to Cite
FMECA, Bayesian networks, maintenance actions effectiveness, share and unify the maintenance knowledge
Baly, R., & Hajj, H. (2012). Wafer Classification Using Support Vector Machines. IEEE Transactions on Semiconductor Manufacturing, vol. 25(3), pp. 373-383.
Ballhaus, W., Pagella, A., & Vogel, C. (2009). A change of pace for the semiconductor industry. Germany, Technology, Media and Telecommunications. Retrieved from http://maltiel-consulting.com/Semiconductor_Industry_After_Economic_Upheaval_maltiel_semiconductor.pdf
Bouaziz, M. F., Zamaï, E., & Hubac, S. (2012). Modélisation de l'état de santé d'un équipement de fabrication par une méthode probabiliste. Proceedings of 9th International Conference on Modeling, Optimization and SIMulation.
Bouckaert, R. R., (1993). Probabilistic Network Construction Using The Minimum Description Length Principle. In Lecture Notes in Computer Science, vol. 747, pp. 41-48.
Chickering, D. M., (2002). Learning Equivalence Classes of Bayesian-Network Structures. Journal of Machine Learning Research, vol. 2, pp. 445-498.
Coutinho, J. S., (1964). Failure effect analysis. Trans. New York Academy. Sci., vol. 26(II), pp. 564–584.
Dale, F., (2012). Intel’s semiconductor market share surges to more than 10-year high in 2011, March 2012.
Efthymiou, K., Papakostas, N., Mourtzis, D., & Ghyryssolouris, G. (2012), On a Predictive Maintenance Platform for Production Systems. Proceedings of CIRP conference on manufacturing systems, pp. 221-226.
Gertsbakh I. B., (1977). Models of preventive maintenance. Amsterdam, The Netherlands: North-Holland Pub. Co
Garcia, A., & Gilabert, E. (2011). Mapping FMEA into Bayesian Netoworks. International Journal of Performability Engineering, vol. 7(6), pp. 525-537.
Hubac, S., & Zamai, E. (2013). Politiques de Maintenance Equipement en Flux de Production Stressant - Equipment Maintenance Policy in Stressed Manufacturing Flow (Technology or Product). Edition TI (Technique de l’ingenieur) [AG 3535].
Hsieh, Y. S., Cheng, F. T., Huang, H. C., Wang, C. R., Wang, S. C., & Yang, H. C. (2013). Vm-based Baseline Predictive Maintenance Scheme. IEEE Transactions on Semiconductor Manufacturing, vol. 26, pp. 132-144.
Jensen, F. V., & Nielsen, T. D. (2007). Bayesian Networks and Decision Graphs. Second Edition, New York, USA: Springer Verlag
Jordan, W. E., (1972). Failure modes, effects and criticality analyses. In Proc. Annu. Reliability Maintainability Symp., pp 30-37.
Kjærulff, U. B., & Madsen, A. L. (2006), Probabilistic Networks for Practitioners - A Guide to Construction and Analysis of Bayesian Networks and Influence Diagrams. Department of Computer Science, Aalborg University, HUGIN Expert A/S
Krishnamurthy, L., Adler, R., Buonadonna, P., Chhabra, J., Flanigan, M., Kushalnagar, N., Nachman, L., & Yarvis, M. (2005). Design and deployment of industrial sensor networks: Experiences from a semiconductor plant and the north sea. Proceedings of the 3rd international conference on Embedded networked sensor systems, November 02-04, San Diego, California, USA
Lee, B., (2001). Using Bayes Belief Networks in Industrial FMEA Modelling and Analysis. In Proc. Annual RELIABILITY and MAINTAINABILITY Symposium. vol. 15(4), pp. 281-293.
Léger, A., Weber, P., Levrat, E., Duval, C., Farret, R., & Iung, B. (2009). Methodological Developments for Probabilistic Risk Analyses of Socio-Technical Systems. Journal of Risk and Reliability, vol. 223, pp. 313-332.
Medina-Oliva, G., & Weber, P. (2013). PRM-based patterns for Knowledge Formalization of Industrial Systems to Support Maintenance Strategies Assessment. Reliability Engineering and system Safety, vol. 116, pp. 38-56.
Mili, A., Bassetto, S., Siadat, A., & Tollenaere, M. (2009). Dynamic Risk Management Unveil Productivity Improvements. Journal of Loss Prevention in the Process Industries, vol. 22, pp. 25-34.
Munteanu, P., & Bendou, M. (2001). The EQ Framework for Learning Equivalence Classes of Bayesian Networks. First IEEE International Conference on Data Mining (IEEE ICDM), San José
Pearl, J., (2000). Causality: Models, Reasoning and Inference. New York, USA: Cambridge University Press, vol. 19, pp. 675-685.
Peter, M. L., (2012). Bayesian Statistics: An Introduction. Wiley & Sons, Inc
Pourret, O., Naïm, P., & Marcot, B. (2008). Bayesian Networks: A Practical Guide to Applications. Chichester, England: John Wiley & Sons, Inc
Reifer, D. J., (1979). Software Failure Modes and Effects Analysis. IEEE Transactions on Reliability, vol. 28(3), pp. 247-249.
Rissanen, J., (1978). Modeling by Shortest Data Description. Automatica, vol. 14(5), pp. 465-658. doi:10.1016/0005-1098(78)90005-5
Schirru, A., Pampuri, S., & DeNicolao, G. (2010). Particle filtering of hidden gamma processes for robust predictive maintenance in semiconductor manufacturing. Proceedings of 6th IEEE CASE.
Shahzad, M. K., Hubac, S., Siadat, A., & Tollenaere, M. (2011). An Extended Business Model to Ensure Time-to-Quality in Semiconductor Manufacturing Industry. International Conference on Enterprise Information Systems. Portugal
Shahzad, M. K., Tollenaere, M., Hubac, S., & Siadat, A. (2011). Extension des Methodes DFM pour l’industrialisation de produits microelectroniques. 9e Congrès International de Genie Industriel. Montréal, Canada
Stamatis, A., Mathioudakis, K., & Papailiou, K. (1992). Optimal Measurement and Health Index Selection For Gas Turbine Performance Status and Fault Diagnosis. Journal of Engineering for Gas Turbines and Power, vol. 114, pp. 209-216.
Stamford, C., (2012). Market share analysis: Total semiconductor revenue, worldwide, April 2012. http://www.gartner.com/newsroom/id/2405215
Susto, G., Beghi, A. & DeLuca, C. (2011). A predictive maintenance system for silicon epitaxial deposition. Proceedings of IEEE Conference on Automation Science and Engineering (CASE), pp. 262–267.
Susto, G., Pampuri, S., Schirru, A., & Beghi, A. (2012). Optimal tuning of epitaxy pyrometers. Proceedings of 23rd IEEE/SEMI Advanced Semiconductor Manufacturing Conference, pp. 294-299.
Susto, G. A., McLoone, S., Pagano, D., Schirru, A., Pampuri, S., & Beghi, A.(2013). Prediction of Integral Type Failures in Semiconductor Manufacturing through Classification Methods. Proceedings of IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA).
Teyssier, M., & Koller, D. (2005). Ordering-based search: A simple and effective algorithm for learning Bayesian networks. Proceedings of 21st Conference on Uncertainty in AI (UAI). pp. 584-590.
Trucco, P., Cagno, E., Ruggeri, F., & Grande, O. (2008). A Bayesian Belief Network Modeling of Organizational Factor in Risk Analysis: A Case Study in Maritime Transportation. Reliability Engineering and system Safety, vol. 93, pp. 823-834.
Vassilis, K., Vassilis, P., & Christos, E. (2013). A Bayesian Approach for Maintenance Action Recommendation. International Journal of Prognostics and Health Management, ISSN 2153-2648, 2013 034
Weber, P., & Jouffe, L. (2006), Complex System Reliability Modeling with Dynamic Object Oriented Bayesian Networks (DOOBN). Reliability Engineering and system Safety, vol. 91, pp. 149-162.
Weber, P., Suhner, M. C., & Iung, B. (2001). System approach-based Bayesian Network to Aid Maintenance of manufacturing process. Proceedings of 6th IFAC Symposium on Cost Oriented Automation, Low Cost Automation. October 8-9, Berlin, Germany
Weild, G., Madsen, A. L., & Israelson, S. (2005). Application of Object-Oriented Bayesian Networks for Condition Monitoring, Root Cause Analysis and Decision Support on Operation of Complex Continuous Process. Computer and Chemical Engineering, vol. 29, pp. 1996-2009.
Yang, L., & Lee, J. (2012). Bayesian Belief Network-based Approach for Diagnostics and Prognostics of Semiconductor Manufacturing. Robotics and Computer-Integrated Manufacturing, vol. 28, pp. 66-74.
Zou, X., & Bhanu, B. (2005). Tracking Humans using Multi-modal Fusion. Proceedings of Computer Society Conference on Computer Vision and Pattern Recognition (CVPRW'05). San Diego, California, USA
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