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Journal of Intelligent Manufacturing

Erschienen in:

14.12.2015

Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance

verfasst von: Alexandros Bousdekis, Babis Magoutas, Dimitris Apostolou, Gregoris Mentzas

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 6/2018

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Abstract

In manufacturing enterprises, maintenance is a significant contributor to the total company’s cost. Condition based maintenance (CBM) relies on prognostic models and uses them to support maintenance decisions based on the predicted condition of equipment. Although prognostic-based decision support for CBM is not an extensively explored area, there exist methods which have been developed in order to deal with specific challenges such as the need to cope with real-time information, to predict the health state of equipment and to continuously update maintenance-related recommendations. The current work aims at providing a literature review for prognostic-based decision support methods for CBM. We analyse the literature in order to identify combinations of methods for prognostic-based decision support for CBM, propose a practical technique for selecting suitable combinations of methods and set the guidelines for future research.

Vorheriger Artikel Modeling and optimization of manufacturing process performance using Modelica graphical representation and process analytics formalism

Nächster Artikel Cyber physical process monitoring systems

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Adhatrao, K., Gaykar, A., Dhawan, A., Jha, R., & Honrao, V. (2013). Predicting students’ performance using ID3 and C4. 5 classification algorithms. arXiv:1310.2071

Aissani, N., Beldjilali, B., & Trentesaux, D. (2009). Dynamic scheduling of maintenance tasks in the petroleum industry: A reinforcement approach. Engineering Applications of Artificial Intelligence, 22(7), 1089–1103.CrossRef

Battese, G. E., Rao, D. P., & O’Donnell, C. J. (2004). A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. Journal of Productivity Analysis, 21(1), 91–103.CrossRef

Besnard, F., & Bertling, L. (2010). An approach for condition-based maintenance optimization applied to wind turbine blades. IEEE Transactions on Sustainable Energy, 1(2), 77–83.CrossRef

Besnard, F., Patriksson, M., Stromberg, A., Wojciechowski, A., Fischer, K., & Bertling, L. (2011). A stochastic model for opportunistic maintenance planning of offshore wind farms. InIEEE Trondheim on PowerTech (pp. 1–8).

Bousdekis, A., Magoutas, B., Apostolou, D., & Mentzas, G. (2015). A proactive decision making framework for condition-based maintenance. Industrial Management & Data Systems, 115(7), 1225–1250.CrossRef

Bouvard, K., Artus, S., Berenguer, C., & Cocquempot, V. (2011). Condition-based dynamic maintenance operations planning and grouping. Application to commercial heavy vehicles. Reliability Engineering & System Safety, 96(6), 601–610.CrossRef

Businoska, A. (2010). Kingaroy flagship UCG project roars into life. The Australian mining review. May 2010, Publications & Exhibitions, 8–9.

Castro, I. T., Huynh, K. T., Barros, A., & Berenguer, C. (2012). A predictive maintenance strategy based on mean residual life for systems subject to competing failures due to degradation and shocks. In Proceedings of the 11th international probabilistic safety assessment and management conference and the annual European safety and reliability conference-PSAM 11/ESREL 2012, pp. 375–384.

Chen, X. J., Zhang, Z. G., & Tong, Y. (2014). An improved ID3 decision tree algorithm. Advanced Materials Research, 962, 2842–2847.CrossRef

Dechert, W. D., & Nishimura, K. (2012). A complete characterization of optimal growth paths in an aggregated model with a non-concave production function. In J. Stachurski, A. Venditti, & M. Yano (Eds.), Nonlinear Dynamics in Equilibrium Models (pp. 237-257). Berlin: Springer.

Elwany, A. H., & Gebraeel, N. Z. (2008). Sensor-driven prognostic models for equipment replacement and spare parts inventory. IIE Transactions, 40(7), 629–639.CrossRef

Engel, Y., & Etzion, O. (2011). Towards proactive event-driven computing. In Proceedings of the 5th ACM international conference on Distributed event-based system. ACM 2011 (pp. 125–136).

Engel, Y., Etzion, O., & Feldman, Z. (2012). A basic model for proactive event-driven computing. In Proceedings of the 6th ACM international conference on distributed event-based systems (pp. 107–118).

Gaddam, S. R., Phoha, V. V., & Balagani, K. S. (2007). K-Means+ ID3: A novel method for supervised anomaly detection by cascading K-Means clustering and ID3 decision tree learning methods. IEEE Transactions on Knowledge and Data Engineering, 19(3), 345–354.CrossRef

Garg, A., & Deshmukh, S. G. (2006). Maintenance management: Literature review and directions. Journal of Quality in Maintenance Engineering, 12(3), 205–238.CrossRef

Grossman, G. M., & Helpman, E. (2004). Managerial incentives and the international organization of production. Journal of International Economics, 63(2), 237–262.CrossRef

Huynh, K. T., Barros, A., & Berenguer, C. (2012). Maintenance decision-making for systems operating under indirect condition monitoring: value of online information and impact of measurement uncertainty. IEEE Transactions on Reliability, 61(2), 410–425.CrossRef

Iung, B., Levrat, E., Marquez, A. C., & Erbe, H. (2009). Conceptual framework for e-maintenance: Illustration by e-maintenance technologies and platforms. Annual Reviews in Control, 33(2), 220–229.CrossRef

Ivy, J. S., & Nembhard, H. B. (2005). A modeling approach to maintenance decisions using statistical quality control and optimization. Quality and Reliability Engineering International, 21(4), 355–366.CrossRef

Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510.CrossRef

Jin, C., De-lin, L., & Fen-xiang, M. (2009). An improved ID3 decision tree algorithm. In 4th International conference on computer science and education, ICCSE’09 (pp. 127–130).

Kaiser, K. A., & Gebraeel, N. Z. (2009). Predictive maintenance management using sensor-based degradation models. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 39(4), 840–849.

Kietz, J., Serban, F., Bernstein, A., & Fischer, S. (2009). Towards cooperative planning of data mining workflows. In Proceedings of the third generation data mining workshop at the 2009 European conference on machine learning. ECML 2009, pp. 1–12.

Li, Y., Chun. L., Nee. A., & Ching Y (2005). An agent-based platform for webenabled equipment predictive maintenance. In: Proceedings of IAT’05 IEEE/WIC/ACM international conference on intelligent agent technology, Compie‘gne, France, 2005.

Magoutas, B., Stojanovic, N., Bousdekis, A., Apostolou, D., Mentzas, G., & Stojanovic, L. (2014). Anticipation-driven architecture for proactive enterprise decision making. CAiSE (Forum/Doctoral Consortium) , 2014 (pp. 121–128)

Mitchell, T. M. (1997). Machine Learning. New York: McGraw-Hill Companies.

Muller, A., Suhner, M. C., & Iung, B. (2007). Maintenance alternative integration to prognosis process engineering. Journal of Quality in Maintenance Engineering, 13(2), 198–211.CrossRef

Muller, A., Marquez, A. C., & Iung, B. (2008). On the concept of e-maintenance: Review and current research. Reliability Engineering & System Safety, 93(8), 1165–1187.CrossRef

Pal, M., & Mather, P. M. (2003). An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment, 86(4), 554–565.CrossRef

Peng, Y., Dong, M., & Zuo, M. J. (2010). Current status of machine prognostics in condition-based maintenance: A review. The International Journal of Advanced Manufacturing Technology, 50(1–4), 297–313.CrossRef

Sathyadevan, S., & Nair, R. R. (2015). Comparative analysis of decision tree algorithms ID3, C4.5 and random forest. Computational Intelligence in Data Mining, 1(1), 549–562. Springer India.

Sharma, N., & Bansal, K. L. (2015). Comparative study of data mining tools. Journal of Advanced Database Management & Systems, 2(2), 35–41.

Telford, S., Mazhar, M. I. & Howard, I. (2011). Condition based maintenance (CBM) in the oil and gas industry: An overview of methods and techniques. In Proceedings of the 2011 international conference on industrial engineering and operations management, Kuala Lumpur, Malaysia.

Voisin, A., Levrat, E., Cocheteux, P., & Iung, B. (2010). Generic prognosis model for proactive maintenance decision support: Application to pre-industrial e-maintenance test bed. Journal of Intelligent Manufacturing, 21(2), 177–193.CrossRef

Waeyenbergh, G., & Pintelon, L. (2002). A framework for maintenance concept development. International Journal of Production Economics, 77(3), 299–313.CrossRef

Waeyenbergh, G., & Pintelon, L. (2009). CIBOCOF: A framework for industrial maintenance concept development. International Journal of Production Economics, 121(2), 633–640.CrossRef

Wu, S. J., Gebraeel, N., Lawley, M. A., & Yih, Y. (2007). A neural network integrated decision support system for condition-based optimal predictive maintenance policy. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 37(2), 226–236.CrossRef

Yam, R. C. M., Tse, P. W., Li, L., & Tu, P. (2001). Intelligent predictive decision support system for condition-based maintenance. The International Journal of Advanced Manufacturing Technology, 17(5), 383–391.CrossRef

Yeo, B. (2010). Shale gas versus CSG: Shale the new flavour (pp. 22–25). May/June, Aspermount Limited

Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. New York: McGraw-Hill Osborne Media.

Titel: Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance
verfasst von: Alexandros Bousdekis
Babis Magoutas
Dimitris Apostolou
Gregoris Mentzas
Publikationsdatum: 14.12.2015
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 6/2018
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-015-1179-5

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