Concept and Economic Evaluation of Prescriptive Maintenance Strategies for an Automated Condition Monitoring System
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Published
Mar 24, 2021
Robert Meissner
Hendrik Meyer
Kai Wicke
Abstract
In order to reduce operating costs and increase the operational stability, the aviation industry is continuously introducing digital technologies to automate the state detection of their assets and derive maintenance decisions. Thus, many industry efforts and research activities have focused on an early state fault detection and the prediction of system failures. Since research has mainly been limited to the calculation of remaining useful lifetimes (RUL) and has neglected the impact on surrounding processes, changes on the objectives of the involved stakeholders, resulting from these technologies, have hardly been addressed in existing work. However, to comprehensibly evaluate the potential of a fault diagnosis and failure prognosis system, including its effects on adjacent maintenance processes, the condition monitoring system’s maturity level needs to be taken into account, expressed for example through the technology’s automation degree or the prognostic horizon (PH) for reliable failure projections. In this paper, we present key features of an automatic condition monitoring architecture for the example of a Tire Pressure Indication System (TPIS). Furthermore, we develop a prescriptive maintenance strategy by modeling the involved stakeholders of aircraft and line maintenance operations with their functional dependencies. Subsequently, we estimate the expected implications for a small aircraft fleet with the introduction of such a monitoring system with various levels of technological maturity. Additionally, we calculate the maintenance cost savings potential for different measurement strategies and compare these results to the current state-of-the-art maintenance approach. To estimate the effects of implementing an automated condition monitoring system, we use a discrete-event, agentbased simulation setup with an exemplary flight schedule and a simulated time span of 30 calendar days. The obtained results allow a comprehensive estimation of the maintenance related implications on airline operation and provide key aspects in the development of an airline’s prescriptive maintenance strategy.
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Keywords
Prescriptive, Maintenance, CBM, Automation, Aviation
References
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Bill, A., Roiz`es, J., & Pichon, B. (2019). Wireless tire pressure indication system for aircraft. 7th International Workshop on Aircraft System Technologies, 7, 89–98.
Chen, X., Xiao, L., Zhang, X., Xiao, W., & Li, J. (2015). An integrated model of production scheduling and maintenance
planning under imperfect preventive maintenance.
Eksploatacja i Niezawodnosc - Maintenance and Reliability, 17(1), 70–79. doi: 10.17531/ein.2015.1.10
Chiachío, J., Chiachío, M., Saxena, A., Rus, G., & Goebel, K. (2013). An energy-based prognostic framework to predict fatigue damage evolution in composites. In PHM Society (Ed.), Proceedings of the annual conference of the prognostics and health management society 2013 (pp. 363–371). Retrieved 2020-04-06, from https://www.phmsociety.org/sites/phmsociety.org/files/phm submission/2013/phmc 13 050.pdf
Crane Aerospace & Electronics. (2014). Tire pressure and brake temperature systems. Crane Aerospace & Electronics. Retrieved 29.01.2020, from https://www.craneae.com/Products/Sensing/Downloads/SmartStem%20OnBoard%20737%20Max.pdf
Crooks, A. T., & Heppenstall, A. J. (2012). Introduction to agent-based modelling. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agentbased models of geographical systems (Vol. 238, pp. 85–105). Dordrecht: Springer Netherlands. doi: 10.1007/978-90-481-8927-4
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Federal Aviation Administration. (2012). Advisory circular: Maintenance review boards, maintenance type boards, and oem/tch recommended maintenance procedures: Ac 121-22c.
Feldman, K., Jazouli, T., & Sandborn, P. A. (2009). A methodology for determining the return on investment associated with prognostics and health management. IEEE Transactions on Reliability, 58(2), 305–316. doi: 10.1109/TR.2009.2020133
Freeman, F. (2019). Challenges and opportunities for condition-based adaptive aircraft maintenance planning. Z¨urich. Retrieved from https://www.smartmaintenance.ch/doc/Folien/[PS1]_1-1_Freeman_Challenges%20and%20Opportunities%20for%20Condition-based%20Adaptive%20Aircraft%20Maintenance%20Planning.pdf
Gerdes, M., Scholz, D., & Galar, D. (2016). Effects of condition-based maintenance on costs caused by unscheduled maintenance of aircraft. Journal of Quality in Maintenance Engineering, 22(4), 394–417. doi: 10.1108/JQME-12-2015-0062
Goodyear. (2017). Aircraft tire care & maintenance. Retrieved 10.12.2018, from https://www.goodyearaviation.com/resources/pdf/aviation tire care 3 2017.pdf
H¨olzel, N. (2019). Ein bewertungsansatz zur analyse von zustandsmanagementsystemen in verkehrsflugzeugen unter ber¨ucksichtigung neuer instandhaltungskonzepte (PhD Thesis, DLR). doi: 10.15480/882.2543
H¨olzel, N., Schilling, T., & Gollnick, V. (2014). An aircraft lifecycle approach for the cost-benefit analysis of prognostics and condition-based maintenance based on discrete-event simulation. In PHM Society (Ed.), Annual conference of the prognostics and health management society 2014 (Vol. 5, pp. 1–16). Retrieved 06.12.2018, from https://www.phmsociety.org/node/1297
H¨olzel, N., Schr¨oder, C., Schilling, T., & Gollnick, V. (2012). A maintenance packaging and scheduling optimization method for future aircraft. In 6th international meeting for aviation product support processes (imapp). Retrieved from https://elib.dlr.de/76244/
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K¨ahlert, A., Giljohann, S., & Klingauf, U. (2016). Costbenefit analysis and specification of component-level phm systems in aircraft. Universal Journal of Mechanical Engineering, 4(4), 88–98. doi: 10.13189/ujme.2016.040403
Kinnison, H. A. (2004). Aviation maintenance management. New York: McGraw-Hill.
Kolanjiappan, S., & Maran, K. (2011). Lean philosophy in aircraft maintenance. Journal of Management Research and Development, 1, 27–41.
Kurtoglu, T., Mengshoel, O. J., & Poll, S. (2008). A framework for systematic benchmarking of monitoring and diagnostic systems. In 2008 international conference on prognostics and health management (pp. 1–13). IEEE. doi: 10.1109/PHM.2008.4711454
Loutas, T., Eleftheroglou, N., & Zarouchas, D. (2017). A data-driven probabilistic framework towards the in-situ prognostics of fatigue life of composites based on acoustic emission data. Composite Structures, 161, 522–529. doi: 10.1016/j.compstruct.2016.10.109
Meissner, R., Meyer, H., & Raddatz, F. (2019). A measurement frequency estimation method for failure prognosis of an automated tire condition monitoring system. 2019 IEEE International Conference on Prognostics and Health Management, 1–8. doi: 10.1109/ICPHM.2019.8819422
Meissner, R., Raschdorff, F., Meyer, H., & Schilling, T. (2019). Digital transformation in maintenance on the example of a tire pressure indicating system. In O. von Estorff & F. Thielecke (Eds.), Proceedings of the 7th international workshop on aircraft system technologies (pp. 99–108). Herzogenrath: Shaker.
Meyer, H., Bontikous, N., & Plagemann, A. (2017). Development of an end to end maintenance evaluation strategy for new technologies in the context of ivhm. Proceedings of the 6th Aeropsace Europe CEAS Conference, 6, 1–10.
Meyer, H., Zimdahl, J., Kamtsiuris, A., Meissner, R., Raddatz, F., Haufe, S., & B¨aßler, M. (2020). Development of a digital twin for aviation research. In Deutsche Gesellschaft f¨ur Luft- und Raumfahrt (Ed.), Deutscher luft- und raumfahrtkongress 2020.
Nemeth, T., Ansari, F., Sihn, W., Haslhofer, B., & Schindler, A. (2018). Prima-x: A reference model for realizing prescriptive maintenance and assessing its maturity enhanced by machine learning. Procedia CIRP, 72, 1039–1044. doi: 10.1016/j.procir.2018.03.280
Papakostas, N., Papachatzakis, P., Xanthakis, V., Mourtzis, D., & Chryssolouris, G. (2010). An approach to operational aircraft maintenance planning. Decision Support Systems, 48(4), 604–612. doi: 10.1016/j.dss.2009.11.010
Poole, K. (2015). Modellbasierte entwicklung eines systems zur zustandsdiagnose und -vorhersage f¨ur die hydraulische energieversorgung in verkehrsflugzeugen (PhD Thesis). TUHH, Hamburg.
Ritter, O., Wende, G., Gentile, R., Marino, F., Bertolino, A., Raviola, A., & Jacazio, G. (2018). Intelligent diagnostics for aircraft hydraulic equipment. In PHM Society (Ed.), Proceedings of the european conference of the phm society (Vol. 4).
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008). Metrics for evaluating performance of prognostic techniques. In 2008 international conference on prognostics and health management (pp. 1–17). IEEE. doi: 10.1109/PHM.2008.4711436
Saxena, A., & Roemer, M. (2013). Ivhm assessment metrics. In I. K. Jennions (Ed.), Integrated vehicle health management (pp. 107–127). Warrendale: SAE International.
Vachtsevanos, G., & Goebel, K. (2015). Introduction to prognostics. San Diego, USA. Retrieved from http://www.phmsociety.org/sites/phmsociety.org/files/PROGNOSTICS TUTORIAL.pdf
Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., & Wu, B. (Eds.). (2006). Intelligent fault diagnosis and prognosis for engineering systems. Hoboken, N.J.: John Wiley.
Vianna, W. O. L., Rodrigues, L. R., & Yoneyama, T. (2015). Aircraft line maintenance planning based on phm data and resources availability using large neighborhood search. 2015 Annual Conference of the Prognostics and Health Management Society, 1–7.
Wheeler, K. R., Kurtoglu, T., & Poll, S. D. (2010). A survey of health management user objectives related to diagnostic and prognostic metrics. In 29th computers and information in engineering conference (pp. 1287–1298). New York, N.Y.: ASME. doi: 10.1115/DETC2009-87073
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. doi: 10.1007/s001700170173
Zhai, S., Riess, A., & Reinhart, G. (2019). Formulation and solution for the predictive maintenance integrated job shop scheduling problem. 2019 IEEE International Conference on Prognostics and Health Management, 1–8. doi: 10.1109/ICPHM.2019.8819397
Ahmadi, A., Gupta, S., Karim, R., & Kumar, U. (2010). Selection of maintenance strategy for aircraft systems using mulit-criteria decision making methodologies. International Journal of Reliability, Quality and Safety Engineering, 17(03), 223–243. doi: 10.1142/S0218539310003779
Airbus. (2010). Maintenance planning document a320 rev. 34.
Aircraft Commerce. (2019). Airline results of using big data & predictives. Aircraft Commerce(125), 36–42.
Bill, A. (2016). Aircraft tire pressure monitoring system (No. US20170087943A1).
Bill, A., Roiz`es, J., & Pichon, B. (2019). Wireless tire pressure indication system for aircraft. 7th International Workshop on Aircraft System Technologies, 7, 89–98.
Chen, X., Xiao, L., Zhang, X., Xiao, W., & Li, J. (2015). An integrated model of production scheduling and maintenance
planning under imperfect preventive maintenance.
Eksploatacja i Niezawodnosc - Maintenance and Reliability, 17(1), 70–79. doi: 10.17531/ein.2015.1.10
Chiachío, J., Chiachío, M., Saxena, A., Rus, G., & Goebel, K. (2013). An energy-based prognostic framework to predict fatigue damage evolution in composites. In PHM Society (Ed.), Proceedings of the annual conference of the prognostics and health management society 2013 (pp. 363–371). Retrieved 2020-04-06, from https://www.phmsociety.org/sites/phmsociety.org/files/phm submission/2013/phmc 13 050.pdf
Crane Aerospace & Electronics. (2014). Tire pressure and brake temperature systems. Crane Aerospace & Electronics. Retrieved 29.01.2020, from https://www.craneae.com/Products/Sensing/Downloads/SmartStem%20OnBoard%20737%20Max.pdf
Crooks, A. T., & Heppenstall, A. J. (2012). Introduction to agent-based modelling. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agentbased models of geographical systems (Vol. 238, pp. 85–105). Dordrecht: Springer Netherlands. doi: 10.1007/978-90-481-8927-4
Deutsches Intstitut f¨ur Normung e.V. (2010). Din en 13306:2010 maintenance: Maintenance terminology. Deutsches Institut f¨ur Normung e.V.
Federal Aviation Administration. (2012). Advisory circular: Maintenance review boards, maintenance type boards, and oem/tch recommended maintenance procedures: Ac 121-22c.
Feldman, K., Jazouli, T., & Sandborn, P. A. (2009). A methodology for determining the return on investment associated with prognostics and health management. IEEE Transactions on Reliability, 58(2), 305–316. doi: 10.1109/TR.2009.2020133
Freeman, F. (2019). Challenges and opportunities for condition-based adaptive aircraft maintenance planning. Z¨urich. Retrieved from https://www.smartmaintenance.ch/doc/Folien/[PS1]_1-1_Freeman_Challenges%20and%20Opportunities%20for%20Condition-based%20Adaptive%20Aircraft%20Maintenance%20Planning.pdf
Gerdes, M., Scholz, D., & Galar, D. (2016). Effects of condition-based maintenance on costs caused by unscheduled maintenance of aircraft. Journal of Quality in Maintenance Engineering, 22(4), 394–417. doi: 10.1108/JQME-12-2015-0062
Goodyear. (2017). Aircraft tire care & maintenance. Retrieved 10.12.2018, from https://www.goodyearaviation.com/resources/pdf/aviation tire care 3 2017.pdf
H¨olzel, N. (2019). Ein bewertungsansatz zur analyse von zustandsmanagementsystemen in verkehrsflugzeugen unter ber¨ucksichtigung neuer instandhaltungskonzepte (PhD Thesis, DLR). doi: 10.15480/882.2543
H¨olzel, N., Schilling, T., & Gollnick, V. (2014). An aircraft lifecycle approach for the cost-benefit analysis of prognostics and condition-based maintenance based on discrete-event simulation. In PHM Society (Ed.), Annual conference of the prognostics and health management society 2014 (Vol. 5, pp. 1–16). Retrieved 06.12.2018, from https://www.phmsociety.org/node/1297
H¨olzel, N., Schr¨oder, C., Schilling, T., & Gollnick, V. (2012). A maintenance packaging and scheduling optimization method for future aircraft. In 6th international meeting for aviation product support processes (imapp). Retrieved from https://elib.dlr.de/76244/
Ismail, M., Windelberg, J., & Bierig, A. (2016). A potential study of prognostic-based maintenance for primary flight control electro-mechanical actuators. 7th Conference on Recent Advances in Aerospace Actuation Systems and Components, 7, 1–8. Retrieved 20.11.2019, from https://elib.dlr.de/103578/
K¨ahlert, A., Giljohann, S., & Klingauf, U. (2016). Costbenefit analysis and specification of component-level phm systems in aircraft. Universal Journal of Mechanical Engineering, 4(4), 88–98. doi: 10.13189/ujme.2016.040403
Kinnison, H. A. (2004). Aviation maintenance management. New York: McGraw-Hill.
Kolanjiappan, S., & Maran, K. (2011). Lean philosophy in aircraft maintenance. Journal of Management Research and Development, 1, 27–41.
Kurtoglu, T., Mengshoel, O. J., & Poll, S. (2008). A framework for systematic benchmarking of monitoring and diagnostic systems. In 2008 international conference on prognostics and health management (pp. 1–13). IEEE. doi: 10.1109/PHM.2008.4711454
Loutas, T., Eleftheroglou, N., & Zarouchas, D. (2017). A data-driven probabilistic framework towards the in-situ prognostics of fatigue life of composites based on acoustic emission data. Composite Structures, 161, 522–529. doi: 10.1016/j.compstruct.2016.10.109
Meissner, R., Meyer, H., & Raddatz, F. (2019). A measurement frequency estimation method for failure prognosis of an automated tire condition monitoring system. 2019 IEEE International Conference on Prognostics and Health Management, 1–8. doi: 10.1109/ICPHM.2019.8819422
Meissner, R., Raschdorff, F., Meyer, H., & Schilling, T. (2019). Digital transformation in maintenance on the example of a tire pressure indicating system. In O. von Estorff & F. Thielecke (Eds.), Proceedings of the 7th international workshop on aircraft system technologies (pp. 99–108). Herzogenrath: Shaker.
Meyer, H., Bontikous, N., & Plagemann, A. (2017). Development of an end to end maintenance evaluation strategy for new technologies in the context of ivhm. Proceedings of the 6th Aeropsace Europe CEAS Conference, 6, 1–10.
Meyer, H., Zimdahl, J., Kamtsiuris, A., Meissner, R., Raddatz, F., Haufe, S., & B¨aßler, M. (2020). Development of a digital twin for aviation research. In Deutsche Gesellschaft f¨ur Luft- und Raumfahrt (Ed.), Deutscher luft- und raumfahrtkongress 2020.
Nemeth, T., Ansari, F., Sihn, W., Haslhofer, B., & Schindler, A. (2018). Prima-x: A reference model for realizing prescriptive maintenance and assessing its maturity enhanced by machine learning. Procedia CIRP, 72, 1039–1044. doi: 10.1016/j.procir.2018.03.280
Papakostas, N., Papachatzakis, P., Xanthakis, V., Mourtzis, D., & Chryssolouris, G. (2010). An approach to operational aircraft maintenance planning. Decision Support Systems, 48(4), 604–612. doi: 10.1016/j.dss.2009.11.010
Poole, K. (2015). Modellbasierte entwicklung eines systems zur zustandsdiagnose und -vorhersage f¨ur die hydraulische energieversorgung in verkehrsflugzeugen (PhD Thesis). TUHH, Hamburg.
Ritter, O., Wende, G., Gentile, R., Marino, F., Bertolino, A., Raviola, A., & Jacazio, G. (2018). Intelligent diagnostics for aircraft hydraulic equipment. In PHM Society (Ed.), Proceedings of the european conference of the phm society (Vol. 4).
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008). Metrics for evaluating performance of prognostic techniques. In 2008 international conference on prognostics and health management (pp. 1–17). IEEE. doi: 10.1109/PHM.2008.4711436
Saxena, A., & Roemer, M. (2013). Ivhm assessment metrics. In I. K. Jennions (Ed.), Integrated vehicle health management (pp. 107–127). Warrendale: SAE International.
Vachtsevanos, G., & Goebel, K. (2015). Introduction to prognostics. San Diego, USA. Retrieved from http://www.phmsociety.org/sites/phmsociety.org/files/PROGNOSTICS TUTORIAL.pdf
Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., & Wu, B. (Eds.). (2006). Intelligent fault diagnosis and prognosis for engineering systems. Hoboken, N.J.: John Wiley.
Vianna, W. O. L., Rodrigues, L. R., & Yoneyama, T. (2015). Aircraft line maintenance planning based on phm data and resources availability using large neighborhood search. 2015 Annual Conference of the Prognostics and Health Management Society, 1–7.
Wheeler, K. R., Kurtoglu, T., & Poll, S. D. (2010). A survey of health management user objectives related to diagnostic and prognostic metrics. In 29th computers and information in engineering conference (pp. 1287–1298). New York, N.Y.: ASME. doi: 10.1115/DETC2009-87073
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. doi: 10.1007/s001700170173
Zhai, S., Riess, A., & Reinhart, G. (2019). Formulation and solution for the predictive maintenance integrated job shop scheduling problem. 2019 IEEE International Conference on Prognostics and Health Management, 1–8. doi: 10.1109/ICPHM.2019.8819397
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Technical Papers