Prognostic systems representation in a function-based Bayesian model during engineering design
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Published
Nov 16, 2020
Guillaume L’Her
Douglas L. Van Bossuyt
Bryan M. O’Halloran
Abstract
Prognostics and Health Management (PHM) systems are usually only considered and set up in the late stage of design or even during the system’s lifetime, after the major design decision have been made. However, considering the PHM system’s impact on the system failure probabilities can benefit the system design early on and subsequently reduce costs. The identification of failure paths in the early phases of engineering design can guide the designer toward a safer, more reliable and cost-efficient design. Several functional failure modeling methods have been developed recently. One of their advantages is to allow for risk assessment in the early stages of the design. Risk and reliability functional failure analysis methods currently developed do not explicitly model the PHM equipment used to identify and prevent potential system failures. This paper proposes a framework to optimize prognostic systems selection and positioning during the early stages of a complex system design. A Bayesian network, incorporating the PHM systems, is used to analyze the functional model and failure propagation. The algorithm developed within the proposed framework returns the optimized placement of PHM hardware in the complex system, allowing the designer to evaluate the need for system improvement. A design tool was developed to automatically apply the proposed method. A generic pressurized water nuclear reactor primary coolant loop system is used to present a case study illustrating the proposed framework. The results obtained for this particular case study demonstrate the promise of the method introduced in this paper. The case study notably exhibits how the proposed framework can be used to support engineering design teams in making better informed decisions early in the design phase.
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Keywords
PHM design, Bayesian network, Functional Modeling
References
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Andrews, J. D., & Dunnett, S. J. (2000). Event-tree analysis using binary decision diagrams. IEEE Transactions on Reliability, 49(2), 230–238.
Ankan, A., & Panda, A. (2015). Mastering probabilistic graphical models using python. Packt Publishing Ltd.
Bartram, G., & Mahadevan, S. (2015). Probabilistic prognosis with dynamic bayesian networks. International Journal of Prognostics and Health Management.
Boring, R. L., & Blackman, H. S. (2007). The origins of the SPAR-H methods performance shaping factor multipliers. In 2007 IEEE 8th human factors and power plants and HPRCT 13th annual meeting (pp. 177–184). Monterey, CA.
Boudali, H., & Dugan, J. (2006, mar). A Continuous-Time Bayesian Network Reliability Modeling, and Analysis Framework. IEEE Transactions on Reliability, 55(1), 86–97. doi: 10.1109/TR.2005.859228
Chang, A. S. (2002). Reasons for Cost and Schedule Increase for Engineering Design Projects. Journal of Management in Engineering, 18(1). doi: 10.1061/(ASCE)0742-597X(2002)18:1(29)
Choo, B. Y., Adams, S. C., Weiss, B. A., Marvel, J. A., &
Beling, P. A. (2016). Adaptive multi-scale prognostics and health management for smart manufacturing systems. International Journal of Prognostics and Health Management.
Coble, J., Ramuhalli, P., Bond, L., Hines, J. W., & Upadhyaya, B. (2015). A review of prognostics and health management applications in nuclear power plants. International Journal of Prognostics and Health Management.
Conroy, P., Stecki, J., & Thorn, A. (2016). Influence of Environmental Loading Factors on System Design. In European Conference of the Prognostics and Health Management Society 2016. Bilbao, Spain.
Cooper, S. E., Ramey-Smith, A. M., Wreathall, J., Parry, G. W., Bley, D. C., Luckas, W. J., . . . Barriere, M. T. (1996). A technique for human error analysis (ATHEANA) (Tech. Rep.). U.S Nuclear Regulatory Commission, NUREG/CR-6350.
Doguc, O., & Ramirez-Marquez, J. E. (2009). A generic method for estimating system reliability using bayesian networks. Reliability Engineering & System Safety, 94(2), 542–550.
Eisenbart, B., Blessing, L., & Gericke, K. (2012). Functional Modelling Perspectives Across Disciplines : a Literature Review. In International Design Conference - Design 2012. Dubrovnik, Croatia.
Elattar, H., Elminir, H., & Riad, A. (2016). Prognostics: A literature review. Complex Intell. Syst., 2, 125–154. doi: 10.1007/s40747-016-0019-3
Ericson, C. (1999). Fault tree analysis - a history. In Proceedings of the 17th International Systems Safety Conference. Orlando, FL.
Gertman, D., Blackman, H., Marble, J., Byers, J., & Smith, C. (2005). The SPAR-H human reliability analysis method. US Nuclear Regulatory Commission, NUREG/CR-6883.
Gopalratnam, K., Kautz, H., &Weld, D. S. (2005). Extending continuous time bayesian networks. In Proceedings of the 20th national conference on Artificial intelligence (Vol. 2, p. 981). Pittsburgh, PA.
Jensen, D., Tumer, I. Y., & Kurtoglu, T. (2009). Flow state logic (FSL) for analysis of failure propagation in early design. In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 8). San Diego, CA.
Kacprzynski, G. J., Roemer, M. J., & Hess, A. J. (2002). Health Management System Design: Development Simulation and Cost/Benefit Optimization. In 2002 Aerospace Conference Proceedings (Vol. 6, pp. 3065–3072). Big Sky, MT.
Kurtoglu, T., & Tumer, I. Y. (2008). A Graph-Based Fault Identification and Propagation Framework for Functional Design of Complex Systems. Journal of Mechanical Design, 130. doi: 10.1115/1.2885181
Langseth, H., & Portinale, L. (2007). Bayesian networks in reliability. Reliability Engineering and System Safety, 92, 92–108.
L’Her, G. (2016). PHASED. https://github.com/glher/PHASED. GitHub.
Lin, Y., Zakwan, S., & Jennions, I. (2017). A bayesian approach to fault identification in the presence of multicomponent degradation. International Journal of Prognostics and Health Management.
Liu, H.-C., Liu, L., & Liu, N. (2013). Risk evaluation approaches in failure mode and effects analysis: A literature review. Expert Systems With Applications, 40, 828–838.
López, A. J. G., Márquez, A. C., Fernández, J. F. G., & Bolaños, A. G. (2014). Towards the Industrial Application of PHM: Challenges and Methodological Approach. In European Conference of the Prognostics and Health Management Society 2014. Nantes, France.
Lough, K. G., Stone, R., & Tumer, I. Y. (2009). The risk in early design method. Journal of Engineering Design, 20(2), 155–173. doi: 10.1080/09544820701684271
Neil, M., & Marquez, D. (2012). Availability modelling of repairable systems using bayesian networks. Engineering Applications of Artificial Intelligence, 25(4), 698–704.
Nodelman, U., Shelton, C. R., & Koller, D. (2002). Continuous time bayesian networks. In Proceedings of the Eighteenth conference on Uncertainty in Artificial Intelligence (pp. 378–387). Alberta, Canada.
O’Halloran, B. M., Papakonstantinou, N., & Van Bossuyt, D. L. (2015). Modeling of function failure propagation across uncoupled systems. 2015 Annual Reliability and Maintainability Symposium. doi: 10.1109/RAMS.2015.7105107
Pearl, J. (1985). Bayesian networks: A model of selfactivated memory for evidential reasoning. In Seventh Annual Conference of the Cognitive Science Society (pp. 329–334).
Perreault, L., Thornton, M., Strasser, S., & Sheppard, J. W. (2015). Deriving prognostic continuous time Bayesian networks from D-matrices. In Proceedings of IEEE AUTOTESTCON, 2015 (pp. 152–161). National Harbor, MD.
Ramp, I. J., & Van Bossuyt, D. L. (2014). Toward an automated model-based geometric method of representing function failure propagation across uncoupled systems. In ASME 2014 International Mechanical Engineering Congress and Exposition. Montreal, Canada.
Sankavaram, C., Kodali, A., Pattipati, K., Singh, S., Zhang, Y., & Salman, M. (2016). An inference-based prognostic framework for health management of automotive systems. International Journal of Prognostics and Health Management.
Smith, C., Knudsen, J., Calley, M., Beck, S., Kvarfordt, K., & Wood, T. (2005). SAPHIRE basics - An Introduction to Probabilistic Risk Assessment via the Systems Analysis Program for Hands-On Integrated Reliability Evaluations (SAPHIRE) Software. Idaho National Laboratory, Idaho Falls, ID.
Spurgin, A., & Lydell, B. (2002). Critique of current human reliability analysis methods. In Proceedings of the 2002 IEEE 7th Conference on Human Factors and Power Plants (pp. 3–12). Scottsdale, AZ.
Stack, C., & Van Bossuyt, D. L. (2015). Toward a Functional Failure Modeling Method of Representing Prognostic Systems During the Early Phases of Design. Proceedings of the ASME 2015 International Design Engineering Technical Conference & Computers and Information in Engineering Conference(August 2015), DETC2015–46400.
Stone, R. B., Tumer, I. Y., & Wie, M. V. (2005). FFDM: The function failure design method . Journal of Mechanical Design. doi: 10.1115/1.1862678
Stone, R. B., &Wood, K. L. (2000). Development of a Functional Basis for Design. Journal of Mechanical Design, 122, 359–370.
Sun, B., Zeng, S., Kang, R., & Pecht, M. (2012). Benefits and Challenges of System Prognostics. IEEE Transactions on Reliability, 61(2).
Swain, A. D., & Guttmann, H. E. (1983). Handbook of human-reliability analysis with emphasis on nuclear power plant applications. final report (Tech. Rep.). Sandia National Labs., Albuquerque, NM (USA).
Torres-Toledano, J. G., & Sucar, L. E. (1998). Bayesian networks for reliability analysis of complex systems. In Proceedings of the 6th Ibero-American Conference on Artificial Intelligence (pp. 195–206). Lisbon, Portugal.
U.S. Department of Defense. (1949). Procedures for performing a failure mode effect and critical analysis (Tech. Rep. No. MIL-P-1629).
U.S. NRC. (2016). Probabilistic risk assessment (Tech. Rep. No. ML032200337). U.S. Nuclear Regulatory Commission.
Weber, P., Medina-Oliva, G., & Simon, C. (2012). Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Engineering Applications of Artificial Intelligence, 25, 671–682. doi: 10.1016/j.engappai.2010.06.002
Xiao, W. (2016). A probabilistic machine learning approach to detect industrial plant faults. International Journal of Prognostics and Health Management.
Andrews, J. D., & Dunnett, S. J. (2000). Event-tree analysis using binary decision diagrams. IEEE Transactions on Reliability, 49(2), 230–238.
Ankan, A., & Panda, A. (2015). Mastering probabilistic graphical models using python. Packt Publishing Ltd.
Bartram, G., & Mahadevan, S. (2015). Probabilistic prognosis with dynamic bayesian networks. International Journal of Prognostics and Health Management.
Boring, R. L., & Blackman, H. S. (2007). The origins of the SPAR-H methods performance shaping factor multipliers. In 2007 IEEE 8th human factors and power plants and HPRCT 13th annual meeting (pp. 177–184). Monterey, CA.
Boudali, H., & Dugan, J. (2006, mar). A Continuous-Time Bayesian Network Reliability Modeling, and Analysis Framework. IEEE Transactions on Reliability, 55(1), 86–97. doi: 10.1109/TR.2005.859228
Chang, A. S. (2002). Reasons for Cost and Schedule Increase for Engineering Design Projects. Journal of Management in Engineering, 18(1). doi: 10.1061/(ASCE)0742-597X(2002)18:1(29)
Choo, B. Y., Adams, S. C., Weiss, B. A., Marvel, J. A., &
Beling, P. A. (2016). Adaptive multi-scale prognostics and health management for smart manufacturing systems. International Journal of Prognostics and Health Management.
Coble, J., Ramuhalli, P., Bond, L., Hines, J. W., & Upadhyaya, B. (2015). A review of prognostics and health management applications in nuclear power plants. International Journal of Prognostics and Health Management.
Conroy, P., Stecki, J., & Thorn, A. (2016). Influence of Environmental Loading Factors on System Design. In European Conference of the Prognostics and Health Management Society 2016. Bilbao, Spain.
Cooper, S. E., Ramey-Smith, A. M., Wreathall, J., Parry, G. W., Bley, D. C., Luckas, W. J., . . . Barriere, M. T. (1996). A technique for human error analysis (ATHEANA) (Tech. Rep.). U.S Nuclear Regulatory Commission, NUREG/CR-6350.
Doguc, O., & Ramirez-Marquez, J. E. (2009). A generic method for estimating system reliability using bayesian networks. Reliability Engineering & System Safety, 94(2), 542–550.
Eisenbart, B., Blessing, L., & Gericke, K. (2012). Functional Modelling Perspectives Across Disciplines : a Literature Review. In International Design Conference - Design 2012. Dubrovnik, Croatia.
Elattar, H., Elminir, H., & Riad, A. (2016). Prognostics: A literature review. Complex Intell. Syst., 2, 125–154. doi: 10.1007/s40747-016-0019-3
Ericson, C. (1999). Fault tree analysis - a history. In Proceedings of the 17th International Systems Safety Conference. Orlando, FL.
Gertman, D., Blackman, H., Marble, J., Byers, J., & Smith, C. (2005). The SPAR-H human reliability analysis method. US Nuclear Regulatory Commission, NUREG/CR-6883.
Gopalratnam, K., Kautz, H., &Weld, D. S. (2005). Extending continuous time bayesian networks. In Proceedings of the 20th national conference on Artificial intelligence (Vol. 2, p. 981). Pittsburgh, PA.
Jensen, D., Tumer, I. Y., & Kurtoglu, T. (2009). Flow state logic (FSL) for analysis of failure propagation in early design. In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (Vol. 8). San Diego, CA.
Kacprzynski, G. J., Roemer, M. J., & Hess, A. J. (2002). Health Management System Design: Development Simulation and Cost/Benefit Optimization. In 2002 Aerospace Conference Proceedings (Vol. 6, pp. 3065–3072). Big Sky, MT.
Kurtoglu, T., & Tumer, I. Y. (2008). A Graph-Based Fault Identification and Propagation Framework for Functional Design of Complex Systems. Journal of Mechanical Design, 130. doi: 10.1115/1.2885181
Langseth, H., & Portinale, L. (2007). Bayesian networks in reliability. Reliability Engineering and System Safety, 92, 92–108.
L’Her, G. (2016). PHASED. https://github.com/glher/PHASED. GitHub.
Lin, Y., Zakwan, S., & Jennions, I. (2017). A bayesian approach to fault identification in the presence of multicomponent degradation. International Journal of Prognostics and Health Management.
Liu, H.-C., Liu, L., & Liu, N. (2013). Risk evaluation approaches in failure mode and effects analysis: A literature review. Expert Systems With Applications, 40, 828–838.
López, A. J. G., Márquez, A. C., Fernández, J. F. G., & Bolaños, A. G. (2014). Towards the Industrial Application of PHM: Challenges and Methodological Approach. In European Conference of the Prognostics and Health Management Society 2014. Nantes, France.
Lough, K. G., Stone, R., & Tumer, I. Y. (2009). The risk in early design method. Journal of Engineering Design, 20(2), 155–173. doi: 10.1080/09544820701684271
Neil, M., & Marquez, D. (2012). Availability modelling of repairable systems using bayesian networks. Engineering Applications of Artificial Intelligence, 25(4), 698–704.
Nodelman, U., Shelton, C. R., & Koller, D. (2002). Continuous time bayesian networks. In Proceedings of the Eighteenth conference on Uncertainty in Artificial Intelligence (pp. 378–387). Alberta, Canada.
O’Halloran, B. M., Papakonstantinou, N., & Van Bossuyt, D. L. (2015). Modeling of function failure propagation across uncoupled systems. 2015 Annual Reliability and Maintainability Symposium. doi: 10.1109/RAMS.2015.7105107
Pearl, J. (1985). Bayesian networks: A model of selfactivated memory for evidential reasoning. In Seventh Annual Conference of the Cognitive Science Society (pp. 329–334).
Perreault, L., Thornton, M., Strasser, S., & Sheppard, J. W. (2015). Deriving prognostic continuous time Bayesian networks from D-matrices. In Proceedings of IEEE AUTOTESTCON, 2015 (pp. 152–161). National Harbor, MD.
Ramp, I. J., & Van Bossuyt, D. L. (2014). Toward an automated model-based geometric method of representing function failure propagation across uncoupled systems. In ASME 2014 International Mechanical Engineering Congress and Exposition. Montreal, Canada.
Sankavaram, C., Kodali, A., Pattipati, K., Singh, S., Zhang, Y., & Salman, M. (2016). An inference-based prognostic framework for health management of automotive systems. International Journal of Prognostics and Health Management.
Smith, C., Knudsen, J., Calley, M., Beck, S., Kvarfordt, K., & Wood, T. (2005). SAPHIRE basics - An Introduction to Probabilistic Risk Assessment via the Systems Analysis Program for Hands-On Integrated Reliability Evaluations (SAPHIRE) Software. Idaho National Laboratory, Idaho Falls, ID.
Spurgin, A., & Lydell, B. (2002). Critique of current human reliability analysis methods. In Proceedings of the 2002 IEEE 7th Conference on Human Factors and Power Plants (pp. 3–12). Scottsdale, AZ.
Stack, C., & Van Bossuyt, D. L. (2015). Toward a Functional Failure Modeling Method of Representing Prognostic Systems During the Early Phases of Design. Proceedings of the ASME 2015 International Design Engineering Technical Conference & Computers and Information in Engineering Conference(August 2015), DETC2015–46400.
Stone, R. B., Tumer, I. Y., & Wie, M. V. (2005). FFDM: The function failure design method . Journal of Mechanical Design. doi: 10.1115/1.1862678
Stone, R. B., &Wood, K. L. (2000). Development of a Functional Basis for Design. Journal of Mechanical Design, 122, 359–370.
Sun, B., Zeng, S., Kang, R., & Pecht, M. (2012). Benefits and Challenges of System Prognostics. IEEE Transactions on Reliability, 61(2).
Swain, A. D., & Guttmann, H. E. (1983). Handbook of human-reliability analysis with emphasis on nuclear power plant applications. final report (Tech. Rep.). Sandia National Labs., Albuquerque, NM (USA).
Torres-Toledano, J. G., & Sucar, L. E. (1998). Bayesian networks for reliability analysis of complex systems. In Proceedings of the 6th Ibero-American Conference on Artificial Intelligence (pp. 195–206). Lisbon, Portugal.
U.S. Department of Defense. (1949). Procedures for performing a failure mode effect and critical analysis (Tech. Rep. No. MIL-P-1629).
U.S. NRC. (2016). Probabilistic risk assessment (Tech. Rep. No. ML032200337). U.S. Nuclear Regulatory Commission.
Weber, P., Medina-Oliva, G., & Simon, C. (2012). Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas. Engineering Applications of Artificial Intelligence, 25, 671–682. doi: 10.1016/j.engappai.2010.06.002
Xiao, W. (2016). A probabilistic machine learning approach to detect industrial plant faults. International Journal of Prognostics and Health Management.
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Technical Papers