ADEPS: A Methodology for Designing Prognostic Applications
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Prognostics applications predict the future evolution of an asset under study, by diagnosing the actual health state and modeling the future degradation. Due to rapidly growing interest in prognostics, different prediction techniques have been developed independently without a consistent and systematic design. In this paper we formalize the prognostics design process with a novel methodology entitled ADEPS (Assisted Design for Engineering Prognostic Systems). ADEPS combines prognostics concepts with model-based safety assessment, criticality analysis, knowledge engineering and formal verification approaches. The main activities of ADEPS include synthesis of the safety assessment model from the design model, prioritization of the system failure modes, systematic prognostics model selection and verification of the adequacy of the prognostics results with respect to design requirements. By linking system-level safety assessment models and prognostics results, design and safety models are updated with online information about different failure modes. This step enables system-level health assessment including
prognostics predictions of different failure modes. The endto-end application of themethodology for the design and evaluation of a power transformer demonstrates the benefits of the proposed approach including reduced design time and effort, complete consideration of prognostics algorithms and updated system-level health assessment.
How to Cite
##plugins.themes.bootstrap3.article.details##
complex systems, Criticality analysis, Model-based safety analysis, verification requirements, Generic prognostics methodology
Aizpurua, J. I., & Catterson, V. (2015b). Towards a methodology for design of prognostics systems. In Annual conference of the prognostics and health management society (Vol. 6).
Aizpurua, J. I., & Muxika, E. (2013). Model-based design of dependable systems: Limitations and evolution of analysis and verification approaches. International Journal on Advances in Security, 6(1, 2).
Aizpurua, J. I., Muxika, E., Papadopoulos, Y., Chiacchio, F., & Manno, G. (2016). Application of the D3H2 methodology for the cost-effective design of dependable systems. Safety, 2(2), 9. doi:
10.3390/safety2020009
Banjevic, D., & Jardine, A. K. S. (2006). Calculation of reliability function and remaining useful life for a markov failure time process. IMA Journal of Management Mathematics, 17(2), 115-130. doi: 10.1093/imaman/dpi029
Bousdekis, A., Magoutas, B., Apostolou, D., & Mentzas, G. (2015). Supporting the selection of prognostic-based decision support methods in manufacturing. In Proc. of int. conf. on enterprise information systems (p. 487-494). doi: 10.5220/0005372104870494
Catterson, V. M., Melone, J., & Garcia, M. S. (2016, January). Prognostics of transformer paper insulation using statistical particle filtering of on-line data. IEEE Electrical Insulation Magazine, 32(1), 28-33. doi: 10.1109/MEI.2016.7361101
CIGRÉ. (2015). Transformer Reliability Survey (No. 642). Cocheteux, P., Voisin, A., Levrat, E., & Iung, B. (2009). Prognostic design: requirements and tools. In Proc. of MITIP 2009. Bergame, Italy.
Cocheteux, P., Voisin, A., Levrat, E., & Iung, B. (2010). Systemperformance prognostic: context, issues and requirements. In Proc. of AMEST. Lisbon: IFAC.
Daigle, M. J., Bregon, A., & Roychoudhury, I. (2014, June). Distributed prognostics based on structural model decomposition. IEEE Transactions on Reliability, 63(2), 495-510. doi: 10.1109/TR.2014.2313791
Espiritu, J. F., Coit, D.W., & Prakash, U. (2007). Component criticality importance measures for the power industry. Electric Power Systems Research, 77(56), 407 - 420. doi: http://dx.doi.org/10.1016/j.epsr.2006.04.003
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003). Bayesian data analysis. Chapman and Hall/CRC.
Hilber, P. (2008). Maintenance optimization for power distribution systems (PhD Thesis). KTH. IEEE Power and Energy Society. (2011). IEEE Guide for Loading Mineral-Oil-Immersed Transformers and Step-Voltage Regulators. IEEE Std. C57.91.
Joshi, A., Heimdahl, M., Miller, S., & Whalen, M. (2006). Model-Based Safety Analysis (Vol. NASA/CR-2006- 213953; Tech. Rep. No. ID: 20060006673). NASA.
Katoen, J.-P., Kwiatkowska, M., Norman, G., & Parker, D. (2001). Process algebra and probabilisticmethods. performance modelling and verification. In (pp. 23–38). Springer. doi: 10.1007/3-540-44804-7 2
Kumar, S., Torres,M., Chan, Y., & Pecht,M. (2008, June). A Hybrid Prognostics Methodology for Electronic Products. In IEEE IJCNN 2008 (p. 3479-3485). doi: 10.1109/IJCNN.2008.4634294
Kwiatkowska,M., Norman, G., & Parker, D. (2011). PRISM 4.0: Verification of probabilistic real-time systems. In Proc. of CAV’11 (Vol. 6806, pp. 585–591). Springer.
Lee, J., Liao, L., Lapira, E., Ni, J., & Li, L. (2009). Informatics Platform for Designing and Deploying e-Manufacturing Systems. In Collaborative Design and Planning for DigitalManufacturing (p. 1-35). Springer London. doi: 10.1007/978-1-84882-287-0 1
MathWorks. (2016). Matlab/Simulink. http://www.mathworks.com;.
Papadopoulos, Y.,Walker, M., Parker, D., R¨ude, E., Hamann, R., Uhlig, A., . . . Lien, R. (2011). Engineering failure analysis and design optimisation with HiP-HOPS. Engineering Failure Analysis, 18(2), 90-608.
Peysson, F., Ouladsine, M., Outbib, R., Leger, J.-B., Myx, O., & Allemand, C. (2009, June). A Generic PrognosticMethodologyUsing Damage TrajectoryModels. IEEE Transactions on Reliability, 58(2), 277-285. doi: 10.1109/TR.2009.2020123
Ramos, A., Ferreira, J., & Barcelo, J. (2012). Model-based systems engineering: An emerging approach for modern systems. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 42(1), 101-111. doi: 10.1109/TSMCC.2011.2106495
Ruin, T., Levrat, E., Iung, B., & Despujols, A. (2014). Complex maintenance programs quantification (CMPQ) to better control production systems. Journal ofManufacturing Technology Management, 25(4), 491-509. doi: 10.1108/JMTM-04-2013-0042
Rumbaugh, J., Jacobson, I., & Booch, G. (1999). The unified modeling language reference manual [Computer software manual].
Sanders, W. H., & Meyer, J. F. (2001). Stochastic activity networks: Formal definitions and concepts. In Lectures on formal methods and performance analysis (Vol. 2090, p. 315-343). Springer Berlin Heidelberg. doi: 10.1007/3-540-44667-2 9
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher,M. (2008). Metrics for Evaluating Performance of Prognostic Techniques. In PHM 2008 (pp. 1–17).
Saxena, A., Roychoudhury, I., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2012). Requirements Flowdown for Prognostics and Health Management. In Infotech@ Aerospace. AIAA. doi: 10.2514/6.2012-2554
Takata, S., Kirnura, F., van Houten, F.,Westkamper, E., Shpitalni, M., Ceglarek, D., & Lee, J. (2004). Maintenance: Changing role in life cycle management. CIRP Annals - Manufacturing Technology, 53(2), 643 - 655. doi: http://dx.doi.org/10.1016/S0007-8506(07)60033-X
Tang, L., Orchard, M., Goebel, K., & Vachtsevanos, G. (2011). Novel Metrics and Methodologies for the Verification and Validation of Prognostic Algorithms. In Aerospace Conference, 2011 IEEE (p. 1-8). doi: 10.1109/AERO.2011.5747583
Uckun, S., Goebel, K., & Lucas, P. (2008, Oct). Standardizing research methods for prognostics. In PHM 2008 (p. 1-10). doi: 10.1109/PHM.2008.4711437
US Department of Defense. (1980). Procedures for Performing, a Failure Mode, Effects, and Criticality Analysis (MIL-STD-1629A). Washington, DC.
Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., & Wu, B. (2007). Intelligent Fault Diagnosis and Prognosis for Engineering Systems. JohnWiley & Sons, Inc. doi: 10.1002/9780470117842
Van der Borst, M., & Schoonakker, H. (2001). An overview of psa importancemeasures. Reliability Engineering& System Safety, 72(3), 241-245.
Vesely, W., Dugan, J., Fragola, J., Minarick, & Railsback, J. (2002). Fault Tree Handbook with Aerospace Applications (Handbook). NASA.
Weilkiens, T. (2011). Systems engineering with SysML/UML: modeling, analysis, design. Morgan Kaufmann.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.