Integration of Failure Assessments into the Diagnostic Process
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Abstract
The complexity of technical systems requires increasingly advanced fault diagnosis methods to improve safety and reliability. Particularly in domains where maintenance poses an extensive part of the entire operation cost, accurate identification of failure sources has a large economic impact. Modelbased diagnosis, as a subfield of Artificial Intelligence, allows to determine root causes based on observed anomalies and has already been applied to a variety of domains. Abductive model-based diagnosis considers information on failures and how they affect detectable system measurements. Thus, this type of fault localization procedure depends on systematic and analytic knowledge on components, their possible
malfunctions, and the subsequent effects. In this paper, we examine various common failure assessments such as Failure Mode Effect Analysis, in regard to serving as a basis for abductive diagnosis. In particular, we analyze the methods concerning their advantages and limitations as sources of failure information within our diagnosis process.
How to Cite
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FMEA, Model-based diagnosis, FTA, Abductive Reasoning
Bobbio, A., Montani, S., & Portinale, L. (2002). Parametric dependability analysis through probabilistic horn abduction. In Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence (pp. 65–72).
Botsaris, P. N., Konstantinidis, E., & Pitsa, D. (2012). Systemic assessment and analysis of factors affect the reliability of a wind turbine. Journal of Applied Engineering Science (Istrazivanja i projektovanja za privredu), 10(2).
Carlson, C. S. (2014). Understanding and applying the fundamentals of FMEAs. In Proceedings of the 2014 Annual Reliability and Maintainability Symposium.
Console, L., & Dressler, O. (1999). Model-based diagnosis in the real world: lessons learned and challenges remaining. In Proceedings 16th International Joint Conf. on Artificial Intelligence (pp. 1393–1400).
Console, L., Dupre, D. T., & Torasso, P. (1991). On the Relationship Between Abduction and Deduction. Journal of Logic and Computation, 1(5), 661–690. doi: 10.1093/logcom/1.5.661
Cordier, M.-O., Dague, P., Lévy, F., Montmain, J., Staroswiecki, M., & Travé-Massuy`es, L. (2004). Conflicts versus analytical redundancy relations: a comparative analysis of the model based diagnosis approach from the artificial intelligence and automatic control perspectives. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 34(5), 2163–2177.
de Kleer, J., & Williams, B. C. (1987). Diagnosing multiple faults. Artificial Intelligence, 32(1), 97–130.
Friedrich, G., Gottlob, G., & Nejdl, W. (1990, September). Hypothesis classification, abductive diagnosis and therapy. In Proceedings of the International Workshop on Expert Systems in Engineering. Vienna: Springer Verlag, Lecture Notes in Artificial Intelligence, Vo. 462.
Ganesan, S., Eveloy, V., Das, D., & Pecht, M. (2005). Identification and utilization of failure mechanisms to enhance FMEA and FMECA. In Proceedings of the IEEE workshop on accelerated stress testing & reliability (ASTR), austin, texas.
Gray, C., Langmayr, F., Haselgruber, N., & Watson, S. J. (2011). A practical approach to the use of scada data for optimized wind turbine condition based maintenance. EWEA Offshore Wind Amsterdam.
Gray, C. S., & Watson, S. J. (2010). Physics of failure approach to wind turbine condition based maintenance. Wind Energy, 13(5), 395–405.
Hawkins, P. G., & Woollons, D. J. (1998a). Failure modes and effects analysis of complex engineering systems using functional models. Artificial Intelligence in Engineering, 12, 375–397.
Hawkins, P. G., & Woollons, D. J. (1998b). Failure modes and effects analysis of complex engineering systems using functional models. Artificial Intelligence in Engineering, 12(4), 375–397.
Kakas, A. C., Kowalski, R. A., & Toni, F. (1992). Abductive logic programming. Journal of logic and computation, 2(6), 719–770.
Koitz, R., & Wotawa, F. (2015a). Finding explanations: an empirical evaluation of abductive diagnosis algorithms. In Proceedings of the international workshop on defeasible and ampliative reasoning (DARe-15).
Koitz, R., & Wotawa, F. (2015b). From theory to practice: Model-based diagnosis in industrial applications. In Proceedings of the annual conference of the prognostics and health management society 2015.
Koitz, R., &Wotawa, F. (2015c). On the feasibility of abductive diagnosis for practical applications. In 9th IFAC symposium on fault detection, supervision and safety of technical processes.
Lee, B. H. (2001). Using bayes belief networks in industrial fmea modeling and analysis. In Reliability and maintainability symposium, 2001. proceedings. annual (pp. 7–15).
Liu, D. (2016). Systems engineering design principles and models. CRC Press.
Márquez, F. P. G., Tobias, A. M., Pérez, J. M. P., & Papaelias, M. (2012). Condition monitoring of wind turbines: Techniques and methods. Renewable Energy, 46, 169–178.
Marquis, P. (2000). Consequence finding algorithms. In Handbook of defeasible reasoning and uncertainty management systems (pp. 41–145). Springer.
McIlraith, S. A. (1998). Logic-based abductive inference. Knowledge Systems Laboratory, Technical Report KSL-98-19.
Medina-Oliva, G., Iung, B., Barberá, L., Viveros, P., & Ruin, T. (2012). Root cause analysis to identify physical causes. In 11th international probabilistic safety assessment and management conference and the annual european safety and reliability conference, PSAM11-ESREL 2012 (p. CDROM).
Milde, H., Guckenbiehl, T., Malik, A., Neumann, B., & Struss, P. (2000). Integrating model-based diagnosis techniques into current work processes–three case studies from the india project. AI Communications, 13(2), 99–123.
Mode, P. F. (2002). Effects analysis in design (design FMEA) and potential failure mode and effects analysis in manufacturing and assembly processes (process fmea) reference manual. Society of Automotive Engineers, Surface Vehicle Recommended Practice J, 1739.
Nordh, G., & Zanuttini, B. (2008). What makes propositional abduction tractable. Artificial Intelligence, 172, 1245–1284.
Oh, H., Azarian, M. H., Pecht, M., White, C. H., Sohaney, R. C., & Rhem, E. (2010). Physics-of-failure approach for fan phm in electronics applications. In Prognostics and health management conference, 2010. PHM’10. (pp. 1–6).
Pascoe, N. (2011). Reliability technology: Principles and practice of failure prevention in electronic systems. John Wiley & Sons.
Pecht, M., & Dasgupta, A. (1995). Physics-of-failure: an approach to reliable product development. Journal of the IES, 38(5), 30–34.
Poole, D., Goebel, R., & Aleliunas, R. (1987). Theorist: A logical reasoning system for defaults and diagnosis. Springer.
Price, C. J., & Taylor, N. S. (2002). Automated multiple failure fmea. Reliability Engineering & System Safety, 76(1), 1–10.
Rademakers, L., Seebregts, A., & van Den Horn, B. (1993). Reliability analysis in wind engineering. Netherlands Energy Research Foundation ECN.
Rausand, M., & Høyland, A. (2004). System reliability theory: models, statistical methods, and applications (Vol. 396). John Wiley & Sons.
Reiter, R. (1987). A theory of diagnosis from first principles. Artificial Intelligence, 32(1), 57–95.
Struss, P., Malik, A., & Sachenbacher, M. (1996). Case studies in model-based diagnosis and fault analysis of carsubsystems. In Proc. 1st int’l workshop model-based systems and qualitative reasoning (pp. 17–25).
Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault tree handbook (Tech. Rep.). DTIC Document.
Vogl, G. W., Weiss, B. A., & Donmez, M. A. (2014). Standards for prognostics and health management (phm) techniques within manufacturing operations. In Annual conference of the prognostics and health management society, september.
Wotawa, F. (2011). On the use of abduction as an alternative to decision trees in environmental decision support systems. International journal of agricultural and environmental information systems, 2(1), 63–82.
Wotawa, F. (2014). Failure mode and effect analysis for abductive diagnosis. In Proceedings of the international workshop on defeasible and ampliative reasoning (DARe-14) (Vol. 1212). CEURWorkshop Proceedings.
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