A Comparison of Data-driven Techniques for Engine Bleed Valve Prognostics using Aircraft-derived Fault Messages
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Abstract
Prognostics plays an increasingly important role in preventive maintenance and aircraft safety. An approach that has recently become popular in this field is the data-driven technique. This approach consists in the use of past data and advanced statistics to derive estimates for the reliability of an equipment without relying on any physics or engineering principle. Data-driven models have been based on two types of historical data: past failure times and health monitoring data. A kind of health monitoring data rarely used in data-driven models are aircraft-derived maintenance messages. These data consist of fault messages derived from the aircraft onboard systems to notify any unexpected events or abnormal behavior as well as to send warning signals of equipment degradation. Fault messages have not received much attention in aircraft prognostics mostly due to its asynchronous and qualitative nature that often causes difficulties of interpretation. The main goal of this paper is to show that data-driven models based on fault messages can provide better prognostics than traditional prognostics based on past failure times. We illustrate this comparison in an industrial case study, involving a critical component of the engine bleed system. The novelty of our work is the combination of new predictors related to fault messages, and the comparison of datadriven methods such as neural networks and decision trees. Our experimental results show significant performance gain compared to the baseline approach.
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PHM industrial applications, Data Driven Approaches, Big Data Analytics, Aerospace
Ahmad, R., & Kamaruddin, S. (2012). An Overview of Time-based and Condition-based Maintenance in Industrial Application. Computers & Industrial Engineering, 63(1), 135–149.
Arlot, S., Celisse, A., et al. (2010). A Survey of Cross- Validation Procedures for Model Selection. Statistics surveys, 4, 40–79.
Brotherton, T., Jahns, G., Jacobs, J., & Wroblewski, D. (2000). Prognosis of Faults in Gas Turbine Engines. In Aerospace conference (Vol. 6, pp. 163–171).
Brotherton, T., & Johnson, T. (2001). Anomaly Detection for Advanced Military Aircraft using Neural Networks. In Aerospace conference (Vol. 6, pp. 3113–3123).
Byington, C. S., & Roemer, M. J. (2002). Prognostic Enhancements to Diagnostic Systems for Improved Condition-based Maintenance (Military Aircraft). In Aerospace conference (Vol. 6, pp. 6–2815).
Camci, F. (2005). Process Monitoring, Diagnostics and Prognostics using Support Vector Machines and Hidden Markov Models. Graduate School of Wanye State University, Detroit.
Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2012). Nbclust Package: Finding the Relevant Number of Clusters in a Dataset. In UseR! 2012.
Coble, J., & Hines, J. W. (2011). Applying the General Path Model to Estimation of Remaining Useful Life. International Journal of Prognostics and Health Management, 2, 71.
Frangopol, D. M., Kallen, M.-J., & Van Noortwijk, J. M. (2004). Probabilistic Models for Life-Cycle Performance of Deteriorating Structures: Review and Future Directions. Progress in Structural Engineering and Materials, 6(4), 197–212.
Galar, D., Palo, M., Van Horenbeek, A., & Pintelon, L. (2012). Integration of Disparate Data Sources to Perform Maintenance Prognosis and Optimal Decision Making. Insight-non-destructive Testing and Condition Monitoring, 54(8), 440–445.
Garvey, D. R., & Hines, J. W. (2007). Dynamic Prognoser Architecture via the Path Classification and Estimation (PACE) Model. In Artificial Intelligence for Prognostics. in: AAAI fall symposium (pp. 44–49). GensymWebsite. (2007). Retrieved from http://www.gensym.com
Goebel, K., Saha, B., & Saxena, A. (2008). A Comparison of Three Data-driven Techniques for Prognostics. In 62nd Meeting of the Society for Machinery Failure Prevention Technology (MFPT) (pp. 119–131).
He, F., & Shi, W. (2002). WPT-SVMs based Approach for Fault Detection of Valves in Reciprocating Pumps. In American Control Conference (Vol. 6, pp. 4566–4570).
Heng, A., Zhang, S., Tan, A. C., & Mathew, J. (2009). Rotating Machinery Prognostics: State of the Art, Challenges and Opportunities. Mechanical Systems and Signal Processing, 23(3), 724–739.
Hubert, M., & Vandervieren, E. (2008). An Adjusted Boxplot for Skewed Distributions. Computational Statistics & Data Analysis, 52(12), 5186–5201.
Isermann, R. (2006). Fault-diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. Springer Science & Business Media.
Isermann, R., & Balle, P. (1997). Trends in the Application of Model-based Fault Detection and Diagnosis of Technical Processes. Control Engineering Practice, 5(5), 709–719.
James, M. L., & Atkinson, D. J. (1990). Software for Development of Expert Systems.
Knotts, R. M. (1999). Civil Aircraft Maintenance and Support Fault Diagnosis from a Business Perspective. Journal of Quality in Maintenance Engineering, 5(4), 335–348.
Kurien, J., & Nayak, P. P. (2000). Back to the Future for Consistency-based Trajectory Tracking. In AAAI/IAAI (pp. 370–377).
Loyer, J.-L., Henriques, E., & Wiseall, S. (2014). Comparison of Binary Classifiers for Data-driven Prognosis of Jet Engines Health. In European Conference of the Prognostics and Health Management Society (p. 1-12).
Lu, C. J., & Meeker, W. O. (1993). Using Degradation Measures to Estimate a Time-to-failure Distribution. Technometrics, 35(2), 161–174.
Mathur, A., Cavanaugh, K. F., Pattipati, K. R., Willett, P. K., & Galie, T. R. (2001). Reasoning and Modeling Systems in Diagnosis and Prognosis. In Aerospace/Defense Sensing, Simulation, and Controls
(pp. 194–203).
Moreira, R. d. P., & Nascimento Jr., C. L. (2012). Prognostics of Aircraft Bleed Valves using a SVM Classification Algorithm. In Aerospace Conference (pp. 1–8).
Osborne, J., & Waters, E. (2002). Four Assumptions of Multiple Regression that Researchers should always Test. Practical Assessment, Research & Evaluation, 8(2), 1–9.
Pecht, M. (2008). Prognostics and Health Management of Electronics. Wiley Online Library.
Rausand, M., & Høyland, A. (2004). System Reliability Theory: Models, Statistical Methods, and Applications (Vol. 396). John Wiley & Sons.
Sankavaram, C., Pattipati, B., Kodali, A., Pattipati, K., Azam, M., Kumar, S., & Pecht, M. (2009). Model-based and Data-driven Prognosis of Automotive and Electronic Systems. In Automation Science and Engineering (pp. 96–101).
Schwabacher, M., & Goebel, K. (2007). A Survey of Artificial Intelligence for Prognostics. In AAAI Fall Symposium (pp. 107–114).
Si, X.-S., Wang, W., Hu, C.-H., & Zhou, D.-H. (2011). Remaining Useful Life Estimation – A Review on the Statistical Data Driven Approaches. European Journal of Operational Research, 213(1), 1–14.
Steidle, C. E. (1997). The Joint Strike Fighter Program. Johns Hopkins APL Technical Digest, 18(1), 7.
Strong, E. A. (2014). Development of a Method for Incorporating Fault Codes in Prognostic Analysis.
Tukey, J. W. (1977). Exploratory Data Analysis.
Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., &Wu, B. (2006). Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Usa 454p Isbn, 13, 978–0.
Wang, P., & Vachtsevanos, G. (2001). Fault Prognostics using Dynamic Wavelet Neural Networks. AI EDAM, 15(04), 349–365.
Weld, D. S., & Kleer, J. d. (1989). Readings in Qualitative Reasoning about Physical Systems. Morgan Kaufmann Publishers Inc.
Williams, B. C., & Nayak, P. P. (1996). A Model-based Approach to Reactive Self-configuring Systems. In Proceedings of the National Conference on Artificial Intelligence (pp. 971–978).
Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., . . . others (2008). Top 10 Algorithms in Data Mining. Knowledge and Information Systems, 14(1), 1–37.
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