Development of Diagnostics & Prognostics for Condition-Based Decision Support
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Published
Jul 8, 2014
Heiko Mikat
Antonino Marco Siddiolo
Matthias Buderath
Abstract
The market for civil and military aerospace applications shows an increasing demand for service-based contracting ("Performance Based Contracting" - PBC). These contractual-concepts are based on guaranteed performance indicators over a fixed period, enabling a share of the financial risk between the system provider and the operator. The realization of efficient condition monitoring capabilities and reliable prognostics for prediction of spares and personnel demands has been identified as one key enabling factor for a successful implementation of PBC-concepts. To ensure an optimal incorporation of the diagnostic & prognostic functions needed for this purpose, the integration has to be considered as a standard design task during the development and certification phase, rising the need to adapt existing development processes. This adaption includes the extension of certification guidelines, definition of dedicated requirements and realization of innovative verification strategies. During the last years Airbus Defence & Space was working on the definition of a development process for integration of an innovative health management strategy into new aircraft systems to support condition-based operations. Following a summary of condition monitoring and prognostic techniques, selected requirements and guidelines for development of diagnostic & prognostic functions will be presented and discussed.
How to Cite
Mikat, H., Siddiolo, A. M., & Buderath, M. (2014). Development of Diagnostics & Prognostics for Condition-Based Decision Support. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1513
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Keywords
diagnostics and prognostics, condition-based maintenance, Automated Decision Support, Autonomous Logistics, Health Management Systems, Prognostic Information Management
References
C., Schrader, R. & Klingauf, U. (2012). Unscented Kalman Filter with Gaussian Process Degradation Model for Bearing Fault Prognosis. Proceedings of the First European Conference of the Prognostics and Health Management Society 2012. July 2-5, Dresden, Germany
Bechhoefer, E. (2008). A Method for Generalized Prognostics of a Component Using Paris Law. Proceedings of American Helicopter Society 64th Annual Forum 2008. April 29 - May 1, Montreal, Canada
Brotherton, T. (2000). Prognosis of Faults in Gas Turbine Engines. Proceedings of IEEE Aerospace Conference. March 18-25, Big Sky, Montana, USA. doi: 10.1109/AERO.2000.877892
Candela, J.Q., Girard, A., Larsen, J. & Rasmussen, C.E. (2003). Propagation of uncertainty in Bayesian kernel models - application to multiple-step ahead forecasting. Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. April 1-6, Hong Kong, China
Celaya, J.R., Kulkarni, C., Biswas, G., Saha, S. & Goebel, K. (2011). A Model-based Prognostics Methodology for Electrolytic Capacitors Based on Electrical Overstress Accelerated Aging. Proceedings of the Annual Conference of the Prognostics and Health Management Society. September 25-29, Montreal, Quebec, Canada
Chen, H. (2011). A Multiple Model Prediction Algorithm for CNC Machine Wear PHM. International Journal of Prognostics and Health Management 2011, issue 2. ISSN2153-2648
Chung-Chien, C. & Cheng-Ching, Y. (1990). On-Line Fault Diagnosis Using the Signed Directed Graph. Industrial & Engineering Chemistry Research, vol. 29, pp. 1290-1299. doi: 10.1021/ie00103a031
Crepin, P.Y. & Kreß, R. (2000). Model Based Fault Detection for an Aircraft Actuator. Proceedings of the International Council of the Aeronautical Sciences. August 28 - September 1, Harrogate, UK
Daigle, M., Saha, B. & Goebel, K. (2012). A Comparison of Filter-based Approaches for Model-based Prognostics. Proceedings of IEEE Aerospace Conference. March 3-10, Big Sky, Montana, USA. doi: 10.1109/AERO.2012.6187363
Daigle, M., Saxena, A. & Goebel, K. (2012). An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation. Proceedings of the First European Conference of the Prognostics and Health Management Society. July 2-5, Dresden, Germany
Edwards, D., Orchard, M. E., Tang, L., Goebel, K., & Vachtsevanos, G. (2010). Impact in input uncertainty on failure prognostic algorithms: Extending the remaining useful life of nonlinear systems. Proceedings of the Annual Conference of the Prognostics and Health Management Society. October 10-16, Portland, Oregon, USA
Gebraeel, N.Z. & Lawley, M.A. (2008). A Neural Network Degradation Model for Computing and Updating Residual Life Distributions. IEEE Transactions on Automation Science and Engineering, vol. 5, issue 1, pp. 154-163
Goebel, K., Saha, B. & Saxena, A. (2008). A Comparison of three Data-Driven Techniques for Prognostics. Proceedings of the 62nd Meeting of the Society for Machinery Failure Prevention Technology. April 6-8, Virginia Beach, Virginia, USA
Groer, P.G. (2000). Analyses of Time-To-Failure with a Weibull Model. Proceedings of the Maintenance and Reliability Conference. May 8-10, Knoxville, Tennessee, USA
Hunt, S.R. & Hebden, I.G. (1998). Eurofighter 2000 An Integrated Approach to Structural Health and Usage Monitoring. RTO AVT Specialists Meeting on "Exploitation of Structural Leads/Health Data for Reduced Life Cycle Costs". May 11-12,. Brussels, Belgium
Isermann, R. (1992). Identifikation dynamischer Systeme I - Grundlegende Methoden. Springer-Verlag
Isermann, R. (2006). Fault Diagnosis Systems. Springer-Verlag
ISO/IEC Guide 98-3:2008 Uncertainty of measurement - Part 3: Guide to the expression of uncertainty in measurement (GUM:1995)
Jaloretto, M.R., Bizarria, C. & Galvao, R. (2009). Trend Analysis for Prognostics and Health Monitoring. Brazilian Symposium on Aerospace Eng. & Applications. September 14-16, S.J. Campos, SP, Brazil
Javed, K., Gouriveau, R., Zemouri, R. & Zerhouni, N. (2011). Improving Data-Driven Prognostics by Assessing Predictability of Features. Annual Conference of the Prognostics and Health Management Society. September 25-29, Montreal, Quebec, Canada
Jazouli, T. & Sandborn, P. (2011). Using PHM to Meet Availability-Based Contracting Requirements. Proceedings of IEEE Aerospace Conference. March 5-12, Big Sky, Montana, USA
Julier, S. J. & Uhlmann, J. K. (2004). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, vol. 92, issue 3, pp. 401–422. doi: 10.1109/JPROC.2003.823141
Kacprzynski, G.J., Liberson, A., Palladino, A., Roemer, M.J., Hess, A.J. & Begin, M. (2004). Metrics and development tools for prognostic algorithms. Proceedings of IEEE Aerospace Conference 2004. March 6-13, Big Sky, MT, USA. doi: 10.1109/AERO.2004.1368198
Khawaja, T. & Vachtsevanos, G. (2009). A novel Architecture for on-line Failure Prognosis using Probabilistic Least Squares Support Vector Regression Machines. Proceedings of the Annual Conference of the Prognostics and Health Management Society. September 27 – October 1, San Diego, California, USA
Kühl, T. & Pakszies, A. (2011). Condition Monitoring of Civil Jet Engines during Flight. LTH Triebwerkstechnologie, AT 58 100-01
Leao, B.P., Yoneyama, T., Rocha, G.C. & Fitzgibbon, K.T. (2008). Prognostics Performance Metrics and their Relation to Requirements, Design, Verification and Cost-Benefit. International Conference on Prognostics and Health Management. October 6-9, Denver, CO, USA. doi: 10.1109/PHM.2008.4711429
Lee, S.G., Ma, Y.-S., Thimm, G.L. & Verstraeten, J. (2008). Product lifecycle management in aviation maintenance, repair and overhaul. Computers in Industry, vol. 59, issues 2–3, pp. 296–303
Li, X.R. & Jilkov, V.P. (2003). A Survey of Maneuvering Target Tracking-Part V: Multiple-Model Methods. IEEE Transactions on Aerospace and Electronic Systems, vol. 41, issue 4, pp. 1255-1321. doi: 10.1109/TAES.2005.1561886
Liu, D., Guo, L., Pang, J. & Peng, Y. (2013). A Fusion Framework with Nonlinear Degradation Improvement for Remaining Useful Life Estimation of Lithium-ion Batteries. Proceedings of the Annual Conference of the Prognostics and Health Management Society 2013. October 14-17, New Orleans, Louisiana, USA
Liu, D., Pang, J., Zhou, J., Peng, Y., Pecht, M. (2013). Prognostics for State of Health Estimation of Lithium-Ion Batteries based on Combination Gaussian Process Functional Regression. Microelectronics Reliability, vol. 53, Issue 6, pp. 832-839
Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2008). Model-based prognostic techniques applied to a suspension system. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 38, Issue 5, pp. 1156-1168. doi: 10.1109/TSMCA.2008.2001055
Lybeck, N., Marble, S., Morton, B. (2007). Validating Prognostic Algorithms: A Case Study Using Comprehensive Bearing Fault Data. IEEE Aerospace Conference 2007. March 3-10, Big Sky, Montana, USA
Zio, E., Maio, F.D., (2010). A Data-Driven Fuzzy Approach for Predicting the Remaining Useful Life in Dynamic Failure Scenarios of a Nuclear System. Reliability Engineering & System Safety, vol. 95, Issue 1, pp. 49-57
Medjaher, K., Zerhouni, N., Baklouti, J. (2013). Data-Driven Prognostics Based on Health Indicator Construction: Application to PRONOSTIA’s Data. European Control Conference 2013. July 17-19, Zurich, Switzerland
Mikat, H., Siddiolo, A.M., Buderath, M. (2012). Virtual Framework for Validation and Verification of System Design Requirements to enable Condition Based Maintenance. European Conference of the Prognostics and Health Management Society 2012. July 3-5, Dresden, Germany
Münchhof, M. (2006), Model-based Fault Detection for a Hydraulic Servo Axis. Doctoral dissertation. Technische Universität Darmstadt, Darmstadt, Germany
Pandian, A., Ali, A. (2010). A Review of Recent Trends in Machine Diagnosis and Prognosis Algorithms. Reliability Engineering & System Safety, vol. 95, Issue 1, pp. 49-57
Pipe, K. (2003). Measuring the Performance of a HUM System - Features that Count. Proceedings of the 3rd DSTO International Conference on Health and Usage Monitoring. February 17-18, Melbourne, Australia
Quinonero-Candela, J., Hansen, L.K. (2002). Time series prediction based on the relevance vector machine with adaptive kernels. International Conference on Acoustics, Speech, and Signal Processing, vol. 1, pp. 985-988, doi: 10.1109/ICASSP.2002.5743959
Rao, B.K.N., Srinivasa, Pai, P., Nagabhushana, T.N. (2012). Failure Diagnosis and Prognosis of Rolling - Element Bearings using Artificial Neural Networks: A Critical Overview. Journal of Physics: Conference Series, vol. 364, Issue 1
Rasmussen, C.E. (1996). Evaluation of Gaussian Processes and Other Methods for Non-linear Regression. Doctoral dissertation., University of Toronto, Toronto
Reimann, J., Kacprzynski, G., Cabral, D., Marim, R. (2009). Using Condition Based Maintenance to Improve the Profitability of Performance Based Logistic Contracts. Annual Conference of the Prognostics and Health Management Society 2009. September 27-October 1, San Diego, California, USA
Saha, B., Quach, C. C., & Goebel, K. (2012). Optimizing battery life for electric UAVs using a Bayesian framework. Proceedings of the IEEE Aerospace Conference 2012. March 3-10, Bis Sky, Montana, USA
Sankararaman, S., Ling, Y., Shantz, C., & Mahadevan, S. (2011). Uncertainty quantification in fatigue crack growth prognosis. International Journal of Prognostics and Health Management 2011, vol. 2, Issue 2
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S. and Schwabacher, M. (2008). Metrics for Evaluating Performance of Prognostic Techniques. International Conference on Prognostics and Health Management 2008. October 6-9, Denver, Colorado, USA
Saxena, A., Roychoudhury, I., Celaya, J.R., Saha, S., Saha, B. (2010). Requirements Specifications for Prognostics: An Overview. American Institute of Aeronautics and Astronautics
Schaab, J. (2011). Trusted Health Assessment of Dynamic Systems Based on Hybrid Joint Estimation. Doctoral dissertation. Technische Universität Darmstadt, Darmstadt, Germany
Schaab, J., Harrington, M. and Klingauf, U. (2007). On-board Health Assessment of an Electro-mechanical Actuator Using Wavelet Features. Proceedings of the 1st CEAS European Air and Space Conference 2007. September 10-13, Berlin, Germany
Schwabacher, M.A., (2005). A Survey of Data-Driven Prognostics. American Institute of Aeronautics and Astronautics Srivastra, A.N., Mah, R.W., Meyer, C. (2009). Integrated Vehicle Health Management. NASA Aviation Safety Program, Technical Plan 2009
Venkat, V., Raghunathan, R., Kewen, Y. and Suryari, N.K. (2003). A review of process fault detection and diagnosis Part I: Quantitative model-based methods. Computers and Chemical Engineering 2003, vol. 27, Issue 3, pp. 293-311
Venkat, V., Raghunathan, R., Kewen, Y. and Suryari, N.K. (2003). A review of process fault detection and diagnosis Part II: Qualitative models and search strategies. Computers and Chemical Engineering 2003, vol. 27, Issue 3, pp. 313-326
Venkat, V., Raghunathan, R., Kewen, Y. and Suryari, N.K. (2003). A review of process fault detection and diagnosis Part III: Process history based methods. Computers and Chemical Engineering 2003, vol. 27, Issue 3, pp. 327-346
Wang, Y., Wang, W. (2012). Failure Prognostic of Systems with Hidden Degradation Process. Journal of Systems Engineering and Electronics, vol. 23, Issue 2, pp. 314-324, doi: 10.1109/JSEE.2012.00039
Wilmering, T.J., Ramesh, A.V. 2004. Assessing the Impact of Health Management Approaches on System Total Cost of Ownership. IEEE Aerospace Conference 2005. March 5-12, Big Sky, Montana, USA
Ypma, A. (2001). Learning Methods for Machine Vibration Analysis and Health Monitoring. Doctoral dissertation. Technische Universiteit Delft, Delft, Netherlands
Zhang, X., Pisu, P. (2012). An Unscented Kalman Filter Based Approach for the Health-Monitoring and Prognostics of a Polymer Electrolyte Membrane Fuel Cell. Annual Conference on Prognostics and Health Management 2012. September 23-27, Minneapolis, Minnesota, USA
Zhu, J., Yoon, J.M., He, D., Qu, Y., Bechhoefer, E. (2013). Lubrication Oil Condition Monitoring and Remaining Useful Life Prediction with Particle Filtering. International Journal of Prognostics and Health Management 2011, vol. 4, Special Issue Wind Turbine PHM
Bechhoefer, E. (2008). A Method for Generalized Prognostics of a Component Using Paris Law. Proceedings of American Helicopter Society 64th Annual Forum 2008. April 29 - May 1, Montreal, Canada
Brotherton, T. (2000). Prognosis of Faults in Gas Turbine Engines. Proceedings of IEEE Aerospace Conference. March 18-25, Big Sky, Montana, USA. doi: 10.1109/AERO.2000.877892
Candela, J.Q., Girard, A., Larsen, J. & Rasmussen, C.E. (2003). Propagation of uncertainty in Bayesian kernel models - application to multiple-step ahead forecasting. Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. April 1-6, Hong Kong, China
Celaya, J.R., Kulkarni, C., Biswas, G., Saha, S. & Goebel, K. (2011). A Model-based Prognostics Methodology for Electrolytic Capacitors Based on Electrical Overstress Accelerated Aging. Proceedings of the Annual Conference of the Prognostics and Health Management Society. September 25-29, Montreal, Quebec, Canada
Chen, H. (2011). A Multiple Model Prediction Algorithm for CNC Machine Wear PHM. International Journal of Prognostics and Health Management 2011, issue 2. ISSN2153-2648
Chung-Chien, C. & Cheng-Ching, Y. (1990). On-Line Fault Diagnosis Using the Signed Directed Graph. Industrial & Engineering Chemistry Research, vol. 29, pp. 1290-1299. doi: 10.1021/ie00103a031
Crepin, P.Y. & Kreß, R. (2000). Model Based Fault Detection for an Aircraft Actuator. Proceedings of the International Council of the Aeronautical Sciences. August 28 - September 1, Harrogate, UK
Daigle, M., Saha, B. & Goebel, K. (2012). A Comparison of Filter-based Approaches for Model-based Prognostics. Proceedings of IEEE Aerospace Conference. March 3-10, Big Sky, Montana, USA. doi: 10.1109/AERO.2012.6187363
Daigle, M., Saxena, A. & Goebel, K. (2012). An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation. Proceedings of the First European Conference of the Prognostics and Health Management Society. July 2-5, Dresden, Germany
Edwards, D., Orchard, M. E., Tang, L., Goebel, K., & Vachtsevanos, G. (2010). Impact in input uncertainty on failure prognostic algorithms: Extending the remaining useful life of nonlinear systems. Proceedings of the Annual Conference of the Prognostics and Health Management Society. October 10-16, Portland, Oregon, USA
Gebraeel, N.Z. & Lawley, M.A. (2008). A Neural Network Degradation Model for Computing and Updating Residual Life Distributions. IEEE Transactions on Automation Science and Engineering, vol. 5, issue 1, pp. 154-163
Goebel, K., Saha, B. & Saxena, A. (2008). A Comparison of three Data-Driven Techniques for Prognostics. Proceedings of the 62nd Meeting of the Society for Machinery Failure Prevention Technology. April 6-8, Virginia Beach, Virginia, USA
Groer, P.G. (2000). Analyses of Time-To-Failure with a Weibull Model. Proceedings of the Maintenance and Reliability Conference. May 8-10, Knoxville, Tennessee, USA
Hunt, S.R. & Hebden, I.G. (1998). Eurofighter 2000 An Integrated Approach to Structural Health and Usage Monitoring. RTO AVT Specialists Meeting on "Exploitation of Structural Leads/Health Data for Reduced Life Cycle Costs". May 11-12,. Brussels, Belgium
Isermann, R. (1992). Identifikation dynamischer Systeme I - Grundlegende Methoden. Springer-Verlag
Isermann, R. (2006). Fault Diagnosis Systems. Springer-Verlag
ISO/IEC Guide 98-3:2008 Uncertainty of measurement - Part 3: Guide to the expression of uncertainty in measurement (GUM:1995)
Jaloretto, M.R., Bizarria, C. & Galvao, R. (2009). Trend Analysis for Prognostics and Health Monitoring. Brazilian Symposium on Aerospace Eng. & Applications. September 14-16, S.J. Campos, SP, Brazil
Javed, K., Gouriveau, R., Zemouri, R. & Zerhouni, N. (2011). Improving Data-Driven Prognostics by Assessing Predictability of Features. Annual Conference of the Prognostics and Health Management Society. September 25-29, Montreal, Quebec, Canada
Jazouli, T. & Sandborn, P. (2011). Using PHM to Meet Availability-Based Contracting Requirements. Proceedings of IEEE Aerospace Conference. March 5-12, Big Sky, Montana, USA
Julier, S. J. & Uhlmann, J. K. (2004). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, vol. 92, issue 3, pp. 401–422. doi: 10.1109/JPROC.2003.823141
Kacprzynski, G.J., Liberson, A., Palladino, A., Roemer, M.J., Hess, A.J. & Begin, M. (2004). Metrics and development tools for prognostic algorithms. Proceedings of IEEE Aerospace Conference 2004. March 6-13, Big Sky, MT, USA. doi: 10.1109/AERO.2004.1368198
Khawaja, T. & Vachtsevanos, G. (2009). A novel Architecture for on-line Failure Prognosis using Probabilistic Least Squares Support Vector Regression Machines. Proceedings of the Annual Conference of the Prognostics and Health Management Society. September 27 – October 1, San Diego, California, USA
Kühl, T. & Pakszies, A. (2011). Condition Monitoring of Civil Jet Engines during Flight. LTH Triebwerkstechnologie, AT 58 100-01
Leao, B.P., Yoneyama, T., Rocha, G.C. & Fitzgibbon, K.T. (2008). Prognostics Performance Metrics and their Relation to Requirements, Design, Verification and Cost-Benefit. International Conference on Prognostics and Health Management. October 6-9, Denver, CO, USA. doi: 10.1109/PHM.2008.4711429
Lee, S.G., Ma, Y.-S., Thimm, G.L. & Verstraeten, J. (2008). Product lifecycle management in aviation maintenance, repair and overhaul. Computers in Industry, vol. 59, issues 2–3, pp. 296–303
Li, X.R. & Jilkov, V.P. (2003). A Survey of Maneuvering Target Tracking-Part V: Multiple-Model Methods. IEEE Transactions on Aerospace and Electronic Systems, vol. 41, issue 4, pp. 1255-1321. doi: 10.1109/TAES.2005.1561886
Liu, D., Guo, L., Pang, J. & Peng, Y. (2013). A Fusion Framework with Nonlinear Degradation Improvement for Remaining Useful Life Estimation of Lithium-ion Batteries. Proceedings of the Annual Conference of the Prognostics and Health Management Society 2013. October 14-17, New Orleans, Louisiana, USA
Liu, D., Pang, J., Zhou, J., Peng, Y., Pecht, M. (2013). Prognostics for State of Health Estimation of Lithium-Ion Batteries based on Combination Gaussian Process Functional Regression. Microelectronics Reliability, vol. 53, Issue 6, pp. 832-839
Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2008). Model-based prognostic techniques applied to a suspension system. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 38, Issue 5, pp. 1156-1168. doi: 10.1109/TSMCA.2008.2001055
Lybeck, N., Marble, S., Morton, B. (2007). Validating Prognostic Algorithms: A Case Study Using Comprehensive Bearing Fault Data. IEEE Aerospace Conference 2007. March 3-10, Big Sky, Montana, USA
Zio, E., Maio, F.D., (2010). A Data-Driven Fuzzy Approach for Predicting the Remaining Useful Life in Dynamic Failure Scenarios of a Nuclear System. Reliability Engineering & System Safety, vol. 95, Issue 1, pp. 49-57
Medjaher, K., Zerhouni, N., Baklouti, J. (2013). Data-Driven Prognostics Based on Health Indicator Construction: Application to PRONOSTIA’s Data. European Control Conference 2013. July 17-19, Zurich, Switzerland
Mikat, H., Siddiolo, A.M., Buderath, M. (2012). Virtual Framework for Validation and Verification of System Design Requirements to enable Condition Based Maintenance. European Conference of the Prognostics and Health Management Society 2012. July 3-5, Dresden, Germany
Münchhof, M. (2006), Model-based Fault Detection for a Hydraulic Servo Axis. Doctoral dissertation. Technische Universität Darmstadt, Darmstadt, Germany
Pandian, A., Ali, A. (2010). A Review of Recent Trends in Machine Diagnosis and Prognosis Algorithms. Reliability Engineering & System Safety, vol. 95, Issue 1, pp. 49-57
Pipe, K. (2003). Measuring the Performance of a HUM System - Features that Count. Proceedings of the 3rd DSTO International Conference on Health and Usage Monitoring. February 17-18, Melbourne, Australia
Quinonero-Candela, J., Hansen, L.K. (2002). Time series prediction based on the relevance vector machine with adaptive kernels. International Conference on Acoustics, Speech, and Signal Processing, vol. 1, pp. 985-988, doi: 10.1109/ICASSP.2002.5743959
Rao, B.K.N., Srinivasa, Pai, P., Nagabhushana, T.N. (2012). Failure Diagnosis and Prognosis of Rolling - Element Bearings using Artificial Neural Networks: A Critical Overview. Journal of Physics: Conference Series, vol. 364, Issue 1
Rasmussen, C.E. (1996). Evaluation of Gaussian Processes and Other Methods for Non-linear Regression. Doctoral dissertation., University of Toronto, Toronto
Reimann, J., Kacprzynski, G., Cabral, D., Marim, R. (2009). Using Condition Based Maintenance to Improve the Profitability of Performance Based Logistic Contracts. Annual Conference of the Prognostics and Health Management Society 2009. September 27-October 1, San Diego, California, USA
Saha, B., Quach, C. C., & Goebel, K. (2012). Optimizing battery life for electric UAVs using a Bayesian framework. Proceedings of the IEEE Aerospace Conference 2012. March 3-10, Bis Sky, Montana, USA
Sankararaman, S., Ling, Y., Shantz, C., & Mahadevan, S. (2011). Uncertainty quantification in fatigue crack growth prognosis. International Journal of Prognostics and Health Management 2011, vol. 2, Issue 2
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S. and Schwabacher, M. (2008). Metrics for Evaluating Performance of Prognostic Techniques. International Conference on Prognostics and Health Management 2008. October 6-9, Denver, Colorado, USA
Saxena, A., Roychoudhury, I., Celaya, J.R., Saha, S., Saha, B. (2010). Requirements Specifications for Prognostics: An Overview. American Institute of Aeronautics and Astronautics
Schaab, J. (2011). Trusted Health Assessment of Dynamic Systems Based on Hybrid Joint Estimation. Doctoral dissertation. Technische Universität Darmstadt, Darmstadt, Germany
Schaab, J., Harrington, M. and Klingauf, U. (2007). On-board Health Assessment of an Electro-mechanical Actuator Using Wavelet Features. Proceedings of the 1st CEAS European Air and Space Conference 2007. September 10-13, Berlin, Germany
Schwabacher, M.A., (2005). A Survey of Data-Driven Prognostics. American Institute of Aeronautics and Astronautics Srivastra, A.N., Mah, R.W., Meyer, C. (2009). Integrated Vehicle Health Management. NASA Aviation Safety Program, Technical Plan 2009
Venkat, V., Raghunathan, R., Kewen, Y. and Suryari, N.K. (2003). A review of process fault detection and diagnosis Part I: Quantitative model-based methods. Computers and Chemical Engineering 2003, vol. 27, Issue 3, pp. 293-311
Venkat, V., Raghunathan, R., Kewen, Y. and Suryari, N.K. (2003). A review of process fault detection and diagnosis Part II: Qualitative models and search strategies. Computers and Chemical Engineering 2003, vol. 27, Issue 3, pp. 313-326
Venkat, V., Raghunathan, R., Kewen, Y. and Suryari, N.K. (2003). A review of process fault detection and diagnosis Part III: Process history based methods. Computers and Chemical Engineering 2003, vol. 27, Issue 3, pp. 327-346
Wang, Y., Wang, W. (2012). Failure Prognostic of Systems with Hidden Degradation Process. Journal of Systems Engineering and Electronics, vol. 23, Issue 2, pp. 314-324, doi: 10.1109/JSEE.2012.00039
Wilmering, T.J., Ramesh, A.V. 2004. Assessing the Impact of Health Management Approaches on System Total Cost of Ownership. IEEE Aerospace Conference 2005. March 5-12, Big Sky, Montana, USA
Ypma, A. (2001). Learning Methods for Machine Vibration Analysis and Health Monitoring. Doctoral dissertation. Technische Universiteit Delft, Delft, Netherlands
Zhang, X., Pisu, P. (2012). An Unscented Kalman Filter Based Approach for the Health-Monitoring and Prognostics of a Polymer Electrolyte Membrane Fuel Cell. Annual Conference on Prognostics and Health Management 2012. September 23-27, Minneapolis, Minnesota, USA
Zhu, J., Yoon, J.M., He, D., Qu, Y., Bechhoefer, E. (2013). Lubrication Oil Condition Monitoring and Remaining Useful Life Prediction with Particle Filtering. International Journal of Prognostics and Health Management 2011, vol. 4, Special Issue Wind Turbine PHM
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