Adaptive Load-Allocation for Prognosis-Based Risk Management
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
It is an inescapable truth that no matter how well a system is designed it will degrade, and if degrading parts are not repaired or replaced the system will fail. Avoiding the expense and safety risks associated with system failures is certainly a top priority in many systems; however, there is also a strong motivation not to be overly cautious in the design and maintenance of systems, due to the expense of maintenance and the undesirable sacrifices in performance and cost effectiveness incurred when systems are over designed for safety. This paper describes an analytical process that starts with the derivation of an expression to evaluate the desirability of future control outcomes, and eventually produces control routines that use uncertain prognostic information to optimize derived risk metrics. A case study on the design of fault-adaptive control for a skid-steered robot will illustrate some of the fundamental challenges of prognostics-based control design.
How to Cite
##plugins.themes.bootstrap3.article.details##
prognostics, load-allocation, fault adaptive control, risk management
Arulampalam, S., Maskell, S., Gordon, N., & Clapp, T. (2002, Feb.). A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174-188.
Bole, B., Brown, D., Pei, H.-L., Goebel, K., Tang, L., & Vachtsevanos, G. (2010, Oct.). Fault adaptive control of overactuated systems using prognostic estimation. In International conference on prognostics and health management (PHM).
Caglayan, K., Allen, S., & Wehmuller, K. (1988). Evaluation of a second generation reconfiguration strategy for aircraft flight control systems subjected to actuator failure surface damage. In Proceedings of the IEEE national aerospace and electronics conference (p. 520-529).
Doyle, J., Glover, K., Khargonekar, P., & Francis, B. (1987). State-space solutions to standard h2 and h∞ optimal control problems. IEEE Transactions on Automatic control, 33, 831-847.
Gergely, E., Spoiala, D., Spoiala, V., Silaghi, H., & Nagy, Z. (2008). Design framework for risk mitigation in industrial PLC control. In IEEE international conference on automation, quality and testing, robotics (Vol. 2, p. 198-202).
Gokdere, L., Bogdano, A., Chiu, S., Keller, K., & Vian, J. (2006). Adaptive control of actuator lifetime. In IEEE aerospace conference.
Harkegard, O., & Glad, T. (2005, Jan.). Resolving actuator redundancy - optimal control vs. control allocation. Automatica, 41(1), 137-144.
Hattori, Y., Koibuchi, K., & Yokoyama., T. (2002, Sept.). Force and moment control with nonlinear optimum distribution for vehicle dynamics. In Proc. of the 6th international symposium on advanced vehicle control.
Karpenko, M., & Sepehri, N. (2005, January). Fault tolerant control of a servohydraulic positioning system with crossport leakage. IEEE Transactions on Control Systems Technology, 13(1), 155-161.
Lauterbach, B., & Schulz, P. (1990). Pricing warrants: An empirical study of the black-scholes model and its alternatives. Journal of Finance, 45, 1181-1209.
Monaco, J., Ward, D., & Bateman, A. (2004, Sept.). A retrofit architecture for model-based adaptive flight control. In AIAA 1st intelligent systems technical conference.
Montsinger, V. M. (1930). Loading transformers by temperature. Transactions of the American Institute of Electrical Engineers, 32.
Oppenheimer, M., Doman, D., & Bolender, M. (2006). Control allocation for over-actuated systems. In 14th mediterranean conference on control and automation.
Orchard, M., Kacprzynski, G., Goebel, K., Saha, B., & Vacht- sevanos, G. (2008). Advances in uncertainty representation and management for particle filtering applied to prognostics. In International conference on prognostics and health management PHM.
Rao, B. (1998). Handbook of condition monitoring (A. Davies, Ed.). Chapham and Hall.
SAE. (1994). Potential failure mode and effects analysis in design (design FMEA) and potential failure mode and effects analysis in manufacturing and assembly processes, reference manual (Tech. Rep. No. J1739).
Saglimbene, M. (2009). Reliability analysis techniques: How they relate to aircraft certification. In Reliability and maintainability symposium (p. 218-222).
Saha, B., & Goebel, K. (2008). Uncertainty management for diagnostics and prognostics of batteries using bayesian techniques. In IEEE aerospace conference.
Schreiner, A., Balzer, G., & Precht, A. (2008). Risk analysis of distribution systems using value at risk methodology. In Proceedings of the 10th international conference on probabilistic methods applied to power systems.
Schreiner, A., Balzer, G., & Precht, A. (2010). Risk sensitivity of failure rate and maintenance expenditure: application of var metrics in risk management. In 15th IEEE mediterranean electrotechnical conference (p. 1624- 1629).
Sheppard, J., Butcher, S., Kaufman, M., & MacDougall, C. (2006). Not-so-naïve bayesian networks and unique identification in developing advanced diagnostics. In IEEE aerospace conference (p. 1-13).
Srivastava, A., Mah, R., & Meyer, C. (2008, Dec.). Integrated vehicle health management automated detection, diagnosis, prognosis to enable mitigation of adverse events during fight (Tech. Rep.). Version 2.02, National Aeronautics and Space Administration.
Venkataraman, S. (1997). Value at risk for a mixture of normal distributions: the use of quasi-bayesian estimation techniques. Economic Perspectives, 2-13.
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.