Closed-loop Control System for the Reliability of Intelligent Mechatronic Systems

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Tobias Meyer Walter Sextro

Abstract

So-called reliability adaptive systems are able to adapt their system behavior based on the current reliability of the system. This allows them to react to changed operating conditions or
faults within the system that change the degradation behavior. To implement such reliability adaptation, self-optimization can be used. A self-optimizing system pursues objectives, of which the priorities can be changed at runtime, in turn changing the system behavior.
When including system reliability as an objective of the system, it becomes possible to change the system based on the current reliability as well. This capability can be used to control the reliability of the system throughout its operation period in order to achieve a pre-defined or user-selectable system lifetime. This way, optimal planning of maintenance intervals is possible while also using the system capabilities to their full extent.
Our proposed control system makes it possible to react to changed degradation behavior by selecting objectives of the self-optimizing system and in turn changing the operating parameters
in a closed loop. A two-stage controller is designed which is used to select the currently required priorities of the objectives in order to fulfill the desired usable lifetime. Investigations using a model of an automotive clutch system serve to demonstrate the feasibility of our controller. It is shown that the desired lifetime can be achieved reliably.

How to Cite

Meyer, T., & Sextro, W. (2014). Closed-loop Control System for the Reliability of Intelligent Mechatronic Systems. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1483
Abstract 35 | PDF Downloads 25

##plugins.themes.bootstrap3.article.details##

Keywords

self-optimization, reliability adaptive

References
Birolini, A. (2007). Reliability engineering (5th ed.). Berlin Heidelberg: Springer.
Chena, D.,&Trivedi, K. S. (2005, October). Optimization for condition-based maintenance with semi-markov decision process. Reliability Engineering & System Safety, 90(1), 25-29. doi: 10.1016/j.ress.2004.11.001
Fleischer, G. (1973). Energetische Methode der Bestimmung des Verschleißes. Schmierungstechnik, 4(9), 269-274.
Gausemeier, J., Rammig, F. J., Sch¨afer, W., & Sextro, W. (Eds.). (2014). Dependability of self-optimizing mechatronic systems. Heidelberg New York Dordrecht London: Springer. doi: 10.1007/978-3-642-53742-4
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006, October). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483-1510. doi: 10.1016/j.ymssp.2005.09.012
Joo, S. J., Levary, R. R., & Ferris, M. E. (1997, February). Planning preventive maintenance for a fleet of police vehicles using simulation. SIMULATION, 68(2), 93-99. doi: 10.1177/003754979706800202
Kr¨uger, M., Remirez, A., Kessler, J. H., & Tr¨achtler, A. (2013, June). Discrete objective-based control for selfoptimizing systems. In American control conference (acc), 2013 (p. 3403-3408).
Meyer, T., Sondermann-W¨olke, C., Kimotho, J. K., & Sextro, W. (2013). Controlling the remaining useful lifetime using self-optimization. Chemical Engineering Transactions, 33, 625-630. doi: 10.3303/CET1333105
Section
Technical Papers