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JRM Vol.24 No.1 pp. 5-15
doi: 10.20965/jrm.2012.p0005
(2012)

Paper:

Probabilistic Planning for Predictive Condition Monitoring and Adaptation Within the Self-Optimizing Energy Management of an Autonomous Railway Vehicle

Benjamin Klöpper*, Christoph Sondermann-Wölke**,
and Christoph Romaus**

*National Institute of Informatics, Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan

**University of Paderborn, Germany

Received:
January 2, 2011
Accepted:
March 22, 2011
Published:
February 20, 2012
Keywords:
self-optimizing systems, dependability, probabilistic planning, energy management
Abstract

Self-optimizing mechatronic systems are a new class of technical systems. On the one hand, new challenges regarding dependability arise from their additional complexity and adaptivity. On the other hand, their abilities enable new concepts and methods to improve the dependability of mechatronic systems. This paper introduces a multi-level dependability concept for selfoptimizing mechatronic systems and shows how probabilistic planning can be used to improve the availability and reliability of systems in the operating phase. The general idea to improve the availability of autonomous systems by applying probabilistic planning methods to avoid energy shortages is exemplified on the example of an innovative railway vehicle.

Cite this article as:
Benjamin Klöpper, Christoph Sondermann-Wölke, and
and Christoph Romaus, “Probabilistic Planning for Predictive Condition Monitoring and Adaptation Within the Self-Optimizing Energy Management of an Autonomous Railway Vehicle,” J. Robot. Mechatron., Vol.24, No.1, pp. 5-15, 2012.
Data files:
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