by Kaul, Thorben, Meyer, Tobias and Sextro, Walter
Abstract:
State-of-the-art mechatronic systems offer inherent intelligence that enables them to autonomously adapt their behavior to current environmental conditions and to their own system state. This autonomous behavior adaptation is made possible by software in combination with complex sensor and actuator systems and by sophisticated information processing, all of which make these systems increasingly complex. This increasing complexity makes the design process a challenging task and brings new complex possibilities for operation and maintenance. However, with the risk of increased system complexity also comes the chance to adapt system behavior based on current reliability, which in turn increases reliability. The development of such an adaption strategy requires appropriate methods to evaluate reliability based on currently selected system behavior. A common approach to implement such adaptivity is to base system behavior on different working points that are obtained using multiobjective optimization. During operation, selection among these allows a changed operating strategy. To allow for multiobjective optimization, an accurate system model including system reliability is required. This model is repeatedly evaluated by the optimization algorithm. At present, modeling of system reliability and synchronization of the models of behavior and reliability is a laborious manual task and thus very error-prone. Since system behavior is crucial for system reliability, an integrated model is introduced that integrates system behavior and system reliability. The proposed approach is used to formulate reliability-related objective functions for a clutch test rig that are used to compute feasible working points using multiobjective optimization.
Reference:
Kaul, T.; Meyer, T.; Sextro, W.: Formulation of reliability-related objective functions for design of intelligent mechatronic systems. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, volume 231, 2017.
Bibtex Entry:
@article{Kaul2017,
howpublished = {Journal},
title = {Formulation of reliability-related objective functions for design of intelligent mechatronic systems},
volume = {231},
issn = {1748-006X, 1748-0078},
url = {http://journals.sagepub.com/doi/10.1177/1748006X17709376},
doi = {10.1177/1748006X17709376},
language = {en},
abstract = {State-of-the-art mechatronic systems offer inherent
intelligence that enables them to autonomously adapt their behavior
to current environmental conditions and to their own system state.
This autonomous behavior adaptation is made possible by software in
combination with complex sensor and actuator systems and by
sophisticated information processing, all of which make these systems
increasingly complex. This increasing complexity makes the design
process a challenging task and brings new complex possibilities for
operation and maintenance. However, with the risk of increased system
complexity also comes the chance to adapt system behavior based on
current reliability, which in turn increases reliability. The
development of such an adaption strategy requires appropriate methods
to evaluate reliability based on currently selected system behavior.
A common approach to implement such adaptivity is to base system
behavior on different working points that are obtained using
multiobjective optimization. During operation, selection among these
allows a changed operating strategy. To allow for multiobjective
optimization, an accurate system model including system reliability
is required. This model is repeatedly evaluated by the optimization
algorithm. At present, modeling of system reliability and
synchronization of the models of behavior and reliability is a
laborious manual task and thus very error-prone. Since system behavior
is crucial for system reliability, an integrated model is introduced
that integrates system behavior and system reliability. The proposed
approach is used to formulate reliability-related objective functions
for a clutch test rig that are used to compute feasible working
points using multiobjective optimization.},
number = {4},
journal = {Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability},
author = {Kaul, Thorben and Meyer, Tobias and Sextro, Walter},
month = aug,
year = {2017},
pages = {390--399}
}