@ARTICLE{Kaul2017,
  author = {Kaul, Thorben AND Meyer, Tobias AND Sextro, Walter},
  title = {Formulation of reliability-related objective functions for design
	of intelligent mechatronic systems},
  journal = {SAGE Journals},
  year = {2017},
  volume = {Vol. 231(4)},
  pages = {390 - 399},
  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.},
  doi = {10.1177/1748006X17709376},
  keywords = {Integrated model, reliability, system behavior, Bayesian network,
	multiobjective optimization},
  owner = {ekubi},
  timestamp = {2017.09.04}
}