Integrated model for dynamics and reliability of intelligent mechatronic systems

Kaul, Thorben; Meyer, Tobias; Sextro, Walter

doi:10.1201/b19094-290

Cited by 4 publications

(8 citation statements)

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“…Weibull distribution is fitted to the simulation results to obtain parameters of shape b and scale T . 4 The reliability for the belt housing obtained from the lifetime estimator based on FEA simulations and fatigue analysis can now be used in the reliability model.…”

Section: Integrated Modeling Of Behavior and Reliabilitymentioning

confidence: 99%

“…In previous works, 4,22 a reliability model is proposed, where the set of variables ν represents a set of system components of the investigated system. Here, it is assumed that all system components have Boolean states: true tr representing a component in operable state and false fa representing a component failure.…”

Section: Reliability Modelmentioning

confidence: 99%

“…Since component degradation is crucially dependent on Figure 2. Structure of integrated model according to Kaul et al 4 these loads, model-based approaches are used to estimate the influence of load on the reduction of component lifetime. These estimators are used to perform prognostics of the useful component lifetime under current conditions represented by the characteristic maneuver.…”

Section: Lifetime Estimators Of System Componentsmentioning

confidence: 99%

“…The lifetime estimator of the friction plates is introduced in detail in a previous work. 4 Additional lifetime estimators are taken from the literature, for example, for ball-bearings, 16 resistive (displacement) sensors and micro controllers, 17 and are not shown in detail.…”

Section: Lifetime Estimators Of System Componentsmentioning

confidence: 99%

“…The integrated model introduces a methodology for automated model-based transformation of a behavior model into a reliability model. 4 Bayesian networks (BNs) are used as basis for the reliability model.…”

Section: Introductionmentioning

confidence: 99%

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Formulation of reliability-related objective functions for design of intelligent mechatronic systems

Kaul

Meyer

Sextro

2017

Proceedings of the Institution of Mechanical Engineers, Part O:

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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.

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Section: Integrated Modeling Of Behavior and Reliabilitymentioning

confidence: 99%