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Modeling of reliability characteristics typically assumes that components and systems fail if a certain individual damage level is exceeded. Every (mechanical) system damage increases irreversibly due to employed loading and (mechanical) stress, respectively. The main issue of damage estimation is adequate determination of the actual state-of-damage. Several mathematical modeling approaches are known in the literature, focusing on the task of how loading effects damage progression (e.g., Wöhler, 1870) for wear processes. Those models are only valid for specific loading conditions, a priori assumptions, set points, etc. This contribution proposes a general model, covering adequately the deterioration of a set of comparable systems under comparable loading. The main goal of this contribution is to derive the loading-damage connection directly from observation without assuming any damage models at the outset. Moreover, the connection is not investigated in detail (e.g., to examine the changes in material, etc.) but only approximated with a probabilistic approach. The idea is subdivided into two phases: A problem-specific relation between loading applied (to a structure, which contributes to the stress) and failure is derived from simulation, where a probabilistic approach only assumes a distribution function. Subsequently, an adequate general model is set up to describe deterioration progression. The scheme will be shown through simulation-based results and can be used for estimation of the remaining useful life and predictive maintenance/control. [ABSTRACT FROM AUTHOR]

Analytical model-based methods have been developed during the last decades to achieve the goals of fault diagnosis of systems. One of the drawbacks of these methods, is the necessity of precise models of the considered system in order to design an appropriate fault detection/diagnosis system. This strong assumption can not be fulfilled for all cases. Additionally, different models have to be defined for distinguishing different states of machines operation. Qualitative model-based methods and also signal-based methods avoid this problem due to their different principle concepts of modeling. This contribution deals with the idea of combining qualitative model-based methods using fuzzy logic and statistical methods describing signal properties. The desired goal is to design a condition monitoring system based on suitable and available signals, related measurements experiments, and classifying information of the system to be monitored. The key idea of this contribution is the generation of a set of features to distinguish related different states of the system. For validation of the developed method, experimental data are used from an experimental studyed friction and wear processes of a metal surface allowing the distinction of different wear states. The developed method shows good ability to distinct the related states of wear. [ABSTRACT FROM AUTHOR]

In this study, the application of a meta-modelling technique to structure complex environments is demonstrated with two examples. A special situation-operator model developed to model the human-machine-interaction is applied to automated supervision within the HMI context and to realize flexible and situated interaction of autonomous systems. A concept to automated monitoring of human operators is introduced to illustrate the structuring of complex environments as the first example. In the second example, this modelling technique is applied to a mobile robot to autonomously build and update a mental model of the interaction with the environment as an example of a cognitive technical system. [ABSTRACT FROM AUTHOR]

This article concerns modelling and controller design for elastic ship-mounted cranes with the Maryland Rigging. Three inputs are employed to control the vibrations in the plane of the boom; the luff angle is utilized to ensure the controllability of the elastic boom, and the total length of the upper cable in conjunction with the position of its lower suspension point are used to guarantee the controllability of the payload. The dynamic behaviour of the crane is described by a multi-model approach depending on the current values of the cable length and boom luff angle. Consequently, a variable-gain observer and a variable-gain controller are designed to control the vibrations of the crane; the numerical values of the gains are updated according to the current operating region, which is determined by a region finder. Simulation and experimental results show that the expressed control strategy performs well and has a significant effect in controlling the vibrations of the crane for different operating conditions and payload masses. [ABSTRACT FROM AUTHOR]

Model‐free adaptive control (MFAC) approaches are able to be used when system models are not available and some task‐ or system‐specific conditions are fulfilled. This avoids to require exact mathematical models with respect to consideration of modeling errors even in the case of nonlinear systems to be controlled. The control strategy denoted as model‐free when for controller design only the available system data is used. In this contribution based on the knowledge of MFAC, an extension of the existing approach is presented and applied to unknown multivariable systems. The main control idea is that an estimation of real‐time system parameters which represent system dynamical variations is realized by using only inputs and outputs. A novel control algorithm is derived by minimizing both output tracking error and its derivative. The designed controller is applied to a flexible crane, representing a class of MIMO systems. The simulation results demonstrate that the vibrations of a crane system could be reduced significantly. [ABSTRACT FROM AUTHOR]

Highlights • An advanced version of smooth variable structure filter denoted as uncertainty learning filter is proposed for state estimation of nonlinear system in case of imprecise model description. • Dependencies of the estimation performance on parameters can be avoided by introducing an uncertainty learning parameter which can be adapted online. • The uncertainty learning parameter has the capability to tune the proposed filter according to the uncertainties present in the current model description. • Boundedness of estimation error is proven for the adaptive filtering approach. Abstract In this contribution the problem of state estimation of systems with imprecise description is addressed. A revised version of smooth variable structure filter (SVSF) called uncertainty learning filter (ULF) is proposed. By introducing an uncertainty learning parameter a novel strategy to minimize a pseudo mean squared estimation error is developed. The introduced uncertainty learning parameter acts as a tuning factor controlling the influence of the imprecise process model on the estimations. Parameter dependencies can be avoided by adapting the uncertainty learning parameter from the innovation process. Consequently the proposed adaptive uncertainty learning filter (A-ULF) has the ability to tune itself according to the uncertainties of the process model. The boundedness of estimation error of A-ULF approach is proven. Simulation results are given to compare the estimation performance of the proposed method with SVSF, and the extended Kalman filter (EKF). [ABSTRACT FROM AUTHOR]

Abstract: In this contribution, the dynamical behavior of a polymer electrolyte membrane (PEM) fuel cell system is modeled; related control approaches are developed. The system model used for experimental and modeling purposes describes a 1.2kW PEM fuel cell stack and an air blower. Due to the dynamical fuel cell–blower interaction the fuel cell stack and the blower model are validated to real systems respectively. Additionally, a feedback based on PI-control is used for hydrogen pressure control with an anode inlet valve. This controller is able to eliminate a stationary error between the anode and cathode pressures. For principal investigations three control approaches, a classical static feed-forward control approach, a state-space feedback control, and a novel gain-scheduling approach are developed, applied, and compared. As result, it can be shown that the feed-forward approach lacks in performance recovering the excess oxygen ratio to the desired level. The state-space feedback control shows stationary error. The introduced gain-scheduling control approach leads to a fast excess oxygen ratio recovery without stationary deviations. [Copyright &y& Elsevier]