As fault detection and fault diagnosis are more and more finding their way into modern industrial mechatronic products, it is now time to take the next step. Based on the research efforts for fault detection and diagn...
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In this paper, fault detection methods for hydraulic systems based on a parity equation approach with neural net models are presented. Hydraulic systems are used in manifold applications in industry. They are however ...
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This paper investigates the absolute stability problem for Lur'e singularly perturbed systems with multiple nonlinearities. The objective is to determine if the system is absolutely stable for any epsilon E (0, (e...
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ISBN:
(纸本)9781424474264
This paper investigates the absolute stability problem for Lur'e singularly perturbed systems with multiple nonlinearities. The objective is to determine if the system is absolutely stable for any epsilon E (0, (epsilon)_(0)], where epsilon denotes the perturbation parameter and (epsilon)_(0) is a pre-defined positive scalar. Firstly, an epsilon-dependent Lyapunov function of Lur'e-postnikov form is constructed. Then, a stability criterion expressed in terms of epsilon-independent linear matrix inequalities (LMIs) is derived. Finally, an example is given to show the feasibility and effectiveness of the obtained method.
The multiobjective collaborative optimization (MCO) has been widely adopted in concurrent engineering design as a good multiobjective optimization approach provided the problem of optimization results converging often...
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The modeling, analysis, and design of treatment therapies for bone disorders based on the paradigm of force-induced bone growth and adaptation is a challenging task. Mathematical models provide, in comparison to clini...
The modeling, analysis, and design of treatment therapies for bone disorders based on the paradigm of force-induced bone growth and adaptation is a challenging task. Mathematical models provide, in comparison to clinical, medical and biological approaches an structured alternative framework to understand the concurrent effects of the multiple factors involved in bone remodeling. By now, there are few mathematical models describing the appearing complex interactions. However, the resulting models are complex and difficult to analyze, due to the strong nonlinearities appearing in the equations, the wide range of variability of the states, and the uncertainties in parameters. In this work, we focus on analyzing the effects of changes in model structure and parameters/inputs variations on the overall steady state behavior using systems theoretical methods. Based on an briefly reviewed existing model that describes force-induced bone adaptation, the main objective of this work is to analyze the stationary behavior and to identify plausible treatment targets for remodeling related bone disorders. Identifying plausible targets can help in the development of optimal treatments combining both physical activity and drug-medication. Such treatments help to improve/maintain/restore bone strength, which deteriorates under bone disorder conditions, such as estrogen deficiency.
A model-based fault management system for a Three Mass Torsion Oscillator is described. Fault management includes fault detection, fault diagnosis and fault compensation. A process model of system dynamics is derived....
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The presented method for nonlinear system identification is based on the LOLIMOT algorithm introduced by Nelles and Isermann [1996]. The LOLIMOT algorithm divides the input space by a tree-construction algorithm and i...
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The presented method for nonlinear system identification is based on the LOLIMOT algorithm introduced by Nelles and Isermann [1996]. The LOLIMOT algorithm divides the input space by a tree-construction algorithm and interpolates the local linear models by local membership functions. Instead of assuming local linear models, the presented algorithm utilizes general local nonlinear functions, which make the algorithm more flexible. These are approximated by a multidimensional Taylor series. Since the amount of regressors grows fast with the number of inputs and the expansion order, a subset selection procedure is introduced. It reveals significant regressors and gives information about the local functional behavior. The local subset selection is implemented as a stepwise regression with replacement of regressors. Mallows’ C p -statistic is used for the subset selection algorithm and is also implemented for final model selection. The benefit of the extended algorithm lies in the higher flexibility in the local models, which results in less partitions of the input space by a similar approximation quality.
In this paper, fault detection methods for hydraulic systems based on a parity equation approach with neural net models are presented. Hydraulic systems are used in manifold applications in industry. They are however ...
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In this paper, fault detection methods for hydraulic systems based on a parity equation approach with neural net models are presented. Hydraulic systems are used in manifold applications in industry. They are however not yet the subject of intense research in the area of fault detection and diagnosis, which can be mainly attributed to their strong nonlinear behavior, which exacerbates the physical modeling extensively. To avoid the difficulties associated with the physical modeling, a data-driven modeling approach based on the LOLIMOT neural network will be presented in this paper. Different subsystems of the hydraulic servo axis will be modeled using different sensor configurations. Experimental data from a real testbed allow to compare the model fidelity of the different resulting neural nets and can also be used to illustrate the capabilities of the parity-equation based fault detection approach, which in general allows the detection of tiny faults, such as sensor offset faults in the area of a few percent of the maximum sensor readout.
As fault detection and fault diagnosis are more and more finding their way into modern industrial mechatronic products, it is now time to take the next step. Based on the research efforts for fault detection and diagn...
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As fault detection and fault diagnosis are more and more finding their way into modern industrial mechatronic products, it is now time to take the next step. Based on the research efforts for fault detection and diagnosis, a platform has been prepared for research on fault management, i.e. automatic reactions of the system to continue operation after the detection of faults. These reactions may employ hardware redundancy (i.e. switching from a faulty actuator to another, intact one) or analytical redundancy (i.e. switching from a faulty sensor to a “model sensor” or “soft sensor”). A total fault tolerance concept must encompass all components of a system, i.e. the actuators and drives, the sensors as well as the controller and communication. In many cases, a degradation of functions has to be accepted after a fault has appeared. This contribution will center on actuators and drives showing real-world examples of prototypical realizations and examples from industrial products. Concentrating on the most widespread actuation principles, the paper will focus on electric drives and hydraulic actuators. First, a review is given on fault tolerance principles and general structural considerations e.g. hot-standby and cold-standby, focusing on the scheme of an overall fault-tolerant control system. Then, fault statistics for real actuators and drives will be presented. These fault statistics give hints on the parts of the actuators that are most susceptible to faults. Different designs of fault tolerant actuators and drives, which have been realized, shall be presented and evaluated with respect to their capabilities of sustaining faults. Finally, an outlook for fault-tolerant mechatronic systems will be given.
This work presents the theoretical design and the experimental validation of a real time optimisation logic for distributed parameter systems. This logic consists of a hierarchy of two layers. The upper layer is respo...
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