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Advances in model-based fault diagnosis with evolutionary algorithms and neural networks

100%
Witczak M.
International Journal of Applied Mathematics and Computer Science
|
2006
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tom 16
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nr 1
85-99
EN
Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, the classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as evolutionary algorithms and neural networks become more and more popular in industrial applications of fault diagnosis. The main objective of this paper is to present recent developments regarding the application of evolutionary algorithms and neural networks to fault diagnosis. In particular, a brief introduction to these computational intelligence paradigms is presented, and then a review of their fault detection and isolation applications is performed. Close attention is paid to techniques that integrate the classical and soft computing methods. A selected group of them is carefully described in the paper. The performance of the presented approaches is illustrated with the use of the DAMADICS fault detection benchmark that deals with a valve actuator.
2
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Towards robustness in neural network based fault diagnosis

51%
Patan K., Witczak M., Korbicz J.
International Journal of Applied Mathematics and Computer Science
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2008
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tom 18
|
nr 4
443-454
EN
Challenging design problems arise regularly in modern fault diagnosis systems. Unfortunately, classical analytical techniques often cannot provide acceptable solutions to such difficult tasks. This explains why soft computing techniques such as neural networks become more and more popular in industrial applications of fault diagnosis. Taking into account the two crucial aspects, i.e., the nonlinear behaviour of the system being diagnosed as well as the robustness of a fault diagnosis scheme with respect to modelling uncertainty, two different neural network based schemes are described and carefully discussed. The final part of the paper presents an illustrative example regarding the modelling and fault diagnosis of a DC motor, which shows the performance of the proposed strategy.
3
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Towards robust predictive fault-tolerant control for a battery assembly system

51%
Seybold L., Witczak M., Majdzik P., Stetter R.
International Journal of Applied Mathematics and Computer Science
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2015
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tom 25
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nr 4
849-862
EN
The paper deals with the modeling and fault-tolerant control of a real battery assembly system which is under implementation at the RAFI GmbH company (one of the leading electronic manufacturing service providers in Germany). To model and control the battery assembly system, a unified max-plus algebra and model predictive control framework is introduced. Subsequently, the control strategy is enhanced with fault-tolerance features that increase the overall performance of the production system being considered. In particular, it enables tolerating (up to some degree) mobile robot, processing and transportation faults. The paper discusses also robustness issues, which are inevitable in real production systems. As a result, a novel robust predictive fault-tolerant strategy is developed that is applied to the battery assembly system. The last part of the paper shows illustrative examples, which clearly exhibit the performance of the proposed approach.
4
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Fault-tolerant control strategy for actuator faults using LPV techniques: Application to a two degree of freedom helicopter

51%
Montes de Oca S., Puig V., Witczak M., Dziekan Ł.
International Journal of Applied Mathematics and Computer Science
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2012
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tom 22
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nr 1
161-171
EN
In this paper, a Fault Tolerant Control (FTC) strategy for Linear Parameter Varying (LPV) systems that can be used in the case of actuator faults is proposed. The idea of this FTC method is to adapt the faulty plant instead of adapting the controller to the faulty plant. This approach can be seen as a kind of virtual actuator. An integrated FTC design procedure for the fault identification and fault-tolerant control schemes using LPV techniques is provided as well. Fault identification is based on the use of an Unknown Input Observer (UIO). The FTC controller is implemented as a state feedback controller and designed using polytopic LPV techniques and Linear Matrix Inequality (LMI) regions in such a way as to guarantee the closed-loop behavior in terms of several LMI constraints. To assess the performance of the proposed approach, a two degree of freedom helicopter is used.
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