A Distributed Fault Diagnosis Approach Utilizing Adaptive Approximation for a Class of Interconnected Continuous-Time Nonlinear Systems
| Autor: | Keliris, C., Polycarpou, Marios M., Parisini, T. |
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| Přispěvatelé: | IEEE, Keliris, C., Polycarpou, M. M., Parisini, Thomas, Polycarpou, Marios M. [0000-0001-6495-9171] |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: | |
| Zdroj: | Proceedings of the IEEE Conference on Decision and Control CDC |
| Popis: | This paper develops an adaptive approximation based approach for distributed fault diagnosis for a class of interconnected continuous-time nonlinear systems with modeling uncertainties and measurement noise. The proposed approach integrates learning with filtering techniques and allows the derivation of tight detection thresholds. This is accomplished in two ways: at first, by learning the modeling uncertainty through adaptive approximation methods, so that the learned function is used for the derivation of the residual signal, and then by using filtering for dampening measurement noise. The required signals for both tasks are derived through a two-stage filtering process, by exploiting the properties of the filtering framework. Finally, simulation results are used to demonstrate the effectiveness of the proposed approach. C. Keliris, M. M. Polycarpou, and T. Parisini, “A distributed fault diagnosis approach utilizing adaptive approximation for a class of interconnected continuous-time nonlinear systems,” in Proc. IEEE 53rd Annu. Conf. Control Decision Conf., Dec. 2014, pp. 6536–6541. DOI: 10.1109/CDC.2014.7040414 ©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Databáze: | OpenAIRE |
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