An integrated learning and filtering approach for fault diagnosis of a class of nonlinear dynamical systems
| Autor: | Keliris, C., Polycarpou, Marios M., Parisini, T. |
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| Přispěvatelé: | Polycarpou, Marios M. [0000-0001-6495-9171] |
| Rok vydání: | 2015 |
| Předmět: |
SENSOR FAULTS
0209 industrial biotechnology Technology OBSERVER Observer (quantum physics) Computer Networks and Communications Computer science Fault diagnosis filtering learning ADAPTIVE APPROXIMATION APPROACH 02 engineering and technology computer.software_genre Fault (power engineering) Fault detection and isolation Computer Science Artificial Intelligence learning systems 020901 industrial engineering & automation Engineering Adaptive estimation Artificial Intelligence Computer Science Theory & Methods ISOLATION SCHEME 0202 electrical engineering electronic engineering information engineering INPUT-OUTPUT Computer Science Hardware & Architecture Science & Technology Noise measurement Engineering Electrical & Electronic fault diagnosis fault detection Computer Science Applications Stuck-at fault Noise Nonlinear system Computer Science Measurement uncertainty 020201 artificial intelligence & image processing UNCERTAIN SYSTEMS Data mining ABRUPT computer Algorithm Software |
| Zdroj: | IEEE Transactions on Neural Networks and Learning Systems IEEE Trans.Neural Networks Learn.Sys. |
| Popis: | This paper develops an integrated filtering and adaptive approximation-based approach for fault diagnosis of process and sensor faults in a class of continuous-time nonlinear systems with modeling uncertainties and measurement noise. The proposed approach integrates learning with filtering techniques to derive tight detection thresholds, which is accomplished in two ways: 1) by learning the modeling uncertainty through adaptive approximation methods and 2) by using filtering for dampening measurement noise. Upon the detection of a fault, two estimation models, one for process and the other for sensor faults, are initiated in order to identify the type of fault. Each estimation model utilizes learning to estimate the potential fault that has occurred, and adaptive isolation thresholds for each estimation model are designed. The fault type is deduced based on an exclusion-based logic, and fault detectability and identification conditions are rigorously derived, characterizing quantitatively the class of faults that can be detected and identified by the proposed scheme. Finally, simulation results are used to demonstrate the effectiveness of the proposed approach. C. Keliris, M. M. Polycarpou, and T. Parisini, “An Integrated Learning and Filtering Approach for Fault Diagnosis of a Class of Nonlinear Dynamical Systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 4, pp. 988–1004, 2017. DOI: 10.1109/TNNLS.2015.2504418 ©2017 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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