In this section
@article{Keliris:2016:10.1109/TNNLS.2015.2504418,
author = {Keliris, C and Polycarpou, MM and Parisini, T},
doi = {10.1109/TNNLS.2015.2504418},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
pages = {988--1004},
title = {An integrated learning and filtering approach for fault diagnosis of a class of nonlinear dynamical systems},
url = {http://dx.doi.org/10.1109/TNNLS.2015.2504418},
volume = {28},
year = {2016}
}
TY - JOUR
AB - 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.
AU - Keliris,C
AU - Polycarpou,MM
AU - Parisini,T
DO - 10.1109/TNNLS.2015.2504418
EP - 1004
PY - 2016///
SN - 2162-237X
SP - 988
TI - An integrated learning and filtering approach for fault diagnosis of a class of nonlinear dynamical systems
T2 - IEEE Transactions on Neural Networks and Learning Systems
UR - http://dx.doi.org/10.1109/TNNLS.2015.2504418
UR - http://hdl.handle.net/10044/1/39148
VL - 28
ER -