NDLI: Towards Robust Neural-Network-Based Sensor and Actuator Fault Diagnosis: Application to a Tunnel Furnace

Content Provider	Springer Nature Link
Author	Witczak, Marcin Mrugalski, Marcin Korbicz, Józef
Copyright Year	2014
Abstract	The paper shows a unified approach for designing both sensor and actuator fault diagnosis with neural networks. In particular, a general scheme of the group method of data handling neural networks is recalled. Subsequently, a unscented Kalman filter approach for designing the network and determining its uncertainty is briefly portrayed. The achieved results are then used to obtain the so-called robust sensor fault diagnosis scheme. The main contribution of this paper is to show how to use the above-mentioned results for actuator fault diagnosis. In particular, the obtained neural model is used to obtain the input estimates. The achieved estimates are then compared with the original input signals to formulate the diagnostics decisions. The input estimation scheme is based on a chain of robust observers, which guaranties that the input estimates are obtained with a prescribed disturbance attenuation level while ensuring the convergence of the observers. The final part of the paper shows a comprehensive case study regarding the laboratory tunnel furnace, which exhibits the performance of the proposed approach.
Starting Page	71
Ending Page	87
Page Count	17
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	42
Issue Number	1
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2014-11-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	State-space GMDH neural networks Non-linear system identification Robust fault diagnosis Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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