Abstract
This paper presents a new model-based fault detection and estimation framework for a class of multi-input and multi-output (MIMO) nonlinear distributed parameter systems (DPS) described by partial differential equations (PDE) with actuator and sensor faults. The fault functions cover both abrupt and incipient faults. A Luenberger type observer is used to monitor the health of the DPS as a detection observer on the basis of the nonlinear PDE representation of the system with measured output vector. By taking the difference between measured and estimated outputs from this observer, a residual signal is generated for fault detection. If the detection residual exceeds a predefined threshold, a fault will be claimed to be active. Once an actuator or a sensor fault is detected and the fault type is identified, an appropriate fault parameter update law is developed to learn the fault dynamics online with the help of an additional output measurement. Eventually, the proposed detection and estimation framework is demonstrated on a nonlinear process.
Recommended Citation
H. Ferdowsi et al., "Fault Detection and Estimation for a Class of Nonlinear Distributed Parameter Systems," 2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019, article no. 8819432, Institute of Electrical and Electronics Engineers, Jun 2019.
The definitive version is available at https://doi.org/10.1109/ICPHM.2019.8819432
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
Distributed parameter systems; Estimation; Fault detection; MIMO systems; Nonlinear systems
International Standard Book Number (ISBN)
978-153868357-6
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jun 2019
Comments
National Science Foundation, Grant None