Abstract
In this paper, a novel fault diagnostics and prediction (FDP) scheme is introduced by using artificial immune system (AIS) as an online approximator for a class of nonlinear discrete-time systems. Traditionally, AIS is considered as an offline tool for fault detection (FD). However, in this paper, AIS is utilized as an online approximator in discrete time (OLAD) along with a robust adaptive term in the proposed fault diagnostics observer. using the fact that the system outputs are alone measurable, an output residual is determined by comparing the observer and system outputs and a fault is detected if this output residual exceeds a predefined threshold. Upon detection, the OLADs are initiated to learn the unknown fault dynamics while the robust adaptive term ensures asymptotic convergence of the output residual for a state fault whereas a bounded result for an output fault. Additionally, for prognostics purposes, the parameter update law for AIS is used to estimate the time-to-failure (TTF). Finally, the performance of the proposed FDP scheme is demonstrated experimentally on an axial piston pump testbed for two failure modes. © 2011 IEEE.
Recommended Citation
G. R. Halligan et al., "Artificial Immune System-Based Diagnostics and Prognostics Scheme and its Experimental Verification," Proceedings of the IEEE International Conference on Control Applications, pp. 958 - 963, article no. 6044389, Institute of Electrical and Electronics Engineers, Nov 2011.
The definitive version is available at https://doi.org/10.1109/CCA.2011.6044389
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
International Standard Book Number (ISBN)
978-145771062-9
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
07 Nov 2011
