Abstract
In this paper, a model-based fault detection and isolation (FDI) scheme with online fault learning capabilities is proposed for HVAC systems. an observer comprising of an online approximator in discrete-time (OLAD) and a robust term is used for detection. a fault is detected if the generated detection residual, which is defined as the error between the observer outputs and HVAC system states, exceeds an apriori chosen threshold. the OLAD term in the FD observer learns the fault dynamics online while the robust term guarantees asymptotic estimation of the system states. Subsequent to detection, a fault isolation observer, which comprises of the model of fault functions and another robust term, is initiated to identify the root cause. a fault is identified if the isolation residual converges to zero, where the residual is obtained by comparing outputs of the isolation observer and the system. Additionally, we consider different fault scenarios in the system such as single or simultaneous multiple faults. Analytical results for the FDI scheme guarantee the robustness and stability of the proposed scheme. Finally, a simulation example is used to demonstrate the proposed FDI scheme. © 2011 IEEE.
Recommended Citation
B. T. Thumati et al., "An Online Model-Based Fault Diagnosis Scheme for HVAC Systems," Proceedings of the IEEE International Conference on Control Applications, pp. 70 - 75, article no. 6044486, Institute of Electrical and Electronics Engineers, Nov 2011.
The definitive version is available at https://doi.org/10.1109/CCA.2011.6044486
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
