Abstract
Identifying and removing the outliers is important in order to make the data more trustworthy and improve the reliability of fault detection, since outliers in the measured data can cause false alarms. An online outlier identification and removal (OIR) scheme, suitable for nonlinear dynamic systems, is proposed in this paper. A neural network (NN) is utilized to estimate the actual outlier-free system states using only the measured system states which involve outliers. Outlier identification is performed online by finding the difference between measured and estimated states and comparing it with its median and standard deviation over a dynamic time window. Furthermore, the neural network weight update law is designed such that the detected outliers will not affect the state estimation. The proposed OIR scheme is then combined with fault diagnosis scheme as a preprocessing unit, in order to improve fault detection performance. A separate model-based fault detection observer is designed which uses the estimated outlier-free states to perform fault diagnosis. Finally, a simple linear system is used to verify the scheme in simulations followed by a piston pump test bed study. © 2013 IEEE.
Recommended Citation
H. Ferdowsi et al., "A Neural Network based Outlier Identification and Removal Scheme," PHM 2013 - 2013 IEEE International Conference on Prognostics and Health Management, Conference Proceedings, article no. 6621453, Institute of Electrical and Electronics Engineers, Dec 2013.
The definitive version is available at https://doi.org/10.1109/ICPHM.2013.6621453
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
International Standard Book Number (ISBN)
978-146735722-7
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
06 Dec 2013
