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Combining learning in model space fault diagnosis with data validation/reconstruction: Application to the Barcelona water network

Author
Quevedo, J.; Chen, H.; Cuguero-Escofet, M.A.; Tino, P.; Puig, V.; García, D.; Sarrate, R.; Yao, X.
Type of activity
Journal article
Journal
Engineering applications of artificial intelligence
Date of publication
2014-02-14
Volume
30
First page
18
Last page
29
DOI
https://doi.org/10.1016/j.engappai.2014.01.008 Open in new window
Project funding
Making Sense of Nonsense
Repository
http://hdl.handle.net/2117/23031 Open in new window
Abstract
In this paper, an integrated data validation/reconstruction and fault diagnosis approach is proposed for critical infrastructure systems. The proposed methodology is implemented in a two-stage approach. In the first stage, sensor communication faults are detected and corrected, in order to facilitate a reliable dataset to perform system fault diagnosis in the second stage. On the one hand, sensor validation and reconstruction are based on the combined use of spatial and time series models. Spati...
Citation
Quevedo, J. [et al.]. Combining learning in model space fault diagnosis with data validation/reconstruction: Application to the Barcelona water network. "Engineering applications of artificial intelligence", 14 Febrer 2014, vol. 30, p. 18-29.
Keywords
Fault diagnosis, Learning in model space, Reservoir computing, Sensor data validation/reconstruction, Time series
Group of research
CS2AC-UPC - Supervision, Safety and Automatic Control
SAC - Advanced Control Systems
SIC - Smart Control Systems

Participants