Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24314
Title: Data validation of intelligent sensor using predictive filters and fuzzy logic
Authors: Tsang, KM 
Chan, WL 
Keywords: Sensor validation polynomial predictive filters fuzzy classification
Issue Date: 2010
Publisher: Elsevier
Source: Sensors and actuators. A, Physical, 2010, v. 159, no. 2, p. 149-156 How to cite?
Journal: Sensors and actuators. A, Physical 
Abstract: A new approach using polynomial predictive filters and fuzzy logic for the online validation of sensor measurements is proposed. Polynomial predictive filters are applied to measured data records during the fault free learning stage. Predictions derived from the predictive filters are compared with the actual measurements to generate an error sequence. Fuzzy rules are then derived from the error sequence together with the physical constraints of a sensor to classify the quality of measurements. Faulty measurements can then be picked up by the fuzzy detection rules to ensure the correctness of measurements. Experimental results for detecting the quality of measurements from a temperature sensor are presented.
URI: http://hdl.handle.net/10397/24314
ISSN: 0924-4247
EISSN: 1873-3069
DOI: 10.1016/j.sna.2010.03.013
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

14
Last Week
1
Last month
0
Citations as of Apr 25, 2018

WEB OF SCIENCETM
Citations

11
Last Week
0
Last month
0
Citations as of Apr 25, 2018

Page view(s)

45
Last Week
0
Last month
Citations as of Apr 23, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.