DocumentCode :
3054481
Title :
Neural-Network Fault Diagnosis for Electrode Structures in Bio-fluidic Microsystems
Author :
Al-Gayem, Q. ; Richardson, A. ; Liu, H.
Author_Institution :
Centre for Microsyst. Eng., Lancaster Univ., Lancaster, UK
fYear :
2011
fDate :
16-18 May 2011
Firstpage :
143
Lastpage :
148
Abstract :
Lab-on-chip devices are of great interest for analysis in fields including biochemistry, biomedical engineering and bioelectronics. Within these systems, highlevels of reliability and robustness are crucial and normally complemented by requirements for extremely low probabilities of false positives or negatives being generated. Optimizing the design of these devices and investigating new methods for validating functionality and integrity of the readings are therefore required. This paper proposes a new fault diagnosis approach using Artificial Neural Network(ANN) for detecting degradation in electrodes that interface to fluidic or biological systems and form the basis of numerous actuation and sensing mechanisms in the biofluidics area. In this approach, the ANN is constructed and trained with a subset of experimental impedance data which was extracted at different degradation levels. New sets of data are used to test the network and the results show that the ANN has the ability to provide an early warning for degradation within the electrode structure.
Keywords :
bioMEMS; electrodes; fault diagnosis; microfluidics; neural nets; actuation mechanism; artificial neural network; bio-fluidic microsystem; biofluidic area; biological system; electrode structure; fluidic system; neural-network fault diagnosis; sensing mechanism; Degradation; Electrodes; Impedance; Microfluidics; Neurons; Stress; Surface impedance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Mixed-Signals, Sensors and Systems Test Workshop (IMS3TW), 2011 IEEE 17th International
Conference_Location :
Santa Barbara, CA
Print_ISBN :
978-1-4577-1144-2
Type :
conf
DOI :
10.1109/IMS3TW.2011.14
Filename :
6132755
Link To Document :
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