Are neural network techniques the solution to measurement validation, monitoring and automatic diagnosis of sensor faults?

Elena Gaura; M Kraft

doi:10.1109/SICE.2002.1196649

Are neural network techniques the solution to measurement validation, monitoring and automatic diagnosis of sensor faults?

Elena Gaura, M Kraft

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

Abstract

The appropriateness and feasibility of using artificial Neural Network (ANN) techniques to facilitate improved in-service performance of micromachined acceleration measuring devices is questioned in this research and its possible extrapolation to sensor fault diagnosis is attempted. Two examples of closed loop neuro-transducers are given: a micromachined accelerometer with capacitive pick-off and a neural network controlled tunnelling accelerometer. Based on the success of the ANN control method as applied to sensors, the authors investigate the possibility of developing self-diagnosis sensors based on ANNs and a strategy of such development is proposed.

Original language	English
Title of host publication	Proceedings of the 41st SICE Annual Conference, Vols 1-5
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	2052-2057
Number of pages	6
ISBN (Print)	0780376315
DOIs	https://doi.org/10.1109/SICE.2002.1196649
Publication status	Published - 2002
Externally published	Yes
Event	41st Annual Conference of the Society-of-Instrument-and-Control-Engineers (SICE 2002) - OSAKA, Japan Duration: 5 Aug 2002 → 7 Aug 2002

Conference

Conference	41st Annual Conference of the Society-of-Instrument-and-Control-Engineers (SICE 2002)
Country/Territory	Japan
City	OSAKA
Period	5/08/02 → 7/08/02

Keywords

micromachined sensors
closed loop control
fault diagnosis
artificial neural networks
ACCELEROMETER

Access to Document

10.1109/SICE.2002.1196649

Cite this

@inproceedings{d06bab9f5e0341d6b2ceaca63ed5b787,

title = "Are neural network techniques the solution to measurement validation, monitoring and automatic diagnosis of sensor faults?",

abstract = "The appropriateness and feasibility of using artificial Neural Network (ANN) techniques to facilitate improved in-service performance of micromachined acceleration measuring devices is questioned in this research and its possible extrapolation to sensor fault diagnosis is attempted. Two examples of closed loop neuro-transducers are given: a micromachined accelerometer with capacitive pick-off and a neural network controlled tunnelling accelerometer. Based on the success of the ANN control method as applied to sensors, the authors investigate the possibility of developing self-diagnosis sensors based on ANNs and a strategy of such development is proposed.",

keywords = "micromachined sensors, closed loop control, fault diagnosis, artificial neural networks, ACCELEROMETER",

author = "Elena Gaura and M Kraft",

year = "2002",

doi = "10.1109/SICE.2002.1196649",

language = "English",

isbn = "0780376315",

pages = "2052--2057",

booktitle = "Proceedings of the 41st SICE Annual Conference, Vols 1-5",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "41st Annual Conference of the Society-of-Instrument-and-Control-Engineers (SICE 2002) ; Conference date: 05-08-2002 Through 07-08-2002",

}

Gaura, E & Kraft, M 2002, Are neural network techniques the solution to measurement validation, monitoring and automatic diagnosis of sensor faults? in Proceedings of the 41st SICE Annual Conference, Vols 1-5. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 2052-2057, 41st Annual Conference of the Society-of-Instrument-and-Control-Engineers (SICE 2002), OSAKA, Japan, 5/08/02. https://doi.org/10.1109/SICE.2002.1196649

Are neural network techniques the solution to measurement validation, monitoring and automatic diagnosis of sensor faults? / Gaura, Elena; Kraft, M.
Proceedings of the 41st SICE Annual Conference, Vols 1-5. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2002. p. 2052-2057.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review