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An Algorithmic Approach to the Optimal Extraction of Signals from Intelligent Sensors

An Algorithmic Approach to the Optimal Extraction of Signals from Intelligent Sensors
An Algorithmic Approach to the Optimal Extraction of Signals from Intelligent Sensors
This paper describes the development of an intelligent sensor architecture, where signal conditioning is performed onboard the sensor itself, in software. Our proposed architecture uses data-based models of the sensor for signal conditioning and fault detection, so that the sensor is robust to degradation and its processed output includes an estimate of uncertainty with each measurement value for higher level sensor management processes such as data fusion. We use a data-based kernel representation for the signal conditioning system, which avoids deriving physical models of the sensor from first principles. A sparse realisation of the kernel model provides fast predictions and opportunities for efficient updating of the sensor model to enable reconfiguration of the sensor model based on incoming data. We show that these techniques have the ability to detect degradation in a MEMS sensor, using elevated temperatures in laboratory conditions.
intelligent sensor, condition monitoring, novelty detection, kernel density estimation
Boltryk, P.J.
82ca101e-7a14-49b5-8ac9-177a5a739c29
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
White, N.M.
c7be4c26-e419-4e5c-9420-09fc02e2ac9c
Boltryk, P.J.
82ca101e-7a14-49b5-8ac9-177a5a739c29
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
White, N.M.
c7be4c26-e419-4e5c-9420-09fc02e2ac9c

Boltryk, P.J., Harris, C.J. and White, N.M. (2005) An Algorithmic Approach to the Optimal Extraction of Signals from Intelligent Sensors At 2005 NSTI Nanotechnology Conference & Trade Show. 08 - 12 May 2005.

Record type: Conference or Workshop Item (Poster)

Abstract

This paper describes the development of an intelligent sensor architecture, where signal conditioning is performed onboard the sensor itself, in software. Our proposed architecture uses data-based models of the sensor for signal conditioning and fault detection, so that the sensor is robust to degradation and its processed output includes an estimate of uncertainty with each measurement value for higher level sensor management processes such as data fusion. We use a data-based kernel representation for the signal conditioning system, which avoids deriving physical models of the sensor from first principles. A sparse realisation of the kernel model provides fast predictions and opportunities for efficient updating of the sensor model to enable reconfiguration of the sensor model based on incoming data. We show that these techniques have the ability to detect degradation in a MEMS sensor, using elevated temperatures in laboratory conditions.

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More information

Published date: 2005
Venue - Dates: 2005 NSTI Nanotechnology Conference & Trade Show, 2005-05-08 - 2005-05-12
Keywords: intelligent sensor, condition monitoring, novelty detection, kernel density estimation

Identifiers

Local EPrints ID: 30246
URI: http://eprints.soton.ac.uk/id/eprint/30246
PURE UUID: 8ee43844-0929-4717-b6b9-78b333cc1aa5
ORCID for N.M. White: ORCID iD orcid.org/0000-0003-1532-6452

Catalogue record

Date deposited: 11 May 2006
Last modified: 17 Jul 2017 15:55

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