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Teaching old sensors New tricks: archetypes of intelligence

Teaching old sensors New tricks: archetypes of intelligence
Teaching old sensors New tricks: archetypes of intelligence
In this paper a generic intelligent sensor software architecture is described which builds upon the basic requirements of related industry standards (IEEE 1451 and SEVA BS- 7986). It incorporates specific functionalities such as real-time fault detection, drift compensation, adaptation to environmental changes and autonomous reconfiguration. The modular based structure of the intelligent sensor architecture provides enhanced flexibility in regard to the choice of specific algorithmic realizations. In this context, the particular aspects of fault detection and drift estimation are discussed. A mixed indicative/corrective fault detection approach is proposed while it is demonstrated that reversible/irreversible state dependent drift can be estimated using generic algorithms such as the EKF or on-line density estimators. Finally, a parsimonious density estimator is presented and validated through simulated and real data for use in an operating regime dependent fault detection framework.
Inteligent sensors, software architecture, fault detection, drift estimation, sensor modelling
1530-437X
Karatzas, Dimosthenis
4d7e3927-2252-4039-88a4-0daca766e943
Chorti, Arsenia
d1ba1c4d-0b09-480d-8984-7a933ace7e87
White, Neil M.
c7be4c26-e419-4e5c-9420-09fc02e2ac9c
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Karatzas, Dimosthenis
4d7e3927-2252-4039-88a4-0daca766e943
Chorti, Arsenia
d1ba1c4d-0b09-480d-8984-7a933ace7e87
White, Neil M.
c7be4c26-e419-4e5c-9420-09fc02e2ac9c
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Karatzas, Dimosthenis, Chorti, Arsenia, White, Neil M. and Harris, Chris J. (2007) Teaching old sensors New tricks: archetypes of intelligence. IEEE Sensors Journal.

Record type: Article

Abstract

In this paper a generic intelligent sensor software architecture is described which builds upon the basic requirements of related industry standards (IEEE 1451 and SEVA BS- 7986). It incorporates specific functionalities such as real-time fault detection, drift compensation, adaptation to environmental changes and autonomous reconfiguration. The modular based structure of the intelligent sensor architecture provides enhanced flexibility in regard to the choice of specific algorithmic realizations. In this context, the particular aspects of fault detection and drift estimation are discussed. A mixed indicative/corrective fault detection approach is proposed while it is demonstrated that reversible/irreversible state dependent drift can be estimated using generic algorithms such as the EKF or on-line density estimators. Finally, a parsimonious density estimator is presented and validated through simulated and real data for use in an operating regime dependent fault detection framework.

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Published date: 2007
Keywords: Inteligent sensors, software architecture, fault detection, drift estimation, sensor modelling
Organisations: EEE, Southampton Wireless Group

Identifiers

Local EPrints ID: 263553
URI: http://eprints.soton.ac.uk/id/eprint/263553
ISSN: 1530-437X
PURE UUID: 593ee313-54b6-4f9b-a758-30501c11364f
ORCID for Neil M. White: ORCID iD orcid.org/0000-0003-1532-6452

Catalogue record

Date deposited: 19 Feb 2007
Last modified: 20 Jul 2019 01:24

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Contributors

Author: Dimosthenis Karatzas
Author: Arsenia Chorti
Author: Neil M. White ORCID iD
Author: Chris J. Harris

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