TY - JOUR
T1 - Teaching old sensors new tricks
T2 - Archetypes of intelligence
AU - Karatzas, Dimosthenis
AU - Chorti, Arsenia
AU - White, Neil M.
AU - Harris, Christopher J.
PY - 2007/5
Y1 - 2007/5
N2 - 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 extended Kalman filter or online 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.
AB - 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 extended Kalman filter or online 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.
KW - Adaptability
KW - Calibration
KW - Data fusion
KW - Density estimation
KW - Drift estimation
KW - Fault detection
KW - Intelligent sensor
KW - Reliability
KW - Software architecture
UR - http://www.scopus.com/inward/record.url?scp=34247097981&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2007.893986
DO - 10.1109/JSEN.2007.893986
M3 - Article
AN - SCOPUS:34247097981
SN - 1530-437X
VL - 7
SP - 868
EP - 880
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 5
ER -