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 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.