Abstract
Designating a sensor as intelligent is a long-standing term implying that it provides more functionality than merely providing an output measurement. Since there is some discrepancy governing what makes a given sensor intelligent, this paper defines the features required for improving confidence in sensor measurements, from the sensor management perspective. We describe a software framework used to implement tasks such as condition monitoring onboard the sensor itself, rather than at the traditional supervisory level. The algorithms include data-based models, which allows for modelling of non-linear effects and estimation uncertainty, which is a prerequisite for data fusion. Density estimation for novelty detection is demonstrated for an accelerometer that is purposely damaged in an environmental chamber.
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