Detecting external measurement disturbances based on statistical analysis for smart sensors

The transducer process of a sensor is interference-prone to environmental conditions or external disturbances depending on sensor type, measurement procedure etc. Dependable sensors are characterized by a broad independence of those factors or/and they can both detect situations that make a correct measurement impossible and validate the measurement result. In this paper we describe a statistical approach for the detection of faulty measurements caused by external disturbances. Our fault detection algorithm is based on a comparison of faultless reference measurements with current sensing values. Using this enhancement, a sensor becomes a real smart sensing device and supplies an additional validity estimation of each measurement. The approach was implemented and validated in a demonstration setup that integrates an infrared sensor array disturbed by a strong extraneous light.