Use of Self-Calibration Data for Multifunctional MEMS Sensor Prognostics

This paper proposes a solution to monitor the degradation of a multifunctional microelectromechanical systems (MEMS) sensor (MFS) and to recalibrate the sensor output accordingly. The solution is able to predict the remaining useful life based on the recalibration history. The MFS used is a dual pressure-humidity hybrid sensor where model data have been used to demonstrate the applicability and performance of the proposed method for diagnosis, self-correction, and prognosis.