Sensor Fusion via Statistical Hypothesis Testing for Prognosis and Degradation Analysis

Due to the rapid development of sensing technologies, multiple sensors became available for real-time monitoring of the degradation status of machine systems. With such a wealth of data collected from multiple sensors, researchers have proposed different sensor fusion approaches for prognosis and condition monitoring, thus allowing accurate inference of the remaining useful life (RUL) of machine systems. However, almost no method provides a statistical metric to evaluate the quality of degradation signals for prognosis and condition monitoring. To fill this literature gap, this paper develops a sensor fusion framework to check the reliability of given degradation signals for prognosis and degradation analysis through a series of statistical hypothesis tests. A health index is constructed in the sensor fusion framework to help differentiate between distinct degradation states. Based on the health index, the remaining times to various degradation states are estimated, including the RUL to failure. A published degradation data set of aircraft engines is used to evaluate and compare the prognostic and condition monitoring performance of the proposed method with benchmark methods.