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. Note to Practitioners-This paper aims to real-time forecast the condition of operating units given their real-time multisensor data. In particular, this paper proposes a data fusion framework that fuses the multisensor data to construct a health index signal. The health index is designed to distinguish between any two degradation states during the lifecycle of a unit. Therefore, it can be used to estimate the remaining life to reach any degradation state including failure, which helps to raise early maintenance and safety alarms. The overall procedure can be summarized in the following steps: 1) the architecture of the health index is learned from the multisensor data of historical units that already failed; 2) this architecture is then applied to the real-time multisensor data of an operating unit to construct its real-time health index signal; 3) the real-time health index signal is forecast for future time instances; and 4) for each forecast time instance, the forecast health index value is compared with the historical health index signals to find the most likely degradation state of the operating unit at that time instance. The proposed approach was shown to be effective for applications with a single failure mode.