AFCNet: A Fusion Method of Multisensor Data Based on Adaptive Feature Complementation

Sensors are essential for the prognosis and health management of equipment. The fusion of multiple sensors data could improve the accuracy of fault diagnosis. Current data fusion methods focus on high-quality sensor data. However, in practical applications, constraints such as cost or space may limit the use of multiple high-quality sensors. Therefore, it is necessary to explore how to effectively use all available sensor data, even those containing some useful information but less effective when used individually. This paper presents a novel method for sensor data fusion, utilizing the highest-performing sensor data as a back bone and complementing it with preliminary data-level fusion data through multi-stage adaptive feature complementation. Comparative results validate the effectiveness and superiority of this method. The proposed method also demonstrates strong predictive capability by using data from a single period as training samples to forecast future data states.