An ensembled remaining useful life prediction method with data fusion and stage division

Abstract

The remaining useful life (RUL) prediction method based on multi-sensor vibration data is a significant component of predictive maintenance for rolling bearings. However, during the fusion process, it is easy to overlook the consistency of multi-sensor vibration data and cannot adaptively divide degradation stages, resulting in a decrease in the accuracy of the prediction method and limits its applicability in industrial settings. Therefore, this article proposes an integrated prediction method for the RUL of rolling bearings based on data fusion and stage division. Firstly, a data-level fusion method based on multi-sensor vibration signals (MSDF) is proposed. This method dynamically weights sensor data, aiming to consider consistency and reliability in order to achieve data level fusion for multi-sensor vibration signals. Secondly, a stage division method is proposed, which adaptively divides the degradation process into three stages to guide data fusion and ensemble prediction results. Finally, the feature complementarity based ensemble prediction (TCEP) model is proposed to enhance prediction accuracy by learning the degradation difference information of features throughout the prediction process. Furthermore, the outstanding performance of the proposed method was validated using two sets of bearing lifetime vibration signal datasets.