Remaining useful life prediction with uncertainty quantification based on multi-distribution fusion structure

The issue of uncertainty in Remaining Useful Life (RUL) prediction based on the Deep Learning (DL) framework has gained increased attention in recent years. The probabilistic distribution is usually the effective way to capture the associated uncertainty in RUL prediction. However, most existing research relies on a single distribution that assumes a singular underlying pattern in the data. This type of approach restricts the ability to describe dynamic industrial processes and lacks robustness in adapting to the complexity of time-varying degradation. This paper proposes an approach to improve uncertainty quantification in RUL prediction based on the Multi-Distribution Fusion (MDF) structure. Initially, multiple possible RUL prediction results are produced. Subsequently, the MDF is used to integrate the former results with different weights and outputs the final RUL prediction distribution. The proposed method excels in uncertainty capturing in complex scenarios and provides a deeper understanding of the underlying dynamics of the monitoring data. The application of MDF resulted in more enhanced and robust uncertainty concerning RUL predictions. To validate the effectiveness of the proposed method, two neural networks, the Long Short-Term Memory (LSTM) network and Convolutional Neural Network (CNN), are individually combined with MDF and applied to the C-MAPSS datasets.