A new multi-sensor fusion with hybrid Convolutional Neural Network with Wiener model for remaining useful life estimation

Abstract

With the development of smart manufacturing, the health monitoring of the machines has become important. Remaining useful life (RUL) estimation, which could predict the future machine state, has attracted much more attentions. Deep learning (DL) based RUL has achieved remarkable results. But it still faces the issues on the multi-sensor fusion process and the health index (HI) construction, and both of these two issues can affect DL models for RUL. To overcome these issues, this research designed a new hybrid model of Convolutional Neural Network (CNN) and Wiener process, named hybrid CNN-Wiener model. First, the CNN network is adopted to achieve feature-level fusion of multi-sensor signals and to calculate the virtual HI of the machine. Second, the Wiener process model is developed to estimate the value of RUL using virtual HI. Third, the Wiener process model is designed as the layer in CNN network and trained with CNN together. The hybrid CNN-Wiener model has been tested on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset, and its results show that the hybrid CNN-Wiener model has obtained the remarkable promotion by comparing with other famous DL models. The ablation studies have been tested and it shows that the hybrid CNN-Wiener model has been promotion largely with the Wiener model and the multi-sensor fusion techniques.