Noisy parallel hybrid model of NBGRU and NCNN architectures for remaining useful life estimation

Accurate and robust estimation of Remaining Useful life (RUL) is of paramount importance for development of advanced smart and predictive maintenance strategies. To this aim, the paper proposes a new hybrid framework, referred to as the NPBGRU, developed by integration of three fully noisy deep learning architectures. Noisy CNN (NCNN) and Noisy Bi-directional GRU (NBGRU) paths are designed in parallel and their concatenated output is fed into the Noisy fusion center (NFC). Adopting the proposed noisy layers enhances the robustness and generalization behavior of the proposed model. The proposed NPBGRU framework is validated using NASA's C-MAPSS dataset, illustrating state-of-the-art results.