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.

准确，可靠地估计剩余使用寿命（RUL）对于开发先进的智能和预测性维护策略至关重要。为此，本文提出了一种新的混合框架，称为NPBGRU，它是通过将三个完全嘈杂的深度学习架构集成而开发的。噪声CNN（NCNN）和噪声双向GRU（NBGRU）路径是并行设计的，它们的串联输出被馈送到噪声融合中心（NFC）。采用所提出的噪声层增强了所提出模型的鲁棒性和泛化行为。拟议的NPBGRU框架已使用NASA的C-MAPSS数据集进行了验证，并显示了最新结果。