This article proposes a novel deep learning based fusion prognostic method for remaining useful life (RUL) prediction of engineering systems. The proposed framework strategically combines the advantages of bidirectional long short-term memory (BLSTM) networks and particle filter (PF) method and meanwhile mitigates their limitations. In the proposed framework, BLSTM networks are applied for further extracting, selecting, and fusing discriminative features to form predicted measurements of the identified degradation indicator. Simultaneously, PF is utilized to estimate system state and identify unknown parameters of the degradation model for RUL prediction. Hence, the proposed fusion prognostic framework has two innovative features: first, the preprocessed features from raw multisensor data can be intelligently extracted, selected, and fused by the BLSTM networks without specific domain knowledge of feature engineering; second, the predicted measurements with uncertainties obtained from the BLSTM networks will be properly represented by the PF in a transparent manner. Moreover, the developed approach is experimentally validated with machining tool wear tests on a computer numerical control (CNC) milling machine. In addition, the popular techniques employed in this field are also investigated to compare with the proposed method.

中文翻译：

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一种基于增强型深度学习的 RUL 预测融合预测方法

本文提出了一种新的基于深度学习的融合预测方法，用于工程系统的剩余使用寿命 (RUL) 预测。所提出的框架战略性地结合了双向长短期记忆（BLSTM）网络和粒子滤波器（PF）方法的优点，同时减轻了它们的局限性。在所提出的框架中，BLSTM 网络用于进一步提取、选择和融合判别特征，以形成对已识别退化指标的预测测量。同时，利用PF来估计系统状态并识别退化模型的未知参数以进行RUL预测。因此，所提出的融合预测框架具有两个创新特征：第一，可以智能地提取、选择来自原始多传感器数据的预处理特征，并由 BLSTM 网络融合，无需特定的特征工程领域知识；其次，从 BLSTM 网络获得的具有不确定性的预测测量值将以透明的方式由 PF 正确表示。此外，所开发的方法在计算机数控 (CNC) 铣床上通过加工工具磨损测试进行了实验验证。此外，还研究了该领域中采用的流行技术，以与所提出的方法进行比较。开发的方法在计算机数控 (CNC) 铣床上通过加工工具磨损测试进行了实验验证。此外，还研究了该领域中采用的流行技术，以与所提出的方法进行比较。开发的方法在计算机数控 (CNC) 铣床上通过加工工具磨损测试进行了实验验证。此外，还研究了该领域中采用的流行技术，以与所提出的方法进行比较。