In this paper, a Robust Multi-branch Deep learning-based system for remaining useful life (RUL) prediction and condition operations (CO) identification of rotating machines is proposed. In particular, the proposed system comprises main components: (1) an LSTM-Autoencoder to denoise the vibration data; (2) a feature extraction to generate time-domain, frequency-domain, and time-frequency based features from the denoised data; (3) a novel and robust multi-branch deep learning network architecture to exploit the multiple features. The performance of our proposed system was evaluated and compared to the state-of-the-art systems on two benchmark datasets of XJTU-SY and PRONOSTIA. The experimental results prove that our proposed system outperforms the state-of-the-art systems and presents potential for real-life applications on bearing machines.

翻译：本文提出了一种基于鲁棒多分支深度学习的系统，用于旋转机械的剩余使用寿命预测与运行工况识别。具体地，该系统主要由以下组件构成：（1）LSTM自编码器用于振动数据去噪；（2）特征提取模块从去噪数据中生成时域、频域及时频域特征；（3）一种新颖且鲁棒的多分支深度学习网络架构用于挖掘多特征信息。基于XJTU-SY与PRONOSTIA两个基准数据集，我们对该系统的性能进行了评估并与现有最优系统进行了对比。实验结果表明，所提出的系统优于现有最优系统，并在轴承机械的实际应用中展现出潜力。