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旋转机械剩余寿命（RUL）预测与运行工况（CO）识别系统。具体而言，该系统包含三个主要组成部分：（1）LSTM自编码器用于对振动数据进行去噪处理；（2）特征提取模块从去噪数据中生成时域、频域和时频域特征；（3）一种新型鲁棒多分支深度学习网络架构用于挖掘多维特征。我们在XJTU-SY和PRONOSTIA两个基准数据集上评估了所提系统的性能，并与现有最优系统进行了对比。实验结果表明，所提系统优于现有最优方法，在轴承机械的实际应用中展现出潜力。