Wang Huan, Liu Zhiliang, Peng Dandan, Cheng Zhe
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan, 523808, China.
ISA Trans. 2022 Sep;128(Pt B):470-484. doi: 10.1016/j.isatra.2021.11.028. Epub 2021 Dec 15.
Mechanical system usually operates in harsh environments, and the monitored vibration signal faces substantial noise interference, which brings great challenges to the robust fault diagnosis. This paper proposes a novel attention-guided joint learning convolutional neural network (JL-CNN) for mechanical equipment condition monitoring. Fault diagnosis task (FD-Task) and signal denoising task (SD-Task) are integrated into an end-to-end CNN architecture, achieving good noise robustness through dual-task joint learning. JL-CNN mainly includes a joint feature encoding network and two attention-based encoder networks. This architecture allows FD-Task and SD-Task can achieve deep cooperation and mutual learning. The JL-CNN is evaluated on the wheelset bearing dataset and motor bearing dataset, which shows that JL-CNN has excellent fault diagnosis ability and signal denoising ability, and it has good performance under strong noise and unknown noise.
机械系统通常在恶劣环境中运行，监测到的振动信号面临大量噪声干扰，这给鲁棒故障诊断带来了巨大挑战。本文提出了一种用于机械设备状态监测的新型注意力引导联合学习卷积神经网络（JL-CNN）。故障诊断任务（FD-Task）和信号去噪任务（SD-Task）被集成到一个端到端的卷积神经网络架构中，通过双任务联合学习实现了良好的噪声鲁棒性。JL-CNN主要包括一个联合特征编码网络和两个基于注意力的编码器网络。这种架构允许FD-Task和SD-Task实现深度合作和相互学习。在轮对轴承数据集和电机轴承数据集上对JL-CNN进行了评估，结果表明JL-CNN具有出色的故障诊断能力和信号去噪能力，并且在强噪声和未知噪声下具有良好的性能。
