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Knowledge and data dual-driven transfer network for industrial robot fault diagnosis
参考中译:用于工业机器人故障诊断的知识和数据双驱动传输网络


          

刊名：Mechanical Systems and Signal Processing
作者：Tao Yin（School of Automation Science and Engineering, Xi'an Jiaotong University）
Na Lu（School of Automation Science and Engineering, Xi'an Jiaotong University）
Guangshuai Guo（School of Automation Science and Engineering, Xi'an Jiaotong University）
Yaguo Lei（Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University）
Shuhui Wang（Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University）
Xiaohong Guan（School of Automation Science and Engineering, Xi'an Jiaotong University）
刊号：780C0092
ISSN：0888-3270
出版年：2023
年卷期：2023, vol.182
页码：109597-1--109597-22
总页数：22
分类号：TH16
关键词：Knowledge driven；Data driven；Industrial robot；Fault diagnosis；Transfer learning
参考中译：知识驱动；数据驱动；工业机器人；故障诊断；迁移学习
语种：eng
文摘：Various deep transfer learning solutions have been developed for machine fault diagnosis, which are purely data driven. Plenty of prior knowledge on the fault of different machinery parts has been summarized in previous research. The value of these prior knowledge has not been ever deeply explored in the existing deep transfer solutions, which might greatly facilitate the training progress and improve the diagnosis performance of network models. However, how to represent and incorporate prior knowledge into deep models remains a challenge. To address this problem, a knowledge and data dual-driven transfer network for fault diagnosis is developed in this paper. The prior frequency knowledge of fault signal is extracted by envelope analysis and pass band selection. Based on which, a band-pass filter based embedding method is proposed to incorporate the prior knowledge by imprinting the convolutional kernels with the designed filters. A dual channel weight shared deep adaptation network is constructed to perform the prior knowledge embedding and knowledge transfer across domains. Multi-kernel maximum mean discrepancy (MK-MMD) is adopted for domain adaption. A symmetry constraint is used to reserve the linear phase property of the band-pass kernels. The constructed network combines data driven and knowledge driven mechanism which is termed as Knowledge and Data Dual-driven Transfer Network (KDDT Network). Extensive experiments have been performed on an industrial robot rotate vector (RV) reducer dataset collected in our laboratory and some bearing benchmarks. Comparisons results with the state-of-the-art methods have shown the superiority of the proposed method.
参考中译：已有各种深度迁移学习解决方案被开发用于机器故障诊断，它们纯粹是数据驱动的。在以往的研究中，已经总结了大量关于不同机械部件故障的先验知识。这些先验知识的价值在现有的深度迁移解决方案中没有得到深入的挖掘，这可能会极大地促进网络模型的训练进度，提高网络模型的诊断性能。然而，如何将先验知识表示并结合到深层模型中仍然是一个挑战。针对这一问题，本文提出了一种知识和数据双驱动的故障诊断传输网络。通过包络分析和通带选择提取故障信号的先验频率知识。在此基础上，提出了一种基于带通滤波器的嵌入方法，通过在卷积核上嵌入所设计的滤波器来融合先验知识。构建了一个双通道权重共享深度适应网络，用于先验知识嵌入和跨域知识转移。域自适应采用多核最大均值差(MK-MMD)算法。对称约束被用来保留带通核的线性相位特性。构建的网络将数据驱动和知识驱动机制相结合，称为知识和数据双驱动传输网络(KDDT Network)。在实验室收集的工业机器人旋转矢量减速器数据集和一些轴承基准上进行了广泛的实验。与最新方法的比较结果表明了该方法的优越性。



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