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Discriminative feature learning using a multiscale convolutional capsule network from attitude data for fault diagnosis of industrial robots
参考中译:用于工业机器人故障诊断的多尺度卷积胶囊网络鉴别特征学习

刊名：	Mechanical Systems and Signal Processing
作者：	Jianyu Long（School of Mechanical Engineering, Dongguan University of Technology） Yaoxin Qin（School of Mechanical Engineering, Dongguan University of Technology） Zhe Yang（School of Mechanical Engineering, Dongguan University of Technology） Yunwei Huang（School of Mechanical Engineering, Dongguan University of Technology） Chuan Li（School of Mechanical Engineering, Dongguan University of Technology）
刊号：	780C0092
ISSN：	0888-3270
出版年：	2023
年卷期：	2023, vol.182
页码：	109569-1--109569-18
总页数：	18
分类号：	TH16
关键词：	Fault diagnosis；Industrial robot；Attitude；Multiscale convolutional neural network；Capsule network
参考中译：	故障诊断；工业机器人；姿态；多尺度卷积神经网络；胶囊网络
语种：	eng
文摘：	Effective fault diagnosis is important to ensure the reliability, safety, and efficiency of industrial robots. This article proposes a simple yet effective data acquisition strategy based on transmission mechanism analysis, using only one attitude sensor mounted on an end effector or an output component to monitor the attitude of all transmission components. Unlike widely used vibration-monitoring signals, attitude signals can provide fault features reflecting spatial relationships. Using one attitude sensor facilitates the data collection, but weakens fault features and introduces strong background noise in attitude signals. To learn discriminative features from the attitude data collected by the attitude sensor, a multiscale convolutional capsule network (MCCN) is proposed. In MCCN, integrating low-level and high-level features in a convolutional neural network (CNN) as multiscale features is conductive to noise reduction and robust feature extraction, and a capsule network (CapsNet) is used to recognize the spatial relationships in attitude data. The extracted multiscale features in CNN and the spatial-relational features in CapsNet are fused for effective fault diagnosis of industrial robots. The performance of MCCN is evaluated by attaching a softmax-based classifier and integrating it into different transfer learning frameworks to diagnose faults in industrial robots under single and variable working conditions, respectively. Fault diagnosis experiments were conducted on a 6-axis series industrial robot and a parallel robot-driven 3D printer. The superiority of the proposed MCCN was demonstrated by comparing its performance with the other feature learning methods.
参考中译：	有效的故障诊断对于保证工业机器人的可靠性、安全性和高效性具有重要意义。本文在分析传动机构的基础上，提出了一种简单而有效的数据采集策略，只需在末端执行器或输出部件上安装一个姿态传感器来监测所有传动部件的姿态。与广泛使用的振动监测信号不同，姿态信号可以提供反映空间关系的故障特征。使用一个姿态传感器便于数据采集，但削弱了故障特征，并在姿态信号中引入了较强的背景噪声。为了从姿态传感器采集的姿态数据中学习可区分的特征，提出了一种多尺度卷积胶囊网络。在MCCN中，将低层特征和高层特征集成在卷积神经网络(CNN)中作为多尺度特征有助于降噪和稳健的特征提取，而胶囊网络(CapsNet)用于识别姿态数据中的空间关系。将CNN中提取的多尺度特征与CapsNet中的空间关系特征进行融合，实现了工业机器人的有效故障诊断。通过加入基于Softmax的分类器并将其集成到不同的转移学习框架中来评估MCCN的性能，分别在单一和可变工作条件下诊断工业机器人的故障。在六轴串联工业机器人和并联机器人驱动的3D打印机上进行了故障诊断实验。通过与其他特征学习方法的性能比较，证明了MCCN的优越性。

Discriminative feature learning using a multiscale convolutional capsule network from attitude data for fault diagnosis of industrial robots参考中译:用于工业机器人故障诊断的多尺度卷积胶囊网络鉴别特征学习

Discriminative feature learning using a multiscale convolutional capsule network from attitude data for fault diagnosis of industrial robots
参考中译:用于工业机器人故障诊断的多尺度卷积胶囊网络鉴别特征学习