Abstract Monitoring the transmission status of multi-joint industrial robots is very important for the accuracy of the robot motion. The fault diagnosis information is an indispensable basis for the collaborative maintenance of the robots in industry 4.0. In this paper, an attitude data-based intelligent fault diagnosis approach is proposed for multi-joint industrial robots. Based on the analysis of the transmission mechanism, the attitude change of the last joint is employed to reflect the transmission fault of robot components. An economical data acquisition strategy is performed by only installing one attitude sensor on the last joint of the multi-joint robot. Considering the characteristics of attitude data, a hybrid sparse auto-encoder (SAE) and support vector machine (SVM) approach, namely SAE-SVM, is subsequently presented to construct an intelligent fault diagnosis model by learning from the attitude dataset with multiple fault information. Experimental results show that the proposed fault diagnosis approach has promising performance in identifying different faults related to the reducer of a 6-axial multi-joint industrial robot accurately and reliably.

中文翻译：

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基于姿态数据的深度混合学习架构，用于多关节工业机器人智能故障诊断

摘要 监测多关节工业机器人的传动状态对机器人运动的准确性至关重要。故障诊断信息是工业4.0机器人协同维护不可或缺的基础。本文提出了一种基于姿态数据的多关节工业机器人智能故障诊断方法。在分析传动机构的基础上，利用最后一个关节的姿态变化来反映机器人部件的传动故障。通过在多关节机器人的最后一个关节上仅安装一个姿态传感器来执行经济的数据采集策略。考虑到姿态数据的特点，一种混合​​稀疏自编码器（SAE）和支持向量机（SVM）的方法，即SAE-SVM，随后提出了通过从具有多个故障信息的姿态数据集中学习来构建智能故障诊断模型。实验结果表明，所提出的故障诊断方法在准确可靠地识别与六轴多关节工业机器人减速器相关的不同故障方面具有良好的性能。