利用堆叠式变压器编码器层进行基于机器人动力学的电缆故障诊断

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Heonkook Kim
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引用次数: 0

摘要

工业机器人在制造系统中扮演着重要角色,从事焊接、喷涂和组装等任务。为防止灾难性的制造停工,及时诊断机器人控制电缆的故障至关重要。本文提出了一种混合故障诊断方法,将机器人动态模型与基于深度学习的故障诊断相结合,对电缆故障的严重程度进行分类。具体来说,该方法将测量电流获得的测量力矩和动态模型获得的额定力矩结合在一起,实现了在不同运行条件下对电缆故障的稳健诊断。包含故障信息的电缆电流测量信号用于计算关节扭矩,而机器人动态模型则利用关节角度和角速度获得标称关节扭矩。随后,以获得的扭矩差异为输入,以故障严重性概率为输出,构建了基于叠加变压器编码器的分类器。实验结果证明,与现有方法相比,所提出的故障诊断方法具有更高的准确性,突出了将动态模型与基于学习的故障诊断相结合的功效。此外,我们还对所提出的方法和其他最新的故障诊断方法进行了定量和定性比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Robot dynamics-based cable fault diagnosis using stacked transformer encoder layers

Robot dynamics-based cable fault diagnosis using stacked transformer encoder layers

Industrial robots play a vital role in manufacturing systems, engaging in tasks such as welding, painting, and assembling. To prevent catastrophic manufacturing stoppage, it is essential to diagnose faults in control cables of robots in time. This paper proposes a hybrid fault diagnosis method that integrates a robot dynamic model with deep learning-based fault diagnosis to classify the severity of cable faults. Specifically, the proposed method incorporates both the measured torques obtained from measured currents and nominal torques from the dynamic model, achieving robust cable fault diagnosis under varying operating conditions. The measured cable current signals that contain the fault information are used to calculate the joint torques, and a robot dynamic model is used to obtain the nominal joint torques using joint angles and angular velocities. Subsequently, a stacked transformer encoder-based classifier is constructed with the obtained torque disparities as inputs and fault severity probabilities as outputs. Experimental results validate that the proposed fault diagnosis method provides higher accuracy compared to existing methods, highlighting the efficacy of integrating a dynamic model with learning-based fault diagnosis. Furthermore, we conducted a quantitative and qualitative comparison between our proposed method and other recent fault diagnosis methods.

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来源期刊
Electrical Engineering
Electrical Engineering 工程技术-工程:电子与电气
CiteScore
3.60
自引率
16.70%
发文量
0
审稿时长
>12 weeks
期刊介绍: The journal “Electrical Engineering” following the long tradition of Archiv für Elektrotechnik publishes original papers of archival value in electrical engineering with a strong focus on electric power systems, smart grid approaches to power transmission and distribution, power system planning, operation and control, electricity markets, renewable power generation, microgrids, power electronics, electrical machines and drives, electric vehicles, railway electrification systems and electric transportation infrastructures, energy storage in electric power systems and vehicles, high voltage engineering, electromagnetic transients in power networks, lightning protection, electrical safety, electrical insulation systems, apparatus, devices, and components. Manuscripts describing theoretical, computer application and experimental research results are welcomed. Electrical Engineering - Archiv für Elektrotechnik is published in agreement with Verband der Elektrotechnik Elektronik Informationstechnik eV (VDE).
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