{"title":"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","authors":"Heonkook Kim","doi":"10.1007/s00202-024-02718-9","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":50546,"journal":{"name":"Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robot dynamics-based cable fault diagnosis using stacked transformer encoder layers\",\"authors\":\"Heonkook Kim\",\"doi\":\"10.1007/s00202-024-02718-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":50546,\"journal\":{\"name\":\"Electrical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electrical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s00202-024-02718-9\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electrical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s00202-024-02718-9","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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.
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).