Technical Diagnostics of Industrial Robots Using Vibration Signals: Case Study on Detecting Base Unfastening
In the domain of modern manufacturing digitalization, artificial intelligence tools are increasingly employed for condition monitoring and technical diagnostics. However, the majority of existing methodologies primarily concentrate on the technical diagnosis of rotating machines, with a noticeable l...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/270 |
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| author | Daria Fedorova Vladimír Tlach Ivan Kuric Tomáš Dodok Ivan Zajačko Karol Tucki |
| author_facet | Daria Fedorova Vladimír Tlach Ivan Kuric Tomáš Dodok Ivan Zajačko Karol Tucki |
| author_sort | Daria Fedorova |
| collection | DOAJ |
| description | In the domain of modern manufacturing digitalization, artificial intelligence tools are increasingly employed for condition monitoring and technical diagnostics. However, the majority of existing methodologies primarily concentrate on the technical diagnosis of rotating machines, with a noticeable lack of research addressing these issues in sequential machines. In this paper, we deal with the selection of suitable vibration signal characteristics for the detection of an industrial robot’s release from its base during a handling operation. Statistical methods, including one-way ANOVA and <i>t</i>-tests, were used to identify the most significant features, which allowed us to isolate vibration metrics with significant predictive potential. These selected features were then used as inputs to various machine learning models to evaluate the hypothesis that these parameters can reliably indicate fastening releasing events. The results show that the optimized parameters significantly improve the detection accuracy, thus providing a reliable basis for future applications in predictive maintenance and monitoring. The findings represent an advance in robotic condition monitoring, providing a structured approach to feature selection that improves the reliability of disconnection detection in automated systems with potential applicability in various industrial environments. |
| format | Article |
| id | doaj-art-e7955cf7be9943c39c1cdb647f29ba64 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e7955cf7be9943c39c1cdb647f29ba642025-01-10T13:14:59ZengMDPI AGApplied Sciences2076-34172024-12-0115127010.3390/app15010270Technical Diagnostics of Industrial Robots Using Vibration Signals: Case Study on Detecting Base UnfasteningDaria Fedorova0Vladimír Tlach1Ivan Kuric2Tomáš Dodok3Ivan Zajačko4Karol Tucki5Department of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, SlovakiaDepartment of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, SlovakiaDepartment of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, SlovakiaDepartment of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, SlovakiaDepartment of Automation and Production Systems, Faculty of Mechanical Engineering, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, SlovakiaInstitute of Mechanical Engineering, Department of Production Engineering, Warsaw University of Life Sciences, Nowoursynowska 164, 02-787 Warszawa, PolandIn the domain of modern manufacturing digitalization, artificial intelligence tools are increasingly employed for condition monitoring and technical diagnostics. However, the majority of existing methodologies primarily concentrate on the technical diagnosis of rotating machines, with a noticeable lack of research addressing these issues in sequential machines. In this paper, we deal with the selection of suitable vibration signal characteristics for the detection of an industrial robot’s release from its base during a handling operation. Statistical methods, including one-way ANOVA and <i>t</i>-tests, were used to identify the most significant features, which allowed us to isolate vibration metrics with significant predictive potential. These selected features were then used as inputs to various machine learning models to evaluate the hypothesis that these parameters can reliably indicate fastening releasing events. The results show that the optimized parameters significantly improve the detection accuracy, thus providing a reliable basis for future applications in predictive maintenance and monitoring. The findings represent an advance in robotic condition monitoring, providing a structured approach to feature selection that improves the reliability of disconnection detection in automated systems with potential applicability in various industrial environments.https://www.mdpi.com/2076-3417/15/1/270vibrodiagnosticsdetectionfeature extractionfeature selectionensemble modelneural network |
| spellingShingle | Daria Fedorova Vladimír Tlach Ivan Kuric Tomáš Dodok Ivan Zajačko Karol Tucki Technical Diagnostics of Industrial Robots Using Vibration Signals: Case Study on Detecting Base Unfastening Applied Sciences vibrodiagnostics detection feature extraction feature selection ensemble model neural network |
| title | Technical Diagnostics of Industrial Robots Using Vibration Signals: Case Study on Detecting Base Unfastening |
| title_full | Technical Diagnostics of Industrial Robots Using Vibration Signals: Case Study on Detecting Base Unfastening |
| title_fullStr | Technical Diagnostics of Industrial Robots Using Vibration Signals: Case Study on Detecting Base Unfastening |
| title_full_unstemmed | Technical Diagnostics of Industrial Robots Using Vibration Signals: Case Study on Detecting Base Unfastening |
| title_short | Technical Diagnostics of Industrial Robots Using Vibration Signals: Case Study on Detecting Base Unfastening |
| title_sort | technical diagnostics of industrial robots using vibration signals case study on detecting base unfastening |
| topic | vibrodiagnostics detection feature extraction feature selection ensemble model neural network |
| url | https://www.mdpi.com/2076-3417/15/1/270 |
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