Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques
This paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and...
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MDPI AG
2024-11-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/12/11/225 |
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| author | Claudio Urrea Carlos Domínguez |
| author_facet | Claudio Urrea Carlos Domínguez |
| author_sort | Claudio Urrea |
| collection | DOAJ |
| description | This paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and vibration signals, with control effort detecting motor and encoder faults, while vibration signals identify bearing faults. This study compares time-domain signal features and wavelet scattering networks, applied by classification algorithms including wide neural networks (WNNs), efficient linear support vector machine (ELSVM), efficient logistic regression (ELR), and kernel naive Bayes (KNB). Results indicate that a WNN, using wavelet scattering features ranked by one-way anova, is optimal due to its consistency and reliability, while these features enhance computational efficiency by reducing classifier size. Sensitivity analysis demonstrates the classifier’s capacity to detect untrained faults, highlighting the importance of effective feature extraction and classification methods for fault diagnosis in complex robotic systems. This research significantly contributes to fault diagnosis in delta robots and lays the groundwork for future studies on fault tolerance control and predictive maintenance planning. Future work will focus on the physical implementation of the delta robot in laboratory settings, aiming to improve operational efficiency and reliability in industrial applications. |
| format | Article |
| id | doaj-art-5776632a66b24f71a2fe58b5e8ef6c31 |
| institution | OA Journals |
| issn | 2227-7080 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-5776632a66b24f71a2fe58b5e8ef6c312025-08-20T02:27:40ZengMDPI AGTechnologies2227-70802024-11-01121122510.3390/technologies12110225Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence TechniquesClaudio Urrea0Carlos Domínguez1Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile (USACH), Las Sophoras 165, Estación Central, Santiago 9170020, ChileElectrical Engineering Department, Faculty of Engineering, University of Santiago of Chile (USACH), Las Sophoras 165, Estación Central, Santiago 9170020, ChileThis paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and vibration signals, with control effort detecting motor and encoder faults, while vibration signals identify bearing faults. This study compares time-domain signal features and wavelet scattering networks, applied by classification algorithms including wide neural networks (WNNs), efficient linear support vector machine (ELSVM), efficient logistic regression (ELR), and kernel naive Bayes (KNB). Results indicate that a WNN, using wavelet scattering features ranked by one-way anova, is optimal due to its consistency and reliability, while these features enhance computational efficiency by reducing classifier size. Sensitivity analysis demonstrates the classifier’s capacity to detect untrained faults, highlighting the importance of effective feature extraction and classification methods for fault diagnosis in complex robotic systems. This research significantly contributes to fault diagnosis in delta robots and lays the groundwork for future studies on fault tolerance control and predictive maintenance planning. Future work will focus on the physical implementation of the delta robot in laboratory settings, aiming to improve operational efficiency and reliability in industrial applications.https://www.mdpi.com/2227-7080/12/11/225delta robotfault diagnosiskruskal-wallisone-way anovawavelet scattering networkswide neural networks |
| spellingShingle | Claudio Urrea Carlos Domínguez Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques Technologies delta robot fault diagnosis kruskal-wallis one-way anova wavelet scattering networks wide neural networks |
| title | Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques |
| title_full | Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques |
| title_fullStr | Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques |
| title_full_unstemmed | Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques |
| title_short | Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques |
| title_sort | fault diagnosis in a four arm delta robot based on wavelet scattering networks and artificial intelligence techniques |
| topic | delta robot fault diagnosis kruskal-wallis one-way anova wavelet scattering networks wide neural networks |
| url | https://www.mdpi.com/2227-7080/12/11/225 |
| work_keys_str_mv | AT claudiourrea faultdiagnosisinafourarmdeltarobotbasedonwaveletscatteringnetworksandartificialintelligencetechniques AT carlosdominguez faultdiagnosisinafourarmdeltarobotbasedonwaveletscatteringnetworksandartificialintelligencetechniques |