The classification of impact signal of 6 DOF cobot by means of machine learning model
Collaborative robot (Cobot) has seen a rise in adoption rate in the industry as the Industry 4.0 era marches in. Cobot were introduced to replace human operators in harsh environments or repetitive work processes. The health condition monitoring of these cobot have not been standardized due to lack...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer Science and Business Media Deutschland GmbH
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42303/1/The%20classification%20of%20impact%20signal%20of%206%20DOF%20cobot.pdf http://umpir.ump.edu.my/id/eprint/42303/2/The%20classification%20of%20impact%20signal%20of%206%20DOF%20cobot%20by%20means%20of%20machine%20learning%20model_ABS.pdf |
| _version_ | 1817928166859079680 |
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| author | Kai, Gavin Lim Jiann Ahmad Fakhri, Ab. Nasir P.P. Abdul Majeed, Anwar Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin Li, Lim Thai |
| author_facet | Kai, Gavin Lim Jiann Ahmad Fakhri, Ab. Nasir P.P. Abdul Majeed, Anwar Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin Li, Lim Thai |
| author_sort | Kai, Gavin Lim Jiann |
| collection | UMP |
| description | Collaborative robot (Cobot) has seen a rise in adoption rate in the industry as the Industry 4.0 era marches in. Cobot were introduced to replace human operators in harsh environments or repetitive work processes. The health condition monitoring of these cobot have not been standardized due to lack of widely available standardized fault dataset and the high complexity of diagnostic. This study aims to use machine learning algorithms as a mean to identify the cobot pick and place process offset error using vibrational signals. The vibrational sensor was attached to the end effector of the cobot where the vibration signal of 3 axis were collected. The features were then extracted, standardized, and 544 features were selected from 2337 features based on a hypothesis testing method. The dataset was then spilt into training and testing by a ratio of 80:20. Three machine learning models namely, the k-Nearest Neighbors (k-NN), Neural Network (NN), and Support Vector Machine (SVM) classifier were tested, and the classification accuracy of the models was analyzed. A grid search approach was used to identify the best hyperparameter for each model. The model with the highest classification accuracy of 95.2% was the MLP model compared to SVM (92.4%) and kNN (79%). Therefore, it could be established from the study that a comparable classification efficacy is attainable through the identification of significant features. The findings are non-trivial, particularly with respect to the implementation of the developed classifier in real-time. |
| first_indexed | 2024-12-09T02:30:01Z |
| format | Conference or Workshop Item |
| id | UMPir42303 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-12-09T02:30:01Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | UMPir423032024-10-30T04:28:26Z http://umpir.ump.edu.my/id/eprint/42303/ The classification of impact signal of 6 DOF cobot by means of machine learning model Kai, Gavin Lim Jiann Ahmad Fakhri, Ab. Nasir P.P. Abdul Majeed, Anwar Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin Li, Lim Thai QA75 Electronic computers. Computer science T Technology (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Collaborative robot (Cobot) has seen a rise in adoption rate in the industry as the Industry 4.0 era marches in. Cobot were introduced to replace human operators in harsh environments or repetitive work processes. The health condition monitoring of these cobot have not been standardized due to lack of widely available standardized fault dataset and the high complexity of diagnostic. This study aims to use machine learning algorithms as a mean to identify the cobot pick and place process offset error using vibrational signals. The vibrational sensor was attached to the end effector of the cobot where the vibration signal of 3 axis were collected. The features were then extracted, standardized, and 544 features were selected from 2337 features based on a hypothesis testing method. The dataset was then spilt into training and testing by a ratio of 80:20. Three machine learning models namely, the k-Nearest Neighbors (k-NN), Neural Network (NN), and Support Vector Machine (SVM) classifier were tested, and the classification accuracy of the models was analyzed. A grid search approach was used to identify the best hyperparameter for each model. The model with the highest classification accuracy of 95.2% was the MLP model compared to SVM (92.4%) and kNN (79%). Therefore, it could be established from the study that a comparable classification efficacy is attainable through the identification of significant features. The findings are non-trivial, particularly with respect to the implementation of the developed classifier in real-time. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42303/1/The%20classification%20of%20impact%20signal%20of%206%20DOF%20cobot.pdf pdf en http://umpir.ump.edu.my/id/eprint/42303/2/The%20classification%20of%20impact%20signal%20of%206%20DOF%20cobot%20by%20means%20of%20machine%20learning%20model_ABS.pdf Kai, Gavin Lim Jiann and Ahmad Fakhri, Ab. Nasir and P.P. Abdul Majeed, Anwar and Mohd Azraai, Mohd Razman and Ismail, Mohd Khairuddin and Li, Lim Thai (2022) The classification of impact signal of 6 DOF cobot by means of machine learning model. In: Lecture Notes in Electrical Engineering. Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2021 , 20 September 2021through 20 September 2021 , Gambang. pp. 553-560., 900. ISSN 1876-1100 ISBN 978-981192094-3 (Published) https://doi.org/10.1007/978-981-19-2095-0_47 |
| spellingShingle | QA75 Electronic computers. Computer science T Technology (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Kai, Gavin Lim Jiann Ahmad Fakhri, Ab. Nasir P.P. Abdul Majeed, Anwar Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin Li, Lim Thai The classification of impact signal of 6 DOF cobot by means of machine learning model |
| title | The classification of impact signal of 6 DOF cobot by means of machine learning model |
| title_full | The classification of impact signal of 6 DOF cobot by means of machine learning model |
| title_fullStr | The classification of impact signal of 6 DOF cobot by means of machine learning model |
| title_full_unstemmed | The classification of impact signal of 6 DOF cobot by means of machine learning model |
| title_short | The classification of impact signal of 6 DOF cobot by means of machine learning model |
| title_sort | classification of impact signal of 6 dof cobot by means of machine learning model |
| topic | QA75 Electronic computers. Computer science T Technology (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/42303/1/The%20classification%20of%20impact%20signal%20of%206%20DOF%20cobot.pdf http://umpir.ump.edu.my/id/eprint/42303/2/The%20classification%20of%20impact%20signal%20of%206%20DOF%20cobot%20by%20means%20of%20machine%20learning%20model_ABS.pdf |
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