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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...

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Main Authors:	Kai, Gavin Lim Jiann, Ahmad Fakhri, Ab Nasir, Anwar, P. P. Abdul Majeed, Mohd Azraai, Mohd Razman, Ismail, Mohd Khairuddin, Li, Lim Thai
Format:	Conference or Workshop Item
Language:	English English
Published:	Springer Science and Business Media Deutschland GmbH 2022
Subjects:	T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/39456/1/The%20Classification%20of%20Impact%20Signal%20of%206%20DOF%20Cobot%20by%20Means%20of%20Machine.pdf http://umpir.ump.edu.my/id/eprint/39456/2/The%20classi%EF%AC%81cation%20of%20impact%20signal%20of%206%20DOF%20cobot%20by%20means%20of%20machine%20learning%20model_ABS.pdf http://umpir.ump.edu.my/id/eprint/39456/ https://doi.org/10.1007/978-981-19-2095-0_47
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spelling	my.ump.umpir.394562023-12-01T01:36:57Z http://umpir.ump.edu.my/id/eprint/39456/ The classiﬁcation of impact signal of 6 DOF cobot by means of machine learning model Kai, Gavin Lim Jiann Ahmad Fakhri, Ab Nasir Anwar, P. P. Abdul Majeed Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin Li, Lim Thai T Technology (General) TA Engineering (General). Civil engineering (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/39456/1/The%20Classification%20of%20Impact%20Signal%20of%206%20DOF%20Cobot%20by%20Means%20of%20Machine.pdf pdf en http://umpir.ump.edu.my/id/eprint/39456/2/The%20classi%EF%AC%81cation%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 Anwar, P. P. Abdul Majeed 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 2021 , Gambang. 553- 560., 900 (277979). ISSN 1876-1100 ISBN 978-981192094-3 https://doi.org/10.1007/978-981-19-2095-0_47
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English English
topic	T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures
spellingShingle	T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Kai, Gavin Lim Jiann Ahmad Fakhri, Ab Nasir Anwar, P. P. Abdul Majeed 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
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.
format	Conference or Workshop Item
author	Kai, Gavin Lim Jiann Ahmad Fakhri, Ab Nasir Anwar, P. P. Abdul Majeed Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin Li, Lim Thai
author_facet	Kai, Gavin Lim Jiann Ahmad Fakhri, Ab Nasir Anwar, P. P. Abdul Majeed Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin Li, Lim Thai
author_sort	Kai, Gavin Lim Jiann
title	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_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_sort	classiﬁcation of impact signal of 6 dof cobot by means of machine learning model
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2022
url	http://umpir.ump.edu.my/id/eprint/39456/1/The%20Classification%20of%20Impact%20Signal%20of%206%20DOF%20Cobot%20by%20Means%20of%20Machine.pdf http://umpir.ump.edu.my/id/eprint/39456/2/The%20classi%EF%AC%81cation%20of%20impact%20signal%20of%206%20DOF%20cobot%20by%20means%20of%20machine%20learning%20model_ABS.pdf http://umpir.ump.edu.my/id/eprint/39456/ https://doi.org/10.1007/978-981-19-2095-0_47
_version_	1822923893784969216
score	13.234278

The classiﬁcation of impact signal of 6 DOF cobot by means of machine learning model

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