TY - GEN
T1 - The Classification of Impact Signal of 6 DOF Cobot by Means of Machine Learning Model
AU - Kai, Gavin Lim Jiann
AU - Nasir, Ahmad Fakhri Ab
AU - Majeed, Anwar P.P.Abdul
AU - Razman, Mohd Azraai Mohd
AU - Khairuddin, Ismail Mohd
AU - Li, Lim Thai
N1 - Funding Information:
Acknowledgements The authors would like to thank TT Vision Holdings Berhad for providing the Balluff sensor and OMRON TM Cobot to make this evaluation possible as well as for funding the study in collaboration with Universiti Malaysia Pahang via UIC200817 and RDU202406.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Condition-based monitoring
KW - Feature selection
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85131128767&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-2095-0_47
DO - 10.1007/978-981-19-2095-0_47
M3 - Conference Proceeding
AN - SCOPUS:85131128767
SN - 9789811920943
T3 - Lecture Notes in Electrical Engineering
SP - 553
EP - 560
BT - Enabling Industry 4.0 through Advances in Mechatronics - Selected Articles from iM3F 2021
A2 - Khairuddin, Ismail Mohd.
A2 - Abdullah, Muhammad Amirul
A2 - Ab. Nasir, Ahmad Fakhri
A2 - Mat Jizat, Jessnor Arif
A2 - Mohd. Razman, Mohd. Azraai
A2 - Abdul Ghani, Ahmad Shahrizan
A2 - Zakaria, Muhammad Aizzat
A2 - Mohd. Isa, Wan Hasbullah
A2 - Abdul Majeed, Anwar P.
PB - Springer Science and Business Media Deutschland GmbH
T2 - Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2021
Y2 - 20 September 2021 through 20 September 2021
ER -
