Machine Learning Approaches for Robot Fault Diagnosis in Industrial Settings: An Application of Industry 4.0
Keywords:
Keywords: Industrial automation, Robotic manipulators, Fault detection, Artificial neural network, Precise Manufacturing.Abstract
Robotic manipulators play a crucial role in industrial automation, where ensuring their fault-freeoperation is vital for maintaining safety and efficiency. This research introduces a comprehensive multiclass fault detection and classification (FDC) approach, focusing on analyzing force and torque data toidentify various fault types during different operational phases.
References
Arents, J.; Greitans, M. Smart Industrial Robot Control Trends, Challenges and Opportunities Within Manufacturing. Appl. Sci. 2022, 12, 937.
Zhong, R.Y.; Xu, X.; Klotz, E.; Newman, S.T. Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering 2017, 3, 616–630.
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Published
2025-04-03
How to Cite
Pasupunooti Anusha, Tipparapu Vijender, Palnati Aryan, Ramu Pavan, Tallapelly Nithin. (2025). Machine Learning Approaches for Robot Fault Diagnosis in Industrial Settings: An Application of Industry 4.0 . Journal of Computational Analysis and Applications (JoCAAA), 34(4), 50–60. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2272
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