Comparative Analysis of AI-Driven Robotics for Predictive Maintenance and Fault Detection in Motor Drive Control Systems:A Review

Author(s)	Prof. Madhugandha Nivritti Bhosale, Prof. Prashant Laxman Pandit, Prof. Sayema Asfahan Khusro, Prof. Pooja Rajendra Murkute
Country	India
Abstract	The integration of Artificial Intelligence (AI) and robotics is central to the Industry 4.0 paradigm, particularly for optimizing Predictive Maintenance (PM) and Fault Detection (FD) in industrial systems. This comparative analysis reviews two key AI-driven approaches: a general PM framework for industrial robots in automotive manufacturing and a specialized hybrid deep learning model (CNN-RNN) for fault detection in motor drive control systems. The comparison highlights the shift from general condition monitoring to highly accurate, component-specific fault prediction models, demonstrating the superior performance and computational efficiency of specialized hybrid architectures like the Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) over traditional and contemporary methods for critical motor drive applications.
Keywords	Predictive Maintenance, Fault Detection, CNN, RNN, LSTM
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-26
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.61419
Short DOI	https://doi.org/hbdrhp

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International Journal For Multidisciplinary Research