Bashir M. Ahmad

Development and Analysis of AI-Driven Anomaly Detection and Predictive Maintenance Algorithms for Robotic Systems in Industrial Environments: Leveraging Electrical and Sensor Data

This study finds no evidence that attention-enhanced temporal autoencoders offer a universal advantage over simpler deep learning baselines for industrial robotic predictive maintenance; any benefit appears concentrated in detecting subtle, temporally extended fault signatures, is small in magnitude, and was not confirmed as statistically significant in the present sample.