Aug 2026· Advances in Science and Technology· Vol 181, pp. 287 - 292· 0 citations· 10 references
TL;DR
This paper introduces a specialized Parallel Inception-style 1D-CNN (PINC) architecture designed for real-time condition monitoring of the Universal Robots UR3e platform and shows that the PINC model effectively captures vibrational signatures without depending on high-latency cloud processing.
Abstract
This paper introduces a specialized Parallel Inception-style 1D-CNN (PINC) architecture designed for real-time condition monitoring of the Universal Robots UR3e platform. Traditional deep learning models for anomaly detection often require substantial computational resources. In contrast, our proposed PINC model uses per-channel parallel feature extraction to identify multi-scale physical anomalies with high accuracy. Using the CASPER dataset, the architecture achieves a diagnostic accuracy of 99.1% while maintaining a low memory footprint of just 152 KB. These findings show that the PINC model effectively captures vibrational signatures without depending on high-latency cloud processing. The results offer a scalable and efficient hardware solution that enables localized, real-time fault detection in human-robot shared workspaces.
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