Title: Fault prediction and diagnosis of programmable logic controller control units via multi-sensor data

Authors: Lixia Nan; Xinni Hao

Addresses: School of Mechanical Engineering, Yangzhou Polytechnic University, YangZhou, 225009, China ' School of Mechanical Engineering, Yangzhou Polytechnic University, YangZhou, 225009, China

Abstract: Ensuring the reliable operation of programmable logic controller control units is essential for industrial system safety. Traditional diagnostic approaches, which rely on manual inspection and delayed response, prove inadequate for the predictive maintenance requirements of smart manufacturing. This study tackles challenges in multi-sensor data fusion and model adaptability by developing an intelligent framework that combines digital twin technology with meta-learning. The approach integrates vibration and current signals through an attention-based fusion network, enabling rapid adaptation to new equipment with minimal data samples. Experimental results demonstrate exceptional performance: a Matthews correlation coefficient of 0.965 for fault classification and a root mean square error of 0.048 for remaining useful life prediction. These results significantly surpass current state-of-the-art methods, improving diagnostic accuracy by over 3.4% and prediction precision by more than 17% compared to the best existing baseline.

Keywords: programmable logic controllers; PLCs; fault prediction; digital twins; meta-learning; multi-sensor fusion.

DOI: 10.1504/IJSNET.2026.154342

International Journal of Sensor Networks, 2026 Vol.51 No.2, pp.87 - 100

Received: 16 Nov 2025
Accepted: 20 Nov 2025
Published online: 23 Jun 2026 *

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