Article: A physics-informed neural network-based edge-cloud collaborative digital twin for predictive maintenance of ageing railway signalling equipment Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.87 pp.41 - 70 Abstract: This study proposes a physics-informed neural network (PINN)-based edge-cloud collaborative digital twin (DT) for predictive maintenance of ageing railway signalling equipment (RSE). The core model, an adaptive pigeon-inspired optimised PINN (API-PINN), integrates electromechanical degradation physics with operational data to forecast faults and estimate remaining useful life (RUL). The architecture enables low-latency, real-time anomaly detection at the network edge while leveraging the cloud for intensive model training. Experiments on real-world railway signalling data sets can show that API-PINN attains the prediction accuracy of 98.77% and the R<SUP align="right"><SMALL>2</SMALL></SUP> of 0.968, which is better than the baseline models. The given framework offers a strong performance when the data is noisy and imbalanced, allowing for the arrangement of the maintenance schedule proactively, minimising the costs of operations, minimising downtimes, and improving safety. Inderscience Publishers - linking academia, business and industry through research

Title: A physics-informed neural network-based edge-cloud collaborative digital twin for predictive maintenance of ageing railway signalling equipment

Authors: Guobin Ren

Addresses: School of Traffic and Transportation, Shaanxi College of Communications Technology, Xi'an, 710018, China

Abstract: This study proposes a physics-informed neural network (PINN)-based edge-cloud collaborative digital twin (DT) for predictive maintenance of ageing railway signalling equipment (RSE). The core model, an adaptive pigeon-inspired optimised PINN (API-PINN), integrates electromechanical degradation physics with operational data to forecast faults and estimate remaining useful life (RUL). The architecture enables low-latency, real-time anomaly detection at the network edge while leveraging the cloud for intensive model training. Experiments on real-world railway signalling data sets can show that API-PINN attains the prediction accuracy of 98.77% and the R² of 0.968, which is better than the baseline models. The given framework offers a strong performance when the data is noisy and imbalanced, allowing for the arrangement of the maintenance schedule proactively, minimising the costs of operations, minimising downtimes, and improving safety.

Keywords: predictive maintenance; digital twin; edge-cloud computing; railway signalling equipment; RSE; ageing infrastructure.

DOI: 10.1504/IJICT.2026.155637

International Journal of Information and Communication Technology, 2026 Vol.27 No.87, pp.41 - 70

Received: 19 Jan 2026
Accepted: 28 Feb 2026
Published online: 07 Aug 2026 *

Title: A physics-informed neural network-based edge-cloud collaborative digital twin for predictive maintenance of ageing railway signalling equipment

Keep up-to-date