Abstract
Abstract
Digital transformation in metrology, essential for Industry 4.0 and digital Quality Infrastructure, is driving the development of Digital Twins (DTs) to improve accuracy, traceability, and automation. In this work, we developed and experimentally validated a DT at the Portuguese NMI to monitor and optimize standard frequency transfer through an optical fibre link, with the aim of reducing phase instability induced by thermal disturbances, the main uncertainty source in such systems.
The DT combines real-time sensor data, simulation, and Machine Learning algorithms to reproduce the physical link. A Long Short-Term Memory network predicts ambient temperature, which feeds a Neural Network to forecast phase drift. These predictions are used to calculate Fractional Frequency Offsets and apply compensating corrections every 5 and 30 minutes. Measurement uncertainty from the physical and digital components, including the ML models, was quantified using Monte Carlo Dropout to capture random and epistemic contributions. The global expanded uncertainty for phase measurements was estimated as 2.10 ps (k = 2).
The performance was evaluated using a special form of Allan Deviation (ADEVS) and TIE_rms. Periodic corrections significantly improved link stability. At τ = 60 s, ADEVS decreased from 1.64×10⁻¹¹ s without correction to 4.48×10⁻¹² s with 5 minute corrections and 4.94×10⁻¹² s with 30 minute corrections. The 5 minute scheme performed better at short integration times below 120 s, whereas the 30 minute scheme gave lower ADEVS between τ = 240 s and τ = 1200 s. Up to 1200 s, both compensated signals improved by more than 50 % compared with the uncorrected signal.
This work demonstrates, for the first time, that a DT incorporating machine learning–based phase prediction and uncertainty quantification can act as a low-cost, algorithm-driven compensation mechanism for optical frequency transfer links used as secondary links, where primary-laboratory accuracy is not required.