Accurate operational monitoring in energy systems relies on long-duration experimental data to assess performance stability, degradation behavior, and component reliability. In hydrogen stack experiments, high-frequency measurements—such as temperature, voltage, current density, and gas-related signals—are often affected by stochastic noise, sensor drift, and intermittent disturbances. These factors complicate trend interpretation and can impact operational decisions.

We present a real-time signal conditioning framework designed to enhance the fidelity of operational measurements in hydrogen stack testing. The approach constructs high-quality reference signals from curated historical datasets and characterizes measurement variability through controlled noise modeling. A data-driven artificial intelligence (AI) model then separates true physical trends from stochastic and disturbance-driven noise, enabling robust signal conditioning prior to down-sampling and further analysis.
The framework supports real-time deployment within experimental monitoring architectures and is evaluated against conventional filtering techniques, focusing on trend preservation, stability of degradation rate estimation, and consistency of derived performance indicators. While demonstrated with hydrogen stack data, the methodology is applicable to other safety-critical industrial systems where measurement quality directly influences performance evaluation and reliability assessment.