Test and measurement environments generate enormous amounts of measurement data, but most systems still rely on predefined limits and post-processing analysis. As a result, important deviations are often detected too late or not at all.
Artificial Intelligence (AI) introduces a new paradigm: instead of merely recording data, test systems can continuously interpret measurements, detect anomalies, validate test execution, and initiate corrective actions during the test itself.
This white paper looks at how AI can improve measurement and test environments, discusses an application example, and explains where DEWETRON fits in as the bridge between the two.
Enjoy reading!