To optimize production diagnostics in robotic assembly lines, our research team has developed a framework that combines machine modeling, vibrodiagnostics, energy consumption analysis, and material flow modeling. By employing machine learning algorithms, we can monitor data from multiple sources in real-time, detect abnormalities, and predict potential issues.
