Industrial pipelines continuously produce signals. Pressure variations, flow irregularities, temperature shifts and vibration patterns can reveal developing faults—but only if the patterns are identified in time.
Our project explores how machine learning can transform these process measurements into an actionable early-warning system for chemical plants.