Plants increasingly are turning to machine learning (ML) and artificial intelligence (AI) to recognize precise patterns in sensor data. The technologies ease differentiating between normal and abnormal equipment behavior and also detecting specific patterns that lead up to failures — and, so, enhance capabilities for predictive maintenance.
Meanwhile, advances in sensor technology are reducing both procurement and implementation costs and, thus, spurring greater adoption. “It’s now economically feasible to roll out shadow sensing technology in both greenfield and brownfield applications to capture high fidelity data in volumes that were unachievable several years ago,” notes Jim Chappell, vice president, information solutions for Aveva, Chicago.
The incentive for adopting predictive maintenance is compelling, stresses Mike Brooks, senior director, APM Consulting, Aspen Technology, Bedford, Mass. “A European customer tells us that 15% gross margin losses are attributable to unplanned maintenance. Even best-in-class approaches 4–5% losses.”