We are thrilled to share that Machine Design published an in-depth article by Brad Carman, Director of Consulting Services at JuliaHub, exploring how Scientific Machine Learning (SciML) is transforming predictive maintenance across manufacturing.
The Challenge
Equipment failures cost U.S. manufacturers roughly $50 billion annually in lost productivity and repairs. While traditional machine learning offers predictive capabilities, it requires massive volumes of clean historical data, something that rarely exists in real-world industrial environments.
The SciML Solution
As highlighted in the Machine Design article, SciML takes a fundamentally different approach by combining data-driven learning with physics-based models. The key advantage:
80% accuracy from engineering specifications alone
Only 20% requires on-site telemetry for calibration
Detects failure modes that have never appeared in historical data
Works with incomplete, noisy, or poor-quality data
Real-World Impact
The article highlights measurable results across industries:
Manufacturing: 50% increase in operational efficiency
Aviation: 20% reduction in maintenance costs with 99.9% diagnostic accuracy
Automotive: 500× faster models with 2× better accuracy
HVAC: 15% reduction in downtime, 10% reduction in energy costs
Unlike traditional predictive maintenance programs that improve one piece of equipment at a time, SciML scales rapidly across entire asset fleets. A single physics-based model can be deployed across ten or ten thousand machines, delivering ROI faster and more consistently.
Read the Full Article
The Machine Design article by Brad provides detailed insights into how SciML creates digital twins, builds virtual sensors, and transforms deployment strategy from isolated pilots to scalable, physics-based intelligence.
Want to learn how SciML can transform predictive maintenance for your operations? Contact our team to discuss your specific challenges.
