Key takeaways
- The four maturity levels of maintenance, from manual visual inspection through instrument-assisted checks and real-time rule-based monitoring to true AI-driven predictive maintenance
- The four questions predictive maintenance has to answer: whether equipment will fail, what will fail, when it will happen, and why
- How Driverless AI automatically engineers thousands of features from raw sensor streams, in the demo turning 21 turbofan sensors into 5,000 processed indicators
- How machine learning interpretability points engineers to the specific sensor behavior driving a failure prediction, such as an amplitude spike, so they know exactly what to check
- The measurable upside: predictive maintenance can cut downtime by up to 50 percent, lower maintenance costs by 30 percent, and reduce unnecessary scheduled repairs by 12 percent
- How a MOJO model export and auto-generated documentation make it practical to deploy and audit these models in production
Overview
Unplanned downtime costs industry more than 50 billion dollars a year, from 1.3 million dollars an hour for an idle car plant to 20 billion a year in pharma and healthcare. In this session, H2O.ai's Ralph and Oscar break down the four maturity levels of maintenance, from manual visual inspection to true predictive maintenance, noting only about 11 percent of organizations have made that jump. Using a turbofan engine's sensor data as a live example, they build a Driverless AI model that predicts whether, what, when, and why equipment will fail, then show how interpretability turns that prediction into an actionable reason for engineers.