Predictive maintenance has promised value for years, yet many deployments struggle to scale beyond pilots. A key reason is the dependency on hardware-specific solutions and threshold-based configurations that create noise, complexity, and limited trust.
In this session, Rajet Krishnan shares a different approach: a sensor-agnostic, behavior-based model that removes the need for manual thresholds and enables predictive maintenance to scale across sites and equipment types.
Through real-world examples from mining and process industries, he will show how organizations can reduce hundreds of weekly alarms to a handful of meaningful signals, improve early fault detection, and deploy solutions faster without being tied to specific hardware ecosystems.
