Build predictive models for failure and multi-class failure

Most companies have sensor data and historical records of what went wrong, but most still can't predict failures accurately. The gap isn't data; it's how you use it.

Getting a working predictive maintenance model requires three things working together: clean data that actually matters, a problem statement that matches your business and validation that tells you whether your model is correct.

From reactive maintenance to predictive intervention

Getting the right data
Historical failure records combined with real-time sensor streams from PLCs and SCADA systems. But not all data matters. You need variables that actually predict failure.

Framing the problem
A false positive costs a maintenance call, but a false negative costs a production line. The document shows how to map your metrics to business KPIs so you know which mistake is more expensive.

Cleaning and preparing data
Raw data breaks models. The document covers the governed process for pulling data from disparate systems into a single format your models can trust.

Binary and multi-class failures
Some equipment either works or fails. Others degrade gradually via reduced speed, lower throughput or quality drift. Build models for both.

Download the brochue to learn how to go from reactive maintenance to predictive intervention.