Other Industries

What matters more than the sector

We are often asked whether this works for a particular industry. The short answer is that the state of the data matters more than the sector.

Is the equipment producing values? Are those values being stored? Can they be linked to quality results? Once those three are established, the approach looks much the same whatever the industry.

How a project runs

Stage 1 — Assessment. Establish what data exists and where it accumulates. This stage almost always turns up both data everyone assumed was there and is not, and data nobody knew existed.

Stage 2 — Collection and standardization. Bring scattered data into one scheme, applying standards such as AAS and OPC-UA to unify formats that differ by machine.

Stage 3 — Analysis and modeling. Build predictive maintenance, quality prediction and process optimization models on the collected data.

Stage 4 — Field validation. Run it on a real line and check the results. If they do not hold up, go back to stage 2.

Stage 5 — Operation and refinement. Continue retraining and maintaining the models.

You can start without much data

Where defect samples are too few for supervised learning, anomaly detection trained only on normal data is a workable starting point. As data accumulates, you move to more precise models.