Predictive Maintenance

The pitch behind predictive maintenance is straightforward — catch equipment failures before they happen instead of after — but this cluster gets into what that actually requires: the sensors and data feeds needed to train a model on a specific machine’s failure patterns, and how the resulting predictions differ from a fixed preventive-maintenance schedule that services equipment on a calendar regardless of its actual condition.

It also covers where these projects tend to struggle in practice — the data quality and integration challenges that make predictive maintenance harder to deploy successfully than the pitch suggests, and why predicting a specific failure mode is a different problem than simply flagging that something, eventually, will break.