Failure labelling, feature windows, and why most projects fail on data rather than models.
3 questions in Manufacturing & Industrial IoT.
Start by instrumenting outcomes, because you almost certainly cannot say what happened to any past alert. Delivered recall is model recall multiplied by the rate at which alerts are acted on, so at a fifteen per cent action rate a better model changes nothing that anyone experiences.
Eleven events will not support a supervised remaining-life model, and evaluating one row-wise will make it look excellent. Build a per-machine deviation model with physics-derived features, and score it on how many of the eleven it caught with useful warning and how many alerts that cost per machine per month.
They fail on labels rather than models: failures are recorded as repair dates in free-text work orders, the positive class is nearly empty, feature windows leak the outcome, and no baseline was established, so an encouraging first result cannot be trusted or beaten.