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hardCase StudyScenarioSeniorStaffLead

Most predictive maintenance projects never reach production. Why, and what would you do differently?

6 min readUpdated 2026-08-15

Answer

Short answer

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.

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Likely follow-ups

  • You have 40,000 hours of sensor data and eleven recorded failures. What can you honestly attempt?
  • How would you split this dataset, and what does a random split get wrong?
  • The plant already has vibration alarms. What does your model have to beat, and by how much?
  • Six months in, precision has halved with no code change. What do you check first?

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