# Agent context: Unplanned Equipment Downtime

Source: https://aiplaybooks.app/industries/manufacturing/maintenance/unplanned-downtime
AI suitability: conditional

## Objective

Score which monitored assets are at elevated risk of failure within an actionable horizon, so a maintenance planner can schedule intervention inside the next planned maintenance window instead of reacting to a stoppage.

## Constraints

- Never stop or de-rate a production line automatically. The system scores risk, a maintenance planner or reliability engineer decides.
- Correct for survivorship bias in training data: an averted failure is not the same as normal operation, and must not be labelled as such.
- Treat CMMS-logged failure time as approximate, not exact, since it usually records when the repair was closed, not when the fault began.
- Flag the affected model output whenever sensor calibration, sensor replacement, or a setpoint change is detected, rather than let it silently drift.
- Do not score cheap, redundant, non-critical components already on a run-to-failure strategy, since a prediction there changes no decision.
- Size alert lead time to the real planned maintenance window calendar, not to a generic forecast horizon.
- Favor precision over recall wherever the response is an expensive teardown, since an unnecessary teardown creates its own failure risk.

## Inputs

- Vibration RMS, spindle motor current, bearing temperature, and oil particulate counts, sampled continuously or at high frequency per asset.
- CMMS work order history with asset ID, fault description, parts replaced, and open and close dates.
- Asset criticality rating and redundancy status from the equipment master.
- Production schedule and planned maintenance window calendar.

## Outputs

- A failure risk score per asset with an estimated horizon.
- The sensor readings and trend that drove the score, in language a technician can verify on the shop floor.
- A drift flag when a sensor's reading distribution shifts, kept separate from the failure risk queue.
- A ranked worklist of assets to inspect during the next planned maintenance window, truncated to technician capacity.

## Success criteria

- Unplanned downtime hours decrease on instrumented lines relative to the baseline period, at stable or improved precision.
- Unnecessary teardown rate does not increase as alert volume changes.
- Every alert traces back to specific sensor readings and the asset's recent trend.
- A drift flag is resolved, meaning calibration confirmed, sensor swap logged, or setpoint change recorded, before the affected model output is trusted again.

## Known pitfalls

- Training a failure risk model on raw CMMS history without correcting for survivorship bias, so it learns the existing maintenance schedule rather than actual equipment health.
- Treating the "time of failure" in a work order as precise, when it usually just records when the repair was logged, not when the fault began.
- Letting a threshold alarm or a risk score go unrecalibrated after a sensor swap, recalibration, or setpoint change, so it keeps alarming on a distribution that no longer exists.
- Applying a failure risk model to cheap, redundant components where run-to-failure was already the right call, spending model effort on a decision that never changes.
- Chasing recall on an alert that feeds an expensive teardown decision, which drives false alarms that create their own failure risk and burn technician trust.

## When not to use AI here

- The component is cheap, redundant, and non-critical, so run-to-failure is already the correct strategy and a prediction would not change the response.
- Confirmed failure events are too rare or too poorly timestamped in CMMS history to build a reliable training set.
- No planned maintenance window falls within the prediction horizon, so a warning would arrive with no way to act on it.
- Sensor instrumentation on the asset has recently changed, such as recalibration, replacement, or a setpoint change, and that shift has not yet been accounted for in the model.

## Candidate approaches

- Condition threshold alarms (primary): Fixed alarms on individual sensor readings, such as vibration RMS or bearing temperature, that flag an asset once it crosses a set point.
- Failure risk model (primary): A supervised model trained on historical sensor streams and CMMS outcomes that scores each asset's probability of failure within an actionable horizon.
- Sensor drift screening (discovery): Unsupervised monitoring of sensor reading distributions that flags when a stream has shifted, without asserting that the equipment itself is failing.
- Maintenance log triage (supporting): A text classifier trained on CMMS work order free text that extracts fault type, affected component, and likely failure mode to supply cleaner labels for the other solutions.

## Human oversight

A maintenance planner or reliability engineer reviews every failure risk alert and decides whether and when to act. No score stops a production line, schedules a teardown, or reorders parts automatically, and any drift flag is confirmed by a reliability engineer before the affected model output is trusted again.

## Sources

- [Reliability-Centered Maintenance](https://apps.dtic.mil/sti/citations/ADA066579), United Airlines for the US Department of Defense, 1978, research
- [Prognostic modeling of predictive maintenance with survival analysis for mobile work equipment](https://doi.org/10.1038/s41598-022-12572-z), Scientific Reports, 2022, research