Predictive Maintenance:
See the failure before it happens
Not "maintenance by schedule" – but "maintenance when the data says so". Survival models and LSTM learn the remaining useful life of each machine from sensor and maintenance history and warn before the costly unplanned downtime hits.
01 The problem – Unplanned downtime is the most expensive downtime
Why rigid maintenance intervals burn money in both directions
Maintenance on a fixed interval is always wrong: too early you replace intact parts, too late the line stops. The unplanned failure is the costliest of all – lost production, express spare parts, extra shifts and missed deadlines add up by the hour.
The signals of an upcoming failure have long been in your data: vibration, temperature, current draw, cycle times, error codes. No human evaluates that for 48 machines around the clock. A model does.
And in the end the machine is repaired anyway – just unplanned, under time pressure and in the middle of the shift.
02 The model – Remaining useful life instead of rigid intervals
Survival analysis estimates failure probability over time, LSTM detects the pattern in the sensor trace
Anlagen mit Vorwarnung (> Schwelle): 7 von 48 Mediane Vorwarnzeit vor Ausfall: 11 Tage
The survival model (Gradient Boosted Cox) gives each machine a failure probability over the coming weeks. The LSTM detects the anomaly in the high-frequency sensor trace that precedes a failure. Together they yield a reliable warning time – on average 11 days, enough for planned maintenance during low load.
03 Business impact – Fewer unplanned hours
The lever: unplanned downtime becomes planned downtime
No round number: every assumption comes from the sample plant and is stored centrally. With your real figures only the input changes, not the method.
| Item | Value |
|---|---|
| Unplanned downtime hours / year | 900 h |
| Downtime cost per hour | €3,200 |
| Baseline: Unplanned downtime cost / year | €2,880,000 |
| Lever: Reduction via predictive maintenance | 32 % |
| Result: savings / year | €921,600 |
Assumptions of a sample plant – in a real project your data replaces these values.
Planned maintenance costs a fraction: it happens during low load, with prepared spare parts and without an extra shift. The model does not change the amount of maintenance – it shifts it to the right moment.
04 Next steps
From the model to the maintenance cockpit
We read sensor and maintenance history from MES/control system – read-only, without touching the controls.
One critical machine type is modelled. In 4 weeks you see the warnings validated against real failures.
Automatic note into the maintenance system: "Machine X – failure risk in Y days, recommended maintenance window Z."