Predictive Asset Maintenance
Rotating equipment failure predicted 4–8 weeks before it happens.
Unplanned downtime is the single largest hidden cost in physical manufacturing. Colony instruments rotating equipment with vibration and acoustic sensors and runs an ML model that predicts bearing and motor failure weeks in advance — shifting maintenance from reactive to planned and releasing spare-parts working capital.
“Run-to-failure” is a working-capital tax and a downtime lottery.
In a typical industrial manufacturing site, unplanned equipment downtime costs 5–10× a planned intervention on the same equipment. Even with a strong preventive-maintenance schedule, most sites hold months of spare-parts inventory “just in case” against unpredictable failure. Both problems evaporate when you can see the failure coming.
Signals it is you
Unplanned downtime above 5% of scheduled production hours
Spare-parts working capital above 8% of annual maintenance budget
Recurring “we didn't see that coming” failures on critical rotating equipment
Maintenance team dominated by reactive callouts
Every critical rotating asset gets a heartbeat monitor.
Vibration and acoustic sensors deployed on bearings, motors, gearboxes and pumps stream to a Colony ML model trained on both the client's own historical failure data and Colony's cross-client failure signature library. The model outputs a residual life estimate; work orders route automatically when the estimate crosses threshold.
Components
Component · 01
Wireless sensor pack
IP66, battery-swappable, vibration + acoustic on a single node.
Component · 02
Edge streaming layer
Local aggregation on industrial gateway with cellular fallback.
Component · 03
Residual-life ML model
Per-asset calibration on top of a cross-client failure signature library.
Component · 04
CMMS routing
Automatic work-order routing into Fiix, Maximo or SAP PM.
Component · 05
Spare-parts pre-staging
MRP triggered on prediction threshold — parts arrive just before intervention window.
What lives under the hood.
Sensor firmware
Bluetooth LE with LoRa fallback for line-of-sight-limited sites.
Ensemble ML model
CNN on vibration spectrograms + XGBoost on operational features.
CMMS connectors
Fiix, Maximo, SAP Plant Maintenance out of the box.
Spare-parts orchestration
Via existing MRP; no parallel inventory system required.
Reported weekly. Reconciled quarterly.
Unplanned downtime cut by more than half within one quarter. Maintenance team shifts from reactive callouts to root-cause engineering — the highest-leverage work available. Spare-parts working capital falls as parts are pre-staged only when required. Insurance premiums often reduce on renewal following demonstrated predictive-maintenance coverage.
Annual EBITDA
+£480k
Unplanned Downtime
−62%
Spare-Parts WC
−30%
Mean Time To Repair
−45%
From diagnostic to measurable impact.
Phase · 01
Wk 0–2
Diagnostic, asset criticality ranking, sensor placement plan.
Phase · 02
Wk 2–6
Sensor rollout on top 20% of assets (Pareto-first), model training on historical data.
Phase · 03
Wk 6–10
Model tuning, CMMS integration, first predicted-intervention cycle.
Phase · 04
Wk 10–12
Full asset coverage, spare-parts optimisation live.
Where else this travels well.
Every Colony playbook is engineered to be portable. The core intervention shape holds — components adapt to the client context.
Water & wastewater treatment
Energy generation & distribution
Cold-chain refrigeration
HVAC-heavy operations
Often deployed alongside this one.
Think this playbook fits your operation?
Three concise steps and a Colony operational specialist will respond within one working day with a bespoke margin-uplift projection scoped to your footprint.