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From prescribed maintenance to evidence-based maintenance.
Cenosco IMS tells you what matters and how it should be managed. UReason tells you what is happening to it right now. Together they continuously connect your RCM/RBI strategy with real asset condition.
The strategy is mature. The feedback loop is missing.
Most reliability programmes already know what should be maintained, why, and with what level of risk tolerance. That knowledge lives in a mature RCM/RBI strategy. The gap is operational: the strategy is set periodically, and then stays static while the equipment keeps changing.
This integration closes that gap. It is not “adding predictive maintenance to IMS” — it closes the loop between your risk-based maintenance strategy and what the equipment is actually doing in operation.
For existing IMS customers that is especially compelling. You have already invested in RCM/RBI to establish a defensible maintenance strategy for your installed base. This integration makes that strategy dynamic rather than periodic — without asking you to rebuild anything.
“Cenosco IMS tells you what matters and how it should be managed. UReason tells you what is happening to it right now.”
Complementary, not competing.
Each product answers a different half of every maintenance decision.
Strategic & risk context
The system of record for integrity and reliability.
- —Why maintain?
- —What can fail?
- —What are the consequences?
- —How critical is the failure?
- —What maintenance strategy is required?
- —What is the required interval?
- —What is the audit trail?
Operational & condition context
Continuous diagnosis of the installed base.
- —What is happening now?
- —Is it beginning to fail?
- —What degradation mechanism is developing?
- —How fast is the condition changing?
- —What action should be considered now?
- —Can the interval be safely extended or deferred?
- —What does the live data tell us?
A continuous feedback loop on top of the strategy you already have.
The classic IMS workflow runs analysis → task → interval → schedule → execute → record → periodic reassessment. The integration adds the operational half.
The result: the right maintenance action, for the right failure mechanism, on the right asset, at the right time.
A known failure mode, now supported by live evidence.
An RCM analysis manages anticipated failure modes. UReason provides continuous evidence that one of them is — or isn’t — actually developing.
Take a centrifugal pump. The IMS RCM analysis defines a failure mode of bearing degradation, a strategy of condition monitoring, and a criticality of high.
UReason monitors the pump and detects a developing condition consistent with that failure mode. Instead of firing another isolated alarm, the integrated system reports an RCM failure mode that has become observable in real life.
The user is no longer looking at “an AI prediction.” They are looking at a known, high-consequence failure mode with evidence attached — and a recommended action already tied to the IMS task and CMMS work order.
Pump P-101
DeterioratingIMS should not become another alarm-management system.
A deliberate design choice: UReason does not dump hundreds of raw alerts into IMS. It feeds interpreted condition events, mapped to the IMS asset, failure-mode and strategy structure.
Something is changing
Vibration ↑, temperature ↑, efficiency ↓ on the monitored pump.
Probable mechanism
Bearing degradation, 87% confidence — not a threshold breach.
Does it matter?
Criticality A, consequence class production + safety, existing CBM strategy.
One recommendation
Inspect P-101 bearing during the next maintenance window.
Site, priorities, and the reason behind every recommendation.
The whole installed base at a glance
Everything prioritised by IMS criticality combined with UReason condition — never condition alone.
A slightly degraded low-criticality pump should not outrank a moderately deteriorating high-criticality pump.
Intelligent maintenance priorities
The most valuable screen for an RCM practitioner: condition, criticality, failure mechanism and recommended action fused into one ranked list.
From “what maintenance tasks are due?” to “which assets actually need my attention?”
Explainable predictive maintenance
Every recommendation is traceable into the integrity and reliability model — the UReason observation, the IMS criticality and RCM context, and the integrated recommendation, side by side.
Explainability is decisive for the reliability and integrity engineers who live in IMS.
Maintenance priority = consequence/risk × condition × degradation trajectory × action urgency.
| Asset | Condition | IMS criticality | Failure mechanism | Action |
|---|---|---|---|---|
| P-101 | Deteriorating | High | Bearing degradation | Inspect |
| HX-204 | Degrading | High | Fouling | Clean / inspect |
| M-302 | Degrading | Medium | Motor / VFD issue | Diagnose |
| CV-118 | Deteriorating | High | Stiction | Service |
| GB-401 | Abnormal | Medium | Lubrication / bearing | Monitor |
Is our RCM strategy actually working?
Most RCM programmes live with a gap between what we thought would happen and what actually happens. UReason supplies empirical evidence from the installed base to close it.
What IMS can finally report
Predicted failure mechanism and times detected · false positives, actual failures, and failures despite intervention · detection lead time · condition-monitoring effectiveness · actual degradation rate and operating context.
That lets the reliability engineer ask, with evidence: should this PM task remain? Can the interval be extended? Should this asset move from time-based to condition-based maintenance?
It aligns directly with a core IMS objective — rationalising maintenance intervals based on actual degradation rather than generic assumptions.
A trigger mechanism, not an automatic inspection order.
RBI and condition monitoring are not the same thing, and the integration respects that distinction. For pressure equipment and heat exchangers, RBI asks what degradation mechanisms are credible, what are their consequences, and how should we inspect? UReason adds what is the equipment telling us right now?
IMS remains the system of record where the formal RBI decision, justification and audit trail reside. The predictive signal triggers or informs an integrity decision — it does not replace it.
Exchanger HX-204
RBI triggerEvery decision in the integrated system rests on four questions.
Something is changing
Pump efficiency deteriorating.
We believe this is happening
Probable cavitation, bearing degradation or stiction.
This matters, because this asset is important
Criticality, consequence and integrity context.
Therefore, someone should do something
Inspect · lubricate · clean · calibrate · repair · investigate · monitor · defer · reassess RCM · trigger RBI.
The action layer matters most: it turns condition intelligence into a prioritised decision, and connects directly to CMMS execution.
Your site is already modelled in IMS. Let’s make it observable.
There is no need to build a second asset hierarchy for predictive maintenance.
What should we manage?
What is happening?
What should we do now?
Precision about the claim matters.
- Dynamic Reliability Management
- Risk-Based Predictive Maintenance
- Closed-Loop Asset Reliability
It turns your IMS RCM and RBI strategies into living strategies that continuously learn from the real condition of your installed base.
AI replaces RCM
RCM determines the logic of how failures should be managed; predictive analytics provides evidence about what is happening. The two reinforce each other.
Predictive replaces preventive
Condition evidence is used to determine where preventive maintenance is still justified, where it can be optimised, and where condition-based maintenance can take over.
UReason writes work orders automatically
That can be an outcome, but it is not the headline. The higher-value story is the right action, for the right failure mechanism, on the right asset, at the right time. The work order is simply the execution mechanism.
One site. Five to ten real assets. Six steps.
The most convincing demonstration starts not with architecture or APIs, but with your own pumps, motors, gearboxes, control valves and heat exchangers.
That final step is what makes this strategic — not just another predictive-maintenance dashboard.
Not another application to run.
The missing operational feedback loop.
Adopt the integration to make the RCM/RBI investment you already made substantially more valuable — so teams act on the risks that actually require attention, and improve the strategy with evidence from the installed base.
