Predict & Prevent
How Maximo Predict Builds on Health to Anticipate Failure
After turning failure history into understanding, and data into asset health, the next question almost always follows: can we predict failures?
AI promises a lot. But prediction only creates value when people trust it. And trust does not come from algorithms, it comes from context.
Many organisations start their AI journey with a single ambition: “We want to predict failures.”
But prediction without understanding doesn’t create certainty, it creates doubt.
If users don’t know why a prediction is made, or what data it’s based on, they won’t act on it. At best, it becomes an interesting dashboard. At worst, it’s ignored.
Let’s return once more to the centrifugal cooling-water pump from the earlier posts.
Failure causes are structured in Maximo Manage. Health scores show gradual degradation. Vibration and temperature trends confirm abnormal behaviour.
At this point, the organisation already sees the problem forming.
This is where Maximo Predict starts to add value, not by replacing insight, but by amplifying it.
Maximo Predict typically works along two complementary paths.
The first path is prediction based on historical failure data. By analysing Corrective Maintenance Work Orders, failure codes, root causes, repair frequency and MTBF patterns, Predict learns how assets have failed in the past. This works especially well for repetitive assets and known failure modes.
It helps answer questions such as: which assets are statistically most likely to fail next, and which failure patterns tend to repeat under similar conditions. This approach is powerful, but only when failure data is consistent and meaningful.
The second path is prediction based on condition and sensor data. Here, Predict learns from vibration, temperature, pressure, runtime and other condition indicators, combined with degradation trends derived from Health.
Instead of asking what failed before, this approach asks what behaviour usually precedes a failure. This is where early detection becomes possible, long before thresholds are crossed.
Many organisations try to skip straight to AI. Without structured failure data, historical models become unreliable. Without health context, sensor data turns into noise.
Prediction only works when failure causes are understood, degradation patterns are visible, and asset context is clear. AI does not create certainty on its own. It builds on certainty that already exists.
The real value of Predict is not accuracy, it is prioritisation. Predict does not tell you what will fail. It tells you where to look first.
A planner sees rising failure probability, declining health, and known historical failure behaviour. That combination enables confident, explainable decisions: planning maintenance instead of reacting, intervening before performance drops, and avoiding both panic and complacency.
Trust in AI comes from transparency, not precision alone.
Prediction is not the goal. Confident decision-making is.
When AI is grounded in failure history and health insight, prevention becomes realistic rather than speculative.
In the next post, the focus shifts from prediction to practice: how reliability strategies turn insight into repeatable, scalable ways of working.
Published so far:
Up next in this series:
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Reliability Strategies that Scale – From FMEA to Actionable Reliability
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From Certainty to Trust – How AIP Supports Confident Decisions
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The Culture of Trust – Where Technology Meets People