How do you build a shutdown scope from historian data?
Shutdown scoping from historian data means pulling years of process and equipment history for each candidate asset, measuring its degradation rate, and projecting whether it will survive to the next maintenance window. Items with a clear slope toward failure go on the scope, flat trends on interval-only work are deferred, and ambiguous signals are fixed before anyone decides.
A major turnaround is the largest single maintenance event a plant runs, and the scope that drives it is often built from memory, wish lists and whoever argued loudest in the planning meeting. Someone remembers a pump that sounded rough in March. A superintendent wants a valve replaced because it failed last time. A vendor recommends overhauling an exchanger on a fixed interval whether it needs it or not.
This is rarely a planning discipline problem. The planners are usually excellent. It is a data problem: the evidence that shows which equipment is degrading, and how fast, sits in the historian and almost none of it reaches the scope meeting.
This article is for maintenance and reliability managers, turnaround planners, rotating and fixed equipment engineers, and the operations leaders who sign off the shutdown budget at mining, processing, energy and manufacturing sites.
Key takeaways
- Scope, more than execution speed, drives turnaround cost: low-value work planned in, and real degradation found too late.
- The historian already holds the evidence: pump performance drift, exchanger fouling curves, valve behaviour.
- Three things block its use: access, tag context and time.
- AI is useful for the first pass across thousands of trends. A reliability engineer decides what goes on the scope.
- Start 9 to 12 months before the window with one or two equipment classes.
Why is shutdown scoping the lever that matters?
Turnaround benchmarking, including studies published by specialist firm AP-Networks, has long linked cost and schedule overruns to scope: too much low-value work planned in, and too much unplanned work discovered once the plant is open and reacting is expensive.
Both failures have the same root. Scope was decided without evidence. Work went on the list that the data would have shown was not needed, and work stayed off the list that the data would have flagged months earlier.
Every item you add costs labour, materials and critical-path time. Every real degradation you miss becomes a discovery item at the worst moment, or a trip after startup. Getting scope right attacks both sides of the overrun at once.
| Memory and wish list | Historian evidence | Impact | |
|---|---|---|---|
| What drives an item onto the list | Who argued for it | Measured degradation rate | Objective |
| Fixed-interval overhauls | Done regardless of condition | Deferred when trends are flat | Avoided waste |
| Hidden degradation | Found as discovery work mid-event | Flagged months ahead | Planned, not reactive |
| Budget defensibility | Hard to justify to the board | Traceable to evidence | Auditable |
| Post-startup trips | Common | Reduced by catching the real risks | Lower |
What does the historian already know?
The data you need is the ordinary process and equipment history you already record, read for a different question.
Pumps. A wearing pump shows it long before it fails. The differential it produces for a given speed drifts down, the motor current pattern changes, vibration overalls climb, and the gap between duty and standby performance widens. None of that is a single alarm. It is a slope in the trend, visible over months.
Heat exchangers. Fouling follows a curve you can measure. The approach temperature creeps up and the heat transfer coefficient, calculated from existing flow and temperature tags, declines toward the point where cleaning pays for itself.
Control valves. A valve that is passing or sticking reveals itself in the relationship between its output and the process variable it should move.
The principle behind all of it is the P-F interval: the window between the point where a failure becomes detectable and the point where it becomes functional. The historian is where detectability lives. For every candidate item, ask what the trend says and whether the degradation rate means it will or will not survive to the next window.
- Pull historyYears of tags for the candidate asset
- Measure the slopeDegradation rate, not a snapshot
- Project forwardWill it survive to the next window?
- DecideScope in, defer, or monitor
Why does the data stay unused?
If the evidence is already recorded, why is scope still built from memory? Three practical barriers, and none is a shortage of data.
Access. The reliability engineer who should read the trends often cannot reach the historian easily, or sees one tag at a time through an interface built for operators. Getting equipment history into a tool where it can be worked at scale, without a second historian, is covered in our guide to getting historian data into Power BI.
Context. A trend is only diagnostic if you know what the tag means, in what units, measured where. A degradation slope on a mislabelled tag invites a confident wrong conclusion. That is why tag naming and OT data standards decide whether this analysis is possible, and why reusable asset templates matter: they let you ask one question across a whole class of equipment.
Time. Scope is built under deadline, and reading trends asset by asset for a large plant is slow by hand. This is where analytics and AI earn their place, doing the first pass across the estate and surfacing the assets that warrant an engineer's judgement.
What does the trend data show?
- Clear degradation slope toward failure before next windowScope it in
- Flat trend, interval-driven onlyDefer and monitor
- Noisy signal, meaning unclearFix the tag context first
- Degrading but survives to next windowPlan for the window after
Where does AI fit in shutdown scoping, and where does judgement stay?
The useful version of the AI story here is narrow. A model is good at the first pass: reading thousands of equipment trends, flagging the ones whose degradation rate is changing, and ranking candidates so a limited engineering team spends its attention where it matters. It catches slow drift that a person scanning one screen at a time will miss, and it can combine several weak signals (differential, current, vibration, temperature) into one view of a machine.
It does not make the call. Whether a flagged exchanger is scoped this window or the next weighs production risk, parts lead time, critical-path impact and safety. That belongs to the reliability engineer and the turnaround manager. AI narrows the field; a person decides the scope.
An AI system reading operational data needs tag context and a read-only boundary far more than it needs a particular model. We build these to run on your infrastructure in Australia, with no write access to control systems.
This connects to inspection too. Risk-based inspection under API 580 directs inspection effort by likelihood and consequence of failure rather than by calendar, and historian-derived degradation rates are exactly the likelihood evidence that discipline runs on.
- T minus 12 monthsPull the baselineAssemble history for every candidate asset class
- T minus 9 monthsFirst analytic passRank assets by degradation rate across the estate
- T minus 6 monthsEngineering reviewReliability team judges the flagged shortlist
- T minus 3 monthsFreeze the scopeEvidence-backed list, discovery risk reduced
Where does the return come from?
The payoff comes from two directions. You remove low-value work that would have happened on a calendar regardless of condition, which gives back labour, materials and critical-path days. And you catch real degradation early enough to plan for it, turning discovery work and post-startup trips into ordinary planned items. The list below shows the shape of that return for a hypothetical plant, not a claimed result.
- Fixed-interval overhauls deferred on flat trendsRemoved from scope
- Degradation caught before the window, not duringPlanned, not discovery
- Critical-path days freed by cutting low-value workSchedule headroom
- Budget tied to evidence a board can auditDefensible sign-off
For the people who approve the spend, this is the difference between a negotiated wish list and a traceable argument. "The trend shows this machine will not survive to the next window" holds up in front of an executive where "we usually do it" does not. The same evidence-first posture applies to AI and operational decisions at board level.
How do you start before the next window?
You do not need a platform or a large programme to begin.
- Pick the next turnaround with enough runway, ideally 9 to 12 months.
- Choose one or two equipment classes where failure hurts most.
- Assemble their history now and measure degradation rates, not snapshots.
- Bring the ranked shortlist to the reliability team as input to the scope, alongside the usual sources.
- Record which evidence-led items earned their place and which wish-list items the data did not support.
One window of that shows whether the scope got better and builds the case for doing it systematically. The same foundation (accessible history, consistent naming, a trusted asset model) sits under production reconciliation between SCADA and the ERP and alarm rationalisation from historian evidence, so we treat it as one programme.
How Solve8 can help
Our consultants and engineers have worked on historian and plant performance systems, including engagements at BHP, Rio Tinto and Senex Energy across our consultants' careers. We help operations teams get equipment history into a usable shape and build the first-pass analytics behind an evidence-led scope. See our mining, resources and manufacturing work and data, BI and analytics service, or talk to us while there is still runway to the window.
