In 1978, F. Stanley Nowlan and Howard F. Heap published research they had done for United Airlines under Department of Defense sponsorship. The finding — verified across thousands of aircraft components — reshaped every serious maintenance program on earth: only 11% of components benefit from a limit on operating age. The other 89% either fail at random or show no relationship between age and failure probability. For that 89%, fixed-interval overhauls do not prevent failures. They often cause additional failures by introducing infant mortality from the overhaul work itself. This finding was born in aviation, tested on airliners, and adopted first by the US military and commercial nuclear industry. It is the foundation of Reliability-Centered Maintenance and SAE JA1011. And yet, decades later, most airport maintenance programs still schedule ARFF pump overhauls, ramp light-bar replacements, GSE hydraulic services, and terminal HVAC PMs on the calendar — as if 89% of the assets were the 11%. The financial and operational cost is enormous: over-maintaining the 89% and under-maintaining the 11%, at the same time. Below is the working guide to why airport asset age fails as a reliability predictor, the six failure patterns Nowlan-Heap actually found, and the condition-based digital CMMS approach that replaces calendar-driven maintenance with data-driven reliability. Start free and shift one asset class from calendar to condition-based this week, or book a demo to see the P-F interval workflow mapped to your airport asset register.
Aviation · Nowlan-Heap · SAE JA1011 · Condition-Based Maintenance · 2026
Why Airport Asset Age Fails as a Reliability Predictor: Best CMMS 2026
The 1978 finding that reshaped airline maintenance and still drives airport reliability decisions today — 89% of assets do not respond to age-based intervention. The six classic failure patterns, the airport asset classes each pattern actually describes, and the condition-based CMMS approach that replaces calendar-driven waste with data-driven reliability.
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89%
of components fail randomly or without relation to age (Nowlan-Heap 1978)
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11%
of components actually benefit from age-based overhaul or replacement
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6
classic failure patterns (A–F) that describe every asset in the register
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40–70%
unplanned downtime reduction reported by facilities applying RCM properly
The Statistical Reality
The 89 / 11 Split That Reshaped Airline Maintenance
Before Nowlan and Heap, the operating assumption was universal: every complex component has a "right age" at which overhaul restores reliability. The data proved otherwise across thousands of aircraft parts. For 89% of components, calendar-based overhaul either did nothing to prevent failure or actively introduced failures. This is the working shape of that finding — the single most important reliability statistic in the maintenance world.
For the 89%
Fixed-interval overhaul does not improve reliability. It often makes it worse by introducing infant-mortality failures from the overhaul work itself. Condition-based or run-to-failure strategies produce better results at lower cost.
For the 11%
Time-based preventive maintenance genuinely works. Wear-out patterns are real, and scheduled replacement before end-of-useful-life prevents failure. This is where calendar-driven PM earns its place.
The maintenance error is not in choosing calendar PM — it is in choosing calendar PM for the 89% of assets that do not respond to it, while missing condition-based monitoring on the assets that would.
The Six Failure Patterns
Patterns A Through F — What Nowlan-Heap Actually Found
The six classic conditional-probability-of-failure curves are the working alphabet of maintenance strategy. Only three of the six (A, B, C) show any age relationship at all — and together they cover just 11% of components. The other three (D, E, F) describe 89% of assets, and calendar-driven PM does not help them. Below is the working gallery of the six patterns, what they look like, and what percentage of components typically fall into each.
Bathtub Curve
Infant mortality, then flat, then sharp wear-out at end of life. Classic simple mechanical components with clear wear surface.
Share: ~4% of components
Wear-Out
Flat failure rate followed by rising wear-out. The one Nowlan-Heap called the "traditional" pattern — but rarer than believed.
Share: ~2% of components
Gradual Rise
Steadily rising probability of failure from day one, with no distinct wear-out point. Fatigue-driven behaviour.
Share: ~5% of components
Rapid Rise, Then Flat
Low initial failure rising quickly to a constant level. Age-independent from that point onward — calendar PM does not help.
Share: ~7% of components
Constant Random
Flat failure rate across entire life. Failure is age-independent — most electronics, hydraulic components, and complex assemblies.
Share: ~14% of components
Infant Mortality Dominant
High early failure, then dropping to constant low. The largest single pattern — and where calendar overhauls hurt most by re-introducing infant mortality.
Share: ~68% of components
Age-related patterns (A, B, C): 11% combined — calendar PM works. Random / age-independent patterns (D, E, F): 89% combined — condition-based or run-to-failure produces better outcomes.
Applied to the Airport
Which Airport Asset Classes Fall Into Which Pattern
The theory earns its keep when applied to the real asset register. Below is the working mapping — the airport asset classes commonly under maintenance, the failure pattern each actually follows, and the maintenance strategy that produces the best reliability at the lowest cost.
| Airport Asset Class | Typical Pattern | Age Relationship? | Best Strategy |
|---|---|---|---|
| Runway pavement (surface course) | A / B | Yes — wear-out | Age-based rehab plus condition inspection |
| Airfield lighting fixtures (LED) | E / F | No — random / infant mortality | Failure-finding + run-to-failure |
| ARFF vehicle pumps | E | No — constant random | Quarterly test + vibration monitoring |
| GSE hydraulic components | F | No — infant mortality dominant | Oil analysis, condition-based |
| Fuel farm mechanical valves | C / E | Mixed | P-F interval monitoring |
| Terminal HVAC belts / filters | B | Yes — wear-out | Calendar PM at 80% of service life |
| Baggage handling motors | E | No — random | Vibration + thermal monitoring |
| Perimeter fence sensors / cameras | F | No — infant mortality dominant | Failure-finding + run-to-failure |
The Financial Case
Over-Maintain the 89%, Under-Maintain the 11% — at the Same Time
The cost of getting this wrong is not theoretical. Airports running calendar-only maintenance programs typically over-service the 89% (labour, parts, planned downtime, and infant-mortality failures introduced by the overhaul work itself) while missing condition-based monitoring on the 11% (unplanned failures, safety exposure, NOTAM events). Facilities applying RCM properly report 40–70% unplanned downtime reduction, 10–25% maintenance spend reduction, and asset lifespans extended 15–20% beyond design. Oxmaint's mobile CMMS runs the failure-pattern mapping, P-F interval scheduling, and condition-based monitoring as first-class workflows.
The Condition-Based Alternative
The P-F Interval — What Replaces Calendar PM for the 89%
For assets in Patterns D, E, and F, the useful concept is not age. It is the P-F interval — the time between when a potential failure becomes detectable (P) and when it becomes a functional failure (F). Condition monitoring detects P; scheduled inspection at less than half the P-F interval guarantees the failure is caught before it functionally occurs. This is the working operating model that replaces calendar-driven PM.
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P
Potential Failure Detectable
The first measurable indication that failure is developing — a vibration signature shift on a pump bearing, a temperature rise on a bearing housing, a slight drift in a fuel-farm valve response time.
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P-F
The Interval — Time to Act
The elapsed time between detectable warning and functional failure. Varies by asset — hours for some electronic components, weeks or months for mechanical wear. Inspection cadence must be less than half this interval to guarantee catch.
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F
Functional Failure
The asset can no longer perform its intended function. If condition monitoring worked, this point is never reached. The corrective action fires between P and F, on evidence, not on the calendar.
Built for Airports
How Oxmaint Runs Reliability-Centered Airport Maintenance
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Failure Pattern Tagging
Every Asset Classified A–F at Setup
Airport asset register enriched with Nowlan-Heap pattern classification per asset class. Calendar PM assigned only where the pattern actually justifies it — the 11%, not the whole register.
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P-F Interval Scheduling
Inspection Cadence Under Half the P-F Interval
Condition-monitoring inspections scheduled per asset at less than half the documented P-F interval — guaranteed catch of the potential failure before functional failure occurs.
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Sensor & IoT Ingestion
Vibration, Thermal, Oil Analysis Feeding the Same Record
Condition data from vibration sensors, thermal cameras, oil analysis, and fuel-farm SCADA all flow into the same asset record — one source of truth for the P-F trend.
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Auto Corrective
P Detection Fires the Work Order
When condition data crosses the P threshold, the corrective work order fires automatically with priority, owner, and required parts — hours or days before the functional failure, not after.
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Run-to-Failure Where Right
Consequence-Ranked Assets Left Alone
Low-consequence assets in Pattern F flagged for run-to-failure with sparing strategy — spending on those assets diverted to condition monitoring on the high-consequence ones.
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Reliability Reporting
MTBF, MTTR, and Consequence-Weighted Metrics
Reporting anchored to the RCM decision framework — mean time between failure, mean time to repair, and consequence-weighted risk scoring per asset class. Data replaces gut feel.
Measured Outcomes
What Airports Gain Retiring Age as the Predictor
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40–70%
Unplanned Downtime Reduction
Facilities applying RCM properly consistently report unplanned downtime cut by 40–70% within 12–24 months — because failures get caught between P and F, not after.
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10–25%
Maintenance Spend Reduction
Over-maintaining the 89% is where the waste sits. Retiring calendar PM on pattern-D/E/F assets and redeploying to condition monitoring on the 11% cuts spend by 10–25%.
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15–20%
Extended Asset Lifespan
RCM-managed asset lifespans typically extended 15–20% beyond design target — because they are no longer being disturbed by unnecessary calendar overhaul.
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Free Forever Plan to Start
Airport ops teams start on the free plan, pattern-classify one asset class (ARFF, GSE, or airfield lighting), prove the P-F workflow, and scale.
Frequently Asked
Age-vs-Reliability Questions
Where does the 89% figure actually come from?
The 1978 Nowlan-Heap study, performed by United Airlines under US Department of Defense sponsorship. Analysis of thousands of aircraft components revealed that only 11% followed a pattern where age-based replacement improved reliability; 89% either failed randomly or showed no relationship between age and failure probability. The methodology became the foundation of RCM, adopted by military, nuclear, and — increasingly — infrastructure sectors. Start free and apply pattern classification to your airport asset register today.
Does the 89% figure apply to every industry?
No — and this is important. The 89% comes from aircraft component studies. Other sectors — mining, heavy haul, some rotating equipment — show different distributions with higher age-related failure fractions. For airport assets, which are mostly complex-electronic, hydraulic, and rotating equipment, the aviation-anchored 89% is genuinely applicable. The right answer is per-asset-class pattern classification, not blanket rules.
What is a P-F interval and how is it measured?
The elapsed time between when a potential failure first becomes detectable (P) and when the asset can no longer perform its intended function (F). Measured through condition monitoring history — vibration trend on a pump bearing, temperature drift on a motor housing, oil-analysis particle count on a hydraulic system. Inspection cadence must be less than half the P-F interval to guarantee catch. Book a demo to see P-F interval scheduling in action.
Is run-to-failure ever the right strategy for an airport?
Yes, for low-consequence assets in Pattern F where a spare is readily available. Perimeter camera lens fixtures, some ramp lighting, non-critical office HVAC. The maintenance dollar saved by not over-servicing these gets redeployed to condition monitoring on high-consequence assets. RCM does not oppose PM — it forces the consequence question before task selection.
Is there a free plan to pattern-classify one asset class first?
Yes. Oxmaint offers a free forever plan — enough to run Nowlan-Heap pattern classification on one airport asset class (typically ARFF, airfield lighting, or GSE), configure P-F interval scheduling, and prove the reliability gain before rolling out to the full register. Sign up for the free plan and pattern-classify one asset class today.
Classify · Monitor · Detect P · Act Before F
Age Is the Wrong Question. What the Data Shows Is the Right One.
Nowlan and Heap resolved this in 1978. Only 11% of airport components benefit from calendar-based overhaul. The other 89% need condition monitoring and P-F interval scheduling — not another arbitrary service interval. Oxmaint runs the full reliability-centered airport maintenance program: pattern classification per asset, P-F scheduling per component, sensor and IoT ingestion, and consequence-weighted reporting. Retire age as the predictor. Use the evidence.
