Framework A — Diagnosis
How the failure behaves over time
Before deciding what to do about a failure, you need to know how it happens over time. Framework A answers exactly that: does the failure mode age (wear-out), is it random, or is it most likely right after an intervention (infant mortality)?
The six Nowlan & Heap patterns
The classic Nowlan & Heap study (United Airlines, 1978) measured conditional probability of failure against age for thousands of items and found six patterns — with the result that redefined maintenance: only ~11% of items (patterns A, B and C) show a clear wear-out zone that justifies age-based intervention. The other ~89% are dominated by randomness or infant mortality — where scheduled overhaul does not help and can make things worse.
| Pattern | Shape | Share of items |
|---|---|---|
| A | Bathtub (infant + constant + wear-out) | 4% |
| B | Classic wear-out (defined wear zone) | 2% |
| C | Gradual increase, no defined zone | 5% |
| D | Low at start, then constant | 7% |
| E | Pure random (constant rate) | 14% |
| F | Infant mortality, then constant | 68% |
The β ruler (Weibull)
The shape parameter β of the Weibull distribution turns the pattern into a number: β < 1 = infant mortality; β ≈ 1 = random (constant rate); β > 1 = wear-out. Every failure-mode note in the collection carries this reading in the purple Framework A panel — with the editorial category and the typical β cited in the literature.
Explore it in the Weibull calculator →
What the diagnosis feeds
The output of Framework A is the input of Framework B: only after knowing whether the failure gives a measurable warning (P-F interval), whether it ages and whether operations can see it does choosing the task make sense.