The Bathtub Curve: Reliability’s Three Failure Phases
One curve explains why brand-new equipment and worn-out equipment both fail more often than everything in between — and where your maintenance dollars actually belong.
What is the bathtub curve?
The bathtub curve is a graph of an asset’s failure rate over its lifetime, and it takes the shape of a bathtub: failures start high, drop to a low steady level, then climb again as the asset ages. Plot hazard rate on the vertical axis and time on the horizontal, and the line falls, flattens, and rises — three distinct regions that map to three different failure behaviors.
Reliability engineers use the curve as a mental model, not a literal forecast for every machine. It tells you that failures are not random across an asset’s whole life — the reason things break changes depending on how old the asset is, and your maintenance strategy should change with it.
The three phases of the bathtub curve
The curve splits an asset’s life into three phases, each driven by a different failure cause and each calling for a different response.
1. Infant mortality (early failures)
Failure rate is high at first but drops quickly. These “early-life” failures come from defects present before the asset ever ran a full shift — manufacturing flaws, bad components, incorrect installation, commissioning mistakes, or misapplication. Burn-in testing, thorough commissioning, and installation QA weed out weak units before they reach production.
2. Useful life (random failures)
Failure rate settles into a low, roughly constant band. Breakdowns here are random and unrelated to age — a stray voltage spike, contamination, an operator error, a sudden overload. Because you cannot predict which day a random failure lands, condition monitoring and quick response matter more than swapping parts on a calendar. This is the phase you want assets to spend most of their life in.
3. Wear-out (end-of-life failures)
Failure rate rises again as fatigue, corrosion, erosion, and general degradation accumulate. Bearings spall, belts stretch, seals harden, insulation breaks down. Here, age-based failures become predictable, and time- or usage-based replacement, overhaul, or planned retirement is the right move before an in-service failure takes the line down.
| Phase | Dominant failure cause | Maintenance response |
|---|---|---|
| Infant mortality | Defects, bad installation, commissioning errors, faulty parts | Burn-in testing, installation QA, tight commissioning, warranty tracking |
| Useful life | Random events — overloads, contamination, human error | Condition-based monitoring, fast work-order response, run-to-failure on low-criticality assets |
| Wear-out | Fatigue, corrosion, erosion, cumulative degradation | Time/usage-based replacement, overhaul, planned retirement |
What the curve means for your maintenance strategy
The bathtub curve’s biggest lesson is that no single maintenance approach fits an asset’s entire life. Aggressive time-based part swaps during the useful-life phase can actually introduce infant-mortality failures — every time you open a machine and install a new component, you reset that component to the high-failure start of its own little curve. That is exactly why reliability programs moved away from “replace everything on a schedule.”
A practical program layers the strategies to match the phase:
- Early life: catch defects before they ship to the floor with commissioning checks and burn-in, and track warranty claims to spot bad batches.
- Useful life: lean on condition-based maintenance and monitoring so you act on actual asset health, not the calendar.
- Wear-out: shift to planned replacement and overhaul as data shows the failure rate climbing.
Modern research (notably from the airline industry) found that only a minority of components follow a classic wear-out pattern — many show mostly random failure. That is the foundation of reliability-centered maintenance (RCM): choose the tactic that matches how each asset actually fails, rather than assuming everything wears out on a predictable schedule.
How a CMMS reveals where an asset sits on the curve
A CMMS turns the bathtub curve from a textbook diagram into a decision you can make about a specific pump or motor, because it stores the failure history the curve is built from. Every completed work order, failure code, and repair timestamp is a data point on that asset’s real curve.
With that history in one system, you can see the shape emerging:
- Reliability metrics. Trending MTBF, MTTR, and MTTF per asset shows whether time between failures is shrinking — the signal an asset is entering wear-out.
- Failure-code analysis. Tagging each work order with a failure mode reveals whether breakdowns are installation-related (infant mortality) or degradation-related (wear-out), and feeds a structured failure mode and effects analysis (FMEA).
- Condition triggers. eWorkOrders integrates with predictive-maintenance and condition-monitoring vendors such as AssetWatch, which auto-generate a work order the moment a sensor reading crosses threshold — so a rising failure rate creates action instead of a surprise.
Cluster of early failures on a new install? That points to a commissioning or supplier problem to fix at the source. Steadily rising failure rate on an aging asset? The data is telling you it has reached wear-out and it is time to plan replacement — before it fails in service.
See your assets’ real failure curves
eWorkOrders has kept maintenance operations running for 31 years at 99.99% uptime, for teams at McDonald’s, Burger King, and ASSA ABLOY — rated 4.9 on Capterra and 4.9 on G2. We configure the system to fit your assets in a single 90-minute-to-2-hour session, with unlimited-user pricing, so your failure history becomes strategy instead of scattered notes.
Frequently Asked Questions
What are the three phases of the bathtub curve?
Infant mortality (early failures from defects or bad installation, with a high but falling failure rate), useful life (a low, roughly constant rate driven by random events), and wear-out (a rising rate as fatigue, corrosion, and degradation accumulate).
Why is it called the bathtub curve?
When you plot failure rate against time, the line drops steeply, flattens across the middle, then rises at the end — a cross-section that looks like a bathtub. The name describes the shape of the graph, not the equipment.
Does every asset follow the bathtub curve?
No. It is a general model, and reliability research shows many components fail mostly at random rather than in a clear wear-out pattern. That is why reliability-centered maintenance matches the tactic to how each specific asset actually fails.
How does a CMMS help apply the bathtub curve?
A CMMS stores every work order, failure code, and repair time, so you can trend MTBF and failure modes per asset and see which phase it is in — then trigger condition-based work or planned replacement based on real data.
About the author: Janet Jaquis is a CMMS software specialist with over 8 years at eWorkOrders, where she develops educational content, technical guides, whitepapers, and implementation resources for maintenance management professionals. Her work covers preventive maintenance, work order management, asset reliability, inventory and spare parts, mobile maintenance, and CMMS implementation across manufacturing, healthcare, government, food and beverage, and facilities operations. Janet’s content is grounded in customer testimonials, case studies, industry research, and ongoing engagement with the eWorkOrders product team and customer base. Prior to eWorkOrders, she spent her career at AT&T in enterprise technology, working on the development and launch of AT&T WorldNet — one of the first major commercial internet services — and serving as Product Marketing Manager for AT&T WorldNet and AT&T Satellite Services. She holds a degree in Marketing and previously held PMP (Project Management Professional) certification.
