AiVibeQuality, Reliability & Maintenance
Bathtub Curve
The bathtub curve is a graph of failure rate against age that shows three phases: a decreasing failure rate in early life, a roughly constant failure rate during useful life, and an increasing failure rate as items wear out.
The first phase, often called infant mortality, reflects failures from manufacturing defects, poor installation or weak components, which decline as defective items fail and are removed. The middle phase has a low, approximately constant failure rate caused by random events such as overloads or external damage. The final phase shows a rising failure rate as wear, fatigue, corrosion and other ageing mechanisms dominate. In Weibull terms, the three phases correspond to shape parameters below one, equal to one and above one.
The curve is used to explain the logic of burn-in and commissioning tests, which aim to remove early failures before delivery, and of preventive replacement, which aims to replace items before wear-out. It is widely used in electronics reliability, where burn-in screening is common, and in teaching reliability and maintenance concepts.
Real equipment often does not follow the bathtub shape. The Nowlan and Heap study of civil aircraft components identified six different failure patterns and found that many items show no wear-out zone, a key reason why reliability-centred maintenance does not assume that scheduled overhaul improves reliability. Failure patterns should therefore be established from data, for example with Weibull analysis, rather than assumed. Maintenance itself can introduce early-life failures after intrusive work.
Key points
- Three phases: early-life failures, random failures and wear-out.
- Corresponds to Weibull shape parameters below, equal to and above one.
- Explains burn-in testing and preventive replacement.
- Many real items do not follow the bathtub shape.
Where AiVibe comes in
AiAmbA AI Factory use cases include predictive maintenance and visual inspection on textile, automotive and electronics lines. AiAmbA IoT normalises industrial signals from protocols such as OPC UA and MQTT, with driver availability confirmed per installation.
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