Quality, Reliability & Maintenance
Failure Rate
Failure rate is the frequency with which items fail, expressed as failures per unit of operating time or usage; in reliability engineering it often means the hazard rate, the instantaneous rate of failure given survival up to that point.
A simple failure rate is the number of failures divided by the total operating time of a population, for example failures per million hours. In reliability theory, the hazard rate describes how the conditional likelihood of failure changes with age. A constant hazard rate corresponds to the exponential distribution, in which failures are random and the item does not age; a decreasing rate indicates early-life failures, and an increasing rate indicates wear-out. Electronic component failure rates are often expressed in FIT, failures in time, where one FIT is one failure per billion device-hours.
Failure rates are used in reliability prediction, system modelling with reliability block diagrams and fault trees, spare parts calculations, warranty forecasting and functional safety analysis. Component data come from manufacturers, field records and published reliability handbooks and databases. Maintenance teams track failure frequency per asset or per failure mode to identify bad actors and evaluate improvements.
Published or predicted failure rates assume particular operating conditions and can differ substantially from field experience. Assuming a constant failure rate simplifies calculations but is inappropriate for components dominated by wear, such as bearings, belts and seals, for which Weibull analysis is more suitable. Consistent failure definitions and accurate operating time records are essential for meaningful rates.
Key points
- Failures per unit of operating time or usage.
- The hazard rate is the instantaneous failure rate given survival so far.
- A constant failure rate corresponds to the exponential distribution.
- One FIT is one failure per billion device-hours.
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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Related terms
- Mean Time Between Failures (MTBF)Quality, Reliability & Maintenance
- Bathtub CurveQuality, Reliability & Maintenance
- Weibull AnalysisQuality, Reliability & Maintenance
- Mean Time to Failure (MTTF)Quality, Reliability & Maintenance
- Fault Tree Analysis (FTA)Quality, Reliability & Maintenance