MTBF, MTTR and Weibull: reading reliability metrics correctly
Compute MTBF, MTTR and availability from a hypothetical record, then see why MTBF is not a lifetime and how the Weibull shape parameter β bears on scheduled replacement.
Beta, the Weibull shape parameter, shows how failure rate changes with age. Below 1 points to early-life failures, near 1 to random failures, and above 1 to wear-out. A scheduled replacement age only makes sense when beta is above 1, because random failures are not reduced by replacing parts on time.
The Weibull distribution describes time to failure with two parameters. The shape parameter, beta, sets the form of the failure-rate curve. When beta is below 1, the risk falls with age, a pattern typical of early-life failures such as commissioning faults or manufacturing defects. When beta is close to 1, the risk stays roughly constant and failures look random. When beta is above 1, the risk rises with age, which is the pattern of wear-out. The scale parameter, eta, is the characteristic life: the age by which about 63.2 percent of units have failed. Read beta and eta together, because eta alone says nothing about the shape of the failure pattern.
Suppose a bearing type has eta = 5 000 hours. Compare the chance of failing by 2 500 hours under two shapes.
The wear-out shape gives a lower chance of failing early, but risk rises steeply with age after that. The same eta with a different beta tells a different maintenance story, so beta must be estimated from data before any replacement age is chosen.
Beta drives the replacement policy. If it is above 1, a preventive replacement age can reduce wear-out failures, but the cost of early replacement must be weighed against the risk avoided. If it is near 1, scheduled replacement will not help, and the answer lies in condition monitoring or design changes. A frequent mistake is fitting beta from very few failures and treating the result as exact. Another is leaving out suspensions, the units still running at the time of analysis; excluding them can bias beta. For early-life failures with beta below 1, the fix is often in installation, commissioning or incoming quality control rather than in the replacement interval.
There is no fixed number. With few failures, the confidence interval on beta is wide and the estimate should be treated as preliminary. Suspensions, meaning units still running without failure, must be included, because leaving them out biases the fit. Reporting a confidence interval on beta is the honest way to present the result.
The exponential distribution assumes a constant failure rate. It is the special case of Weibull where beta equals 1. Weibull also describes early-life and wear-out patterns. Using an exponential model for wear-out data can make risk look flat when it is actually rising, which misleads replacement decisions.
Example numbers are hypothetical, not measured field results.