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Aircraft Crash Survival Investigation and AnalysisThe 5-day course entails a detailed analysis of the aircraft accident environment with particular emphasis on survivability factors. Contact UsRobertson Aviation Safety Center II Embry-Riddle Aeronautical University 3700 Willow Creek Rd.
Reliability specialists often describe the lifetime of a population of products using a graphical representation called the bathtub curve.
The bathtub curve, displayed in Figure 1 above, does not depict the failure rate of a single item, but describes the relative failure rate of an entire population of products over time. Failures during infant mortality are highly undesirable and are always caused by defects and blunders: material defects, design blunders, errors in assembly, etc.
Also note that the actual time periods for these three characteristic failure distributions can vary greatly. Accelerated Life Test) or HAST (Highly Accelerated Stress Test) and should be applied, with increasing stress levels as needed, until failures are precipitated. The first approach, eliminating root causes, is generally the best approach and can significantly reduce infant mortalities. 100% burn-in processes in place and keep using them, addressing the symptoms rather than identifying the root causes. The Weibull distribution is a very flexible life distribution model that can be used to characterize failure distributions in all three phases of the bathtub curve.


This plot shows ten years (87,600 hours) of time on the x-axis with failure rate on the y-axis. This plot shows the distribution for a beta value typical of complex, high-density integrated circuits (VLSI or Very Large Scale Integrated circuits). To see how burn-in can improve the reliability of high tech parts, we'll use a chart that looks somewhat like the failure rate vs.
Figure 3 shows that, of the failures that occur in the first 20 years (about 4%), most failures occur in the first year or so, just like we observed in the infant mortality example above.
Because there is a low level, constant failure rate, this plot shows failures continuing for a hundred years. We're not really interested in the failures much beyond ten years, so let's look at this same model for only the first ten years.
We see that the plot in Figure 4 looks like the early life and normal life portions of the bathtub curve, and in fact includes both distributions. Above, we see fourteen years of failure distribution for the original parts (not burned-in) and eleven years of expected failure distribution for parts that received three years of burn-in. Increased voltages (relative to normal operating levels) can provide even higher acceleration factors on many types of ICs. It's pretty easy to see that burn-in for two years would find ~2% failures, but operation for an additional two years would find another ~2%.


It explores factors and forces that cause injury and examines the injury-role played by impact forces and occupiable space compromises. A product manufacturer must assure that all specified materials are adequate to function through the intended product life. It is rare to have enough short-term and long-term failure information to actually model a population of products with a calibrated bathtub curve. For most products, this is not effective from a cost standpoint or from a reliability improvement standpoint.
Dots on this plot represent failure times typical of an infant mortality with Weibull beta = 0.2. Increased temperature (relative to normal operating temperatures) can provide an acceleration of tens of times (10x to 30x is typical). The students will examine crashworthiness and delethalization technologies and concepts with a focus on the best ways to protect occupants during a crash.



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