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Most of the real world problems pose practical challenges for making decisions primarily due to availability of limited data. Quantification of risk and assessment of structural reliability becomes difficult in such scenarios. Techniques for performing safety analysis for such problems are discussed in this paper. While complete characterization of a system behavior may be difficult with limited data of its response, statistical models based on extreme value theory provide the basis for making decisions with reasonable confidence. The same may not be true, however, for such structures early in their design cycle due to limited experience of their performance. In such cases response surface methodology can be very useful in determination of risk and suitably making modifications to the design to improve the reliability of the component or system. Applications of these methods for some real world scenarios are demonstrated.

The exponential increase in the number of aircrafts and air travelers has triggered new innovations which aim to make airline services more reliable and consumer friendly. Quick and efficient maintenance actions with minimum downtime are the need of the hour. Areas that have a large potential for improvement in this regard are the real time use of diagnostic data, filtering/elimination of nuisance faults and machine learning capabilities with respect to maintenance actions. Although, numerous LRUs installed on the aircraft generate massive amounts of diagnostic data to detect any possible issue or LRU failure, it is seldom used in real time. The turnaround time for LRU maintenance can be greatly reduced if the results of the diagnostics conducted during LRU normal operation is relayed to ground stations in real-time. This enables the maintenance engineers to plan ahead and initiate maintenance actions well before the aircraft lands and becomes available for maintenance.