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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.