Having the ability to identify potential repeating defects on an aircraft can greatly contribute to increasing the overall fleet availability of any airline. However, the ability to identify such repeating defects on individual aircraft is also dependant on several variables in the aircraft complaint registration process being properly manager. How can heads of engineering ensure that outcomes of repeated defect analysis provide meaningful results? 

Repeated defect analysis revolves around analysing the technical complaints information of each individual aircraft in the fleet and determine if specific complaints keep reappearing on individual aircraft or across a certain fleet of the same aircraft type. Being able to identify such potential complaints can assist in preventing unscheduled maintenance downtime of aircraft and subsequently increase the overall aircraft /fleet availability.

Similar as to aircraft systems reliability monitoring, the information used for repeated defect analysis are the actual technical complaints raised by flight crew (Pirep), maintenance engineers (Marep) or cabin attendants (cabin defects). The difference being that where system reliability monitoring looks at specific trend over time in aircraft systems, repeated defect analysis looks at specific complaints themselves re-appearing over a given period on each individual aircraft. Such repeated defects could be an identification of an impact by operational usage of the aircraft, weather conditions, negative effects of a modification or potential inadequate maintenance practices or component reliability.

Within Avilytics, the repeated defect analysis allows to identify any potential repeating defects on either and individual aircraft or across multiple aircraft in the same fleet.

Repeated Defect Analysis Avilytics









For each individual aircraft the system calculates an Upper Control Limit (UCL) of number of complaints per flight hour of the aircraft. Whenever the actual ratio of number of complaints in any given ATA chapter per flight hour, exceed the calculated UCL of that ATA chapter the system triggers a notification that can be used as an initiator to start further technical assessments on that specific aircraft registration and the system / complaint identified.

Additional cognitive visualizations allow to spot any time driven trends in repeated defects per ATA chapter on either an individual aircraft level or full fleet level.

How to ensure data quality in usage of ATA aircraft system codes?

Data quality in the registration of technical complaints is important in order to be able to make adequate calculations and identifications of potential repeating defects. Having each technical complaint description recorded against their correct 4-digit (main system – subsystem) ATA code will greatly help in identifying such repeating defects. However, memorizing all 99 main systems and subsequent subsystems also seems an impossible task to ask from anyone.

Tip:        Automated RPA routines to verify complaint descriptions and corresponding ATA chapters will assist in increasing data quality

Validating each technical complaint entry can be a tedious and lengthy task. Using robotic process automation to perform these validations automatically will greatly help in improving data quality in an automated way. Subsequently also greatly improving the accuracy in repeated defect identification. Key in the creation of such an ATA chapter validation RPA bot, is the creation of a widespread ATA & complaint description keywords matrix in order to match complaint descriptions with their corresponding 4-digit ATA chapter code.

Do you have any questions?

Feel free to contact us or give us a call on +31 20 8200 7600. We would be happy to help you further. 


AVILYTICS is a fully out-of-the-box aircraft reliability management solution that focuses on providing insights in technical reliability, upcoming potential technical failures as well as organizational efficiency analytics. It combines the traditional scope of aircraft and fleet reliability management with advanced techniques from predictive analytics to also build AOG risk profiles of aircraft, identify aircraft based reoccurring defects and measure organization performance. A full holistic approach to using data in order to increase aircraft availability and fleet performance.