Data-Driven Prognostics Incorporating Environmental Factors for Aircraft Maintenance

Marie Bieber, Wim J.C. Verhagen, Bruno F. Santos

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


SUMMARY & CONCLUSIONSDuring flights aircraft continuously collect data regarding operations, health status and system condition. Data-driven approaches typically applied to system specific sensor data provide a way to predict failures of aircraft systems. However, it is believed that some systems deteriorate faster when subjected to particular environmental conditions, such as humidity or dust. In this study, we consider an aircraft system which is suspected to experience degradation due to humidity during ground operations. We apply a Random Forest approach to sensor data only and a combination of sensor data and environmental data from airports to estimate the system’s remaining useful life. To our knowledge this is the first paper addressing the problem of integrating environmental data in prognostics for aircraft systems using raw sensor data. The method is validated on a data set provided by an airline that includes the per-second sensor data of 11 different sensors for roughly 12,300 flights, as well as 15 removals. Meteorological data for airports worldwide is obtained from the Meteorological Aerodrome Reports database. The results show that incorporating environmental data in prognostics has a potential towards more accurate prediction models.
Original languageEnglish
Title of host publication2021 Annual Reliability and Maintainability Symposium (RAMS)
Subtitle of host publicationProceedings
Number of pages6
ISBN (Electronic)978-1-7281-8017-5
ISBN (Print)978-1-7281-8018-2
Publication statusPublished - 2021
Event2021 Annual Reliability and Maintainability Symposium (RAMS) - Orlando, United States
Duration: 24 May 202127 May 2021


Conference2021 Annual Reliability and Maintainability Symposium (RAMS)
CountryUnited States


  • condition-based maintenance
  • prognostics
  • aircraft maintenance
  • RUL
  • environmental data


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