Use of textual elements to improve reliability prediction for aircraft component behavior

Wim J.C. Verhagen*, Thijs Oudkerk

*Corresponding author for this work

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


Unplanned maintenance is a costly factor in aircraft operations. Predictive maintenance models aim to provide greater insight into future component and system behaviour. In the state of the art, a variety of statistical models and machine learning techniques, amongst others, are used to estimate component remaining useful life. These approaches commonly leverage technical information, such as sensor data. However, the use of data and techniques from other domains is not prevalent. One such example is the application of natural language processing to incorporate textual information, e.g. derived from pilot complaint data. In other words, does the presence and specific content of pilot complaints have potential to improve the predictability of component removals? In this research, data integration and processing from multiple disciplines are combined to address this question. Relevant words from pilot complaints are identified using a term frequency-inverse document frequency (TF-IDF) numerical analysis, after which the most relevant words are used as covariates in a proportional hazards model. Left truncation and right censoring is applied to limit the time-invariant nature of these covariates. The results in the form of hazard ratios indicate a hazard increase of several orders of magnitude with respect to baseline hazard, pointing towards potential value of including these words as predictive parameters.

Original languageEnglish
Title of host publicationTransdisciplinary Engineering for Complex Socio-Technical Systems - Real-Life Applications - Proceedings of the 27th ISTE International Conference on Transdisciplinary Engineering
EditorsJerzy Pokojski, Maciej Gil, Linda Newnes, Josip Stjepandic, Nel Wognum
PublisherIOS Press
Number of pages10
ISBN (Electronic)9781614994398
Publication statusPublished - 2020
Externally publishedYes
Event27th ISTE International Conference on Transdisciplinary Engineering, TE 2020 - Virtual, Online, Poland
Duration: 1 Jul 202010 Jul 2020

Publication series

NameAdvances in Transdisciplinary Engineering


Conference27th ISTE International Conference on Transdisciplinary Engineering, TE 2020
CityVirtual, Online


  • Natural language processing
  • Predictive Maintenance
  • Proportional Hazard Models


Dive into the research topics of 'Use of textual elements to improve reliability prediction for aircraft component behavior'. Together they form a unique fingerprint.

Cite this