Print Email Facebook Twitter Machine Learning for Predictive Maintenance Title Machine Learning for Predictive Maintenance: A Boeing 747 Bleed Air Valves case study Author IJzermans, Erik (TU Delft Aerospace Engineering) Contributor Verhagen, W.J.C. (mentor) Curran, R. (mentor) Degree granting institution Delft University of Technology Programme Aerospace Engineering | Transport and Operations Date 2018-07-16 Abstract The newest generation of aircraft has seen a strong increase in sensor data generated on-board. The available data has the potential to indicate the health state of individual components based on which their maintenance requirements can be determined, a maintenance strategy called Condition Based Maintenance. Predictive Maintenance is a specific condition based maintenance strategy that aims to determine these requirements in advance by predicting failures from the sensor data. It has the potential to reduce costly unanticipated maintenance or unnecessarily conservative maintenance. In the short term it could add significant value for components that are currently subject to a reactive maintenance policy. In the long term it could potentially disrupt the traditional maintenance practice of periodic inspection. One of the main challenges in applying Predictive Maintenance in the aviation industry is translating the large amounts of sensor data into a reliable failure prediction, a process called prognostics.In this study, state-of-the-art machine learning, and specifically deep learning models, have been investigated for their potential for prognostics. A case study has been performed at KLM Royal Dutch Airlines on the Boeing 747 Bleed Air Valves, traditionally some of the most challenging components from a maintenance perspective. It has been shown that fully self-learning algorithms can be used for prognostics, enabling the implementation of one of the first real-life predictive maintenance implementations. Subject Machine LearningDeep LearningPredictive MaintenancePrognostics To reference this document use: http://resolver.tudelft.nl/uuid:27037d74-d49b-4dfe-bcad-ccf0a0bfd957 Embargo date 2023-07-16 Part of collection Student theses Document type master thesis Rights © 2018 Erik IJzermans Files PDF 02_07_18_Final_Thesis_IJz ... ersion.pdf 4.54 MB Close viewer /islandora/object/uuid:27037d74-d49b-4dfe-bcad-ccf0a0bfd957/datastream/OBJ/view