Aerospace industries have become increasingly concerned about system availability and reliability. Data driven based prognosis is an emerging application aimed at building predictive models from readily available maintenance and operational databases. After validation, these models can be integrated into PHM systems to monitor equipment health and predict component failures before such events disrupt operations.
For this project legacy data collected from two databases associated to a fleet of civil aircrafts during a period spanning 5 years have been used. The first database contains Central Management System (CMS) data (BIT messages and Flight Deck Effects), the second logs of maintenance activities. Part of the data collected from 2012 to mid 2015 have been used for the learning phase the rest spanning 2012-2016 period have been used for validation
The goal is to predict failure events within an interval ranging from two to ten flights in advance to avoid unscheduled maintenance activities and operational disruptions. Hence two flights represent the minimal notice period/prognostics horizon whilst 10 flights is the maximal acceptable wasted life. Data-driven based prognostic uses pattern recognition and machine learning techniques to train historic data. In the proposed approach both techniques have been used. Through Support Vector Machine a prognostics anomaly detection step is initially performed to select the flight legs candidate for a prognostics alert. In a further step a subspace technique, borrowed from image processing domain and named Eigenface, allows to produce the signatures of the different types of maintenance actions and a template matching algorithm determines among the prognostics alert candidates the component to be replaced.
Several tests have been conducted for different types of replacements and results will be presented using Receiver Operating Characteristic (ROC) curves and precision/recall metrics. Information contained in ROC allows the airliner to identify, according to its economic criteria, the optimal prognostics operating points.
How to Cite
Prognostics Data Driven Pattern Recognition
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.