Commercial aircrafts generate a huge amount of data during each flight by sampling hundreds of variables at different resolutions during all phases of flight. While having this enormous source of data is useful for learning of faulty system behavior, its huge dimensionality and size can be an impeding factor to such analysis.
To address this problem, we have devised a data-driven process that automatically extracts persistent, underlying latent states that can succinctly describe the data and thereby reduce its dimensionality, while preserving the most salient aspects important for fault or potential fault analysis. By analyzing how these latent states transition in time by computing a transition matrix for every leg, which we use as features, we can classify certain precursors which are indicative of a potential fault. Specifically, this is achieved by supervised and unsupervised learning of hundreds of latent state transitions for a given subsystem. Analysis of temporal dynamics of state transitions allows us to pinpoint at what time the sensor variables were behaving atypically in a flight leg, thus allowing airline maintainers to fix the faulty component quicker and avoid flight delays due to unplanned maintenance. We demonstrate our method of supervised and unsupervised classification over temporal dynamics of system state transition on two subsystems, the Fan Air Modulating Valve (FAMV) and the Flow Control Valve (FCV), and have obtained 100% true positive rate (for both systems) and a false positive rate of 0.05-0.08%.
How to Cite
Fault Prediction, Latent states, Unsupervised Learning, Machine Learning, Outlier Analysis
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
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.