Semi-Supervised Active Learning for Anomaly Detection in Aviation

Author:

Memarzadeh Milad1ORCID,Matthews Bryan2,Templin Thomas3,Sharif Rohani Aida1,Weckler Daniel2

Affiliation:

1. Universities Space Research Association, Moffett Field, California 94035

2. KBRwyle, Moffett Field, California 94035

3. NASA Ames Research Center, Moffett Field, California 94035

Abstract

Anomaly detection in commercial aviation is an extremely challenging yet crucial task. Accurately detecting operationally significant anomalies can save civilian lives and/or result in significant savings in maintenance cost. The current practice uses manually tuned rule-based mechanisms to flag exceedances from predefined safety boundaries. However, this system cannot identify unknown risks and emerging vulnerabilities. Recently, innovative approaches based on machine learning have been used to automate anomaly detection. However, there are limits to their applicability in the field of aviation due to several challenges: 1) Properly reviewed data are scarce in aviation and, as a result, supervised learning cannot reach optimal performance. 2) Operationally significant anomalies do not coincide with statistically significant ones and, as a result, unsupervised learning fails to provide reliable and robust performance. In this paper, we propose a semi-supervised active learning framework for anomaly detection (SALAD), which detects operationally significant anomalies in flight operational quality assurance data. The developed framework works with vast amounts of unlabeled data as well as a small quantity of labeled data reviewed by subject matter experts to reliably identify safety anomalies in flight operations. Moreover, the model’s active learning strategy allows it to detect unknown anomalies that might emerge in the system. We validate the performance of the SALAD with a real-world case study of anomaly detection during the approach to landing of commercial aircraft. We specifically show that the proposed framework reaches reliable performance when only 1% of the data is labeled and can identify unknown anomalies effectively.

Funder

Ames Research Center

Publisher

American Institute of Aeronautics and Astronautics (AIAA)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Aerospace Engineering

Reference14 articles.

1. “Flight Operational Quality Assurance,” U.S. Dept. of Transportation, Federal Aviation Administration Advisory Circular 120-82, 2004, https://www.faa.gov/documentLibrary/media/Advisory_Circular/AC_120-82.pdf [retrieved 3 Dec. 2021].

2. Outlier Detection

3. Critical Parameter Identification for Safety Events in Commercial Aviation Using Machine Learning

4. Anomaly Detection and Cause Analysis During Landing Approach Using Recurrent Neural Network

5. Vector Autoregressive Model-Based Anomaly Detection in Aviation Systems

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