Multiclass Anomaly Detection in Flight Data Using Semi-Supervised Explainable Deep Learning Model-Reference-Cited by-同舟云学术

Multiclass Anomaly Detection in Flight Data Using Semi-Supervised Explainable Deep Learning Model

Published:2022-02 Issue:2 Volume:19 Page:83-97
ISSN:2327-3097
Container-title:Journal of Aerospace Information Systems
language:en
Short-container-title:Journal of Aerospace Information Systems

Author:

Memarzadeh Milad¹^ORCID,Matthews Bryan¹,Templin Thomas¹

Affiliation:

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

Funder

Ames Research Center

Publisher

American Institute of Aeronautics and Astronautics (AIAA)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Aerospace Engineering

Link

https://arc.aiaa.org/doi/pdf/10.2514/1.I010959

Reference11 articles.

1. “Flight Operational Quality Assurance,” Federal Aviation Administration, TR No. 120-82, 2004, https://www.faa.gov/documentLibrary/media/Advisory_Circular/AC_120-82.pdf.

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

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

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

5. Anomaly Detection and Diagnosis Algorithms for Discrete Symbol Sequences with Applications to Airline Safety

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