Study of Mathematical Algorithms to Identify Abnormal Patterns in Aircraft Flight Data Engenharia

126 Pages·2012·13.81 MB·English

Study of Mathematical Algorithms to Identify
Abnormal Patterns in Aircraft Flight Data
Helder Mendes
Disserta(cid:231)ªo para a Obten(cid:231)ªo do Grau de Mestre em
Engenharia ElectrotØcnica e de Computadores
Jœri
Presidente: Prof. Nuno Cavaco Gomes Horta
Orientador: Prof. Bertinho Manuel Andrade da Costa
Vogal: Prof. Agostinho Rui Alves da Fonseca
2012/06/12

Acknowledgements
First of all I would like to thank my family, my wife Fernanda and my sons Afonso
and Mafalda, for providing me the environment for this work to be produced and bear
with my absent time. To my parents for always encouraged my studies in all stages of
my life.
A great word of gratitude to the coordinator of this work, Prof. Bertinho Andrade
Costa from Instituto Superior TØcnico (IST) and Instituto de Engenharia de Sistemas
e Computadores - Investiga(cid:231)ªo e Desenvolvimento (INESC - ID), for his continuous
guidance, support in my doubts and for the long hours spent discussing all the details.
A special word of gratitude to my colleague from TAP Portugal, Engo Manuel Tereno
who was always willing to contribute with his huge knowledge on di(cid:27)erent areas. For
the support and advice.
To my colleagues in TAP from the Flight Data Analysis area, that contribute ev-
eryday for the improvement of the analysis produced.

Abstract
This work presents several techniques to classify Autoland (cid:29)ights starting from the
Standardalgorithmsprovidedbytheaircraftmanufacturersandformersoftwareimple-
mentations, evolving then to newer concepts of unidimensional and multidimensional
statistical approaches. Several methods have been tried to distinguish between Normal
from Abnormal automatic landings based on (cid:29)ight data analysis. Together with this
analysis, the automatic procedure to detect and classify new automatic landings was
implemented with the use of Support Vector Machines.
Keywords: Autoland, Flight Data Analysis, Kernel Machines, Support Vector Ma-
chines, Principal Component Analysis, Single Value Decomposition.

Resumo
Neste trabalho sªo apresentadas vÆrias tØcnicas de classi(cid:28)ca(cid:231)ªo de voos Autoland
(Aterragens AutomÆticas) com inicio na utiliza(cid:231)ªo de algoritmos standard fornecidos
pelos fabricantes de aviªo e utilizados em programas de anÆlise no passado, evoluindo
para novos conceitos estat(cid:237)sticos de anÆlise unidimensional e multidimensional. VÆrios
mØtodos foram testados para fazer a distin(cid:231)ªo entre aterragens automÆticas Normais
e Nªo Normais com base na anÆlise dos dados de voo. A par desta anÆlise, foi criado
um procedimento automÆtico para detectar e classi(cid:28)car novas aterragens automÆticas
com uso de Support Vector Machines.
Palavras-Chave: Aterragens AutoamÆticas, AnÆlise de dados de voo, Kernel Ma-
chines,Support Vector Machines, AnÆlise das Componentes Principais, Decomposi(cid:231)ªo
em Valores Singulares.

Contents
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Aircraft Recording Systems . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.1 Flight Data Recorders . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.2 Acquisition Units and Non-Crash Recorders . . . . . . . . . . . 5
1.2.3 Flight Data Recording Process . . . . . . . . . . . . . . . . . . . 9
1.3 Flight Data Analysis Software - AGS (Analysis Ground Station) . . . . 10
1.4 Previous Work and Thesis Scope . . . . . . . . . . . . . . . . . . . . . 11
1.5 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 Autoland Analysis 14
2.1 The Autoland Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2 Standard Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.1 Poor Integrity (PI) . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.2 Poor Performance (PPF) . . . . . . . . . . . . . . . . . . . . . . 18
2.3 Key Point Data Extraction . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.1 General Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3.2 Air Sector Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3.3 Ground Sector Data . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3.4 AGS Procedure Output . . . . . . . . . . . . . . . . . . . . . . 21
2.4 Autoland and Autoapproach Decision Logics . . . . . . . . . . . . . . . 22
2.4.1 Logics for Flight Type Determination . . . . . . . . . . . . . . . 23
2.5 Autoland Evaluation with Standard Procedure (PPF and PI) . . . . . . 26
2.6 Histogram Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.6.1 Evaluation File Format . . . . . . . . . . . . . . . . . . . . . . . 28
2.7 Outlier Identi(cid:28)cation and Removal . . . . . . . . . . . . . . . . . . . . 30
2.8 Criteria to Identify Abnormal Cases . . . . . . . . . . . . . . . . . . . . 33
2.9 Multidimensional Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 34
2.9.1 Principal Components Analysis (PCA) . . . . . . . . . . . . . . 34
2.9.2 Biplot Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3 Kernel Methods and Support Vector Machines 42
3.1 Introduction to SVMs . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.2 Binary Classi(cid:28)er - Hard Margins . . . . . . . . . . . . . . . . . . . . . . 43
3.3 Binary Classi(cid:28)er - Soft Margins . . . . . . . . . . . . . . . . . . . . . . 49
3.4 Binary Classi(cid:28)cation - Kernel Trick . . . . . . . . . . . . . . . . . . . . 51
4 Presentation of Results 54
4.1 Applications of the methods to novelty data . . . . . . . . . . . . . . . 55
4.2 Practical Application - Autoland Flights Year 2011 . . . . . . . . . . . 55
4.2.1 Input Space (cid:6)3(cid:27) Evaluation . . . . . . . . . . . . . . . . . . . . 55
4.2.2 Principal Components (cid:6)3(cid:27) Evaluation . . . . . . . . . . . . . . 56
4.2.3 SVM Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.2.4 Conclusions on the Obtained Results . . . . . . . . . . . . . . . 56
4.3 Practical Application - Autoland Flights Year 2009 . . . . . . . . . . . 58
4.3.1 Input Space (cid:6)3(cid:27) Evaluation . . . . . . . . . . . . . . . . . . . . 58
4.3.2 Principal Components (cid:6)3(cid:27) Evaluation . . . . . . . . . . . . . . 58
4.3.3 SVM Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.3.4 Conclusions on the Obtained Results . . . . . . . . . . . . . . . 59
5 Conclusions and Future Work 61
5.1 Conclusions on the obtained results . . . . . . . . . . . . . . . . . . . . 62
5.2 Future Work and Developments . . . . . . . . . . . . . . . . . . . . . 62
5.3 Final Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
A Flight Data Extracted from AGS (Airbus A320 (cid:29)eet) 65
B Additional Procedures 69
B.1 Wind Component Calculation . . . . . . . . . . . . . . . . . . . . . . . 69
B.1.1 AGS code for Wind Components Calculation . . . . . . . . . . . 70
B.2 Touch Down Point Determination . . . . . . . . . . . . . . . . . . . . . 71
B.2.1 AGS code for Touch Down determination . . . . . . . . . . . . . 75
B.3 Baro-Inertial Vertical Speed and Radio Altitude Rate . . . . . . . . . . 76