Pattern-Based Prognostic Methodology for Condition Based Maintenance Using Selected and

214 Pages·2015·2.47 MB·English

Titre: Fault Prognostics Using Logical Analysis of Data and Non-Parametric
Title: Reliability Estimation Methods
Auteur:
Ahmed Ragab Anwar Ragab
Author:
Date: 2014
Type: Mémoire ou thèse / Dissertation or Thesis
Ragab, A. R. A. (2014). Fault Prognostics Using Logical Analysis of Data and Non-
Référence:
Parametric Reliability Estimation Methods [Thèse de doctorat, École
Citation:
Polytechnique de Montréal]. PolyPublie. https://publications.polymtl.ca/1658/
Document en libre accès dans PolyPublie
Open Access document in PolyPublie
URL de PolyPublie:
https://publications.polymtl.ca/1658/
PolyPublie URL:
Directeurs de
recherche: Soumaya Yacout, & Mohamed-Salah Ouali
Advisors:
Programme:
Génie industriel
Program:
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UNIVERSITÉ DE MONTRÉAL

FAULT PROGNOSTICS USING LOGICAL ANALYSIS OF DATA AND
NON-PARAMETRIC RELIABILITY ESTIMATION METHODS

AHMED RAGAB ANWAR RAGAB
DÉPARTEMENT DE MATHÉMATIQUES ET DE GENIE INDUSTRIEL
ÉCOLE POLYTECHNIQUE DE MONTRÉAL

THÈSE PRÉSENTÉE EN VUE DE L’OBTENTION
DU DIPLÔME DE PHILOSOPHIAE DOCTOR
(GÉNIE INDUSTRIEL)
DECEMBRE 2014

© Ahmed Ragab Anwar Ragab, 2014.
UNIVERSITÉ DE MONTRÉAL

ÉCOLE POLYTECHNIQUE DE MONTRÉAL

Cette thèse intitulée :

FAULT PROGNOSTICS USING LOGICAL ANALYSIS OF DATA AND
NON-PARAMETRIC RELIABILITY ESTIMATION METHODS

présentée par : RAGAB Ahmed Ragab Anwar
en vue de l’obtention du diplôme de : Philosophiae Doctor
a été dûment acceptée par le jury d’examen constitué de :
M. ADJENGUE Luc-Désiré, Ph. D., président
Mme YACOUT Soumaya, D. Sc., membre et directrice de recherche
M. OUALI Mohamed-Salah, Doctorat, membre et codirecteur de recherche
M. AGARD Bruno, Doctorat, membre
M. KUMRAL Mustafa, Ph. D., membre
iii
DEDICATE
I dedicate this thesis to my parents, my beloved wife (Yasmine), my son (Omar), and my daughter
(Sarah) whom I am indebted to for their sacrifices, encouragement, and endless and unwavering love.
iv
ACKNOWLEDGEMENTS
There are many people I wish to thank for their help on this thesis. First, I would like to express
my profound gratitude to my research director Dr. Soumaya Yacout whom I have had the privilege
of being her student and whom I am heavily indebted to. Without her, this thesis could not have
been found. I am even more fortunate that she generously gave me more of her valuable time to
complete this work. I am indebted to her initial vision and long-term support for the research
interest upon which this thesis is based. I appreciate her patience and gentle guidance during the
whole process of this research. Her words of wisdom provided me with enough confidence in my
skills and encouraged me to undertake this work. She shared with me her knowledge that is
valuable as gold.
My profound gratitude goes as well to my co-director of research Dr. Mohamed-Salah Ouali whom
I received wisdom and knowledge. He shared with me his experience and knowledge throughout
the work. I appreciate his dedication and insightful comments. I have relied upon his insights,
encouragement, and experience to formulate my thinking with regard to the technical and
theoretical aspects in this thesis. I am deeply grateful to his remarkable support. I appreciate his
patience, suggestions for improvements, and willingness to test algorithms and point out errors in
the programs’ codes. I am grateful to him for giving me the chance to prove myself.
I owe a special note of regards to Dr. Luc Adjengue, Dr. Bruno Agard, and Dr. Mustafa Kumral
for their constructive role as members of my jury.
My gratitude and appreciation go as well to Dr. Hany Osman for providing me continuously with
the updated versions of the software cbmLAD.
I would like to extend my thanks and appreciation to all those colleagues from our department for
their support and advices to enhance the compilation of this thesis, in particular Xavier de Carné
de Carnavalet, Mohamed Ossama, and Wissem Maazoun.
I would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC)
for their fund to accomplish this doctoral program. My thanks go as well to Prüftechnik Canada
for supporting us with the vibration data employed in this research.
v
RÉSUMÉ
Estimer la durée de vie utile restante (RUL) d’un système qui fonctionne suivant différentes
conditions de fonctionnement représente un grand défi pour les chercheurs en maintenance
conditionnelle (CBM). En effet, il est difficile de comprendre la relation entre les variables qui
représentent ces conditions de fonctionnement et la RUL dans beaucoup de cas en pratique à cause
du degré élevé de corrélation entre ces variables et leur dépendance dans le temps. Il est également
difficile, voire impossible, pour des experts d’acquérir et accumuler un savoir à propos de systèmes
complexes, où l'échec de l'ensemble du système est vu comme le résultat de l'interaction et de la
concurrence entre plusieurs modes de défaillance.
Cette thèse présente des méthodologies pour le pronostic en CBM basé sur l'apprentissage
automatique, et une approche de découverte de connaissances appelée Logical Analysis of Data
(LAD). Les méthodologies proposées se composent de plusieurs implémentations de la LAD
combinées avec des méthodes non paramétriques d'estimation de fiabilité. L'objectif de ces
méthodologies est de prédire la RUL du système surveillé tout en tenant compte de l'analyse des
modes de défaillance uniques ou multiples. Deux d’entre elles considèrent un mode de défaillance
unique et une autre considère de multiples modes de défaillance. Les deux méthodologies pour le
pronostic avec mode unique diffèrent dans la manière de manipuler les données.
Les méthodologies de pronostique dans cette recherche doctorale ont été testées et validées sur la
base d'un ensemble de tests bien connus. Dans ces tests, les méthodologies ont été comparées à des
techniques de pronostic connues; le modèle à risques proportionnels de Cox (PHM), les réseaux de
neurones artificiels (ANNs) et les machines à vecteurs de support (SVMs). Deux ensembles de
données ont été utilisés pour illustrer la performance des trois méthodologies: l'ensemble de
données du turboréacteur à double flux (turbofan) qui est disponible au sein de la base de données
pour le développement d'algorithmes de pronostic de la NASA, et un autre ensemble de données
obtenu d’une véritable application dans l'industrie. Les résultats de ces comparaisons indiquent que
chacune des méthodologies proposées permet de prédire avec précision la RUL du système
considéré.
Cette recherche doctorale conclut que l’approche utilisant la LAD possède d’importants mérites et
avantages qui pourraient être bénéfiques au domaine du pronostic en CBM. Elle est capable de
gérer les données en CBM qui sont corrélées et variantes dans le temps. Son autre avantage et
qu’elle génère un savoir interprétable qui est bénéfique au personnel de maintenance.
vi
ABSTRACT
Estimating the remaining useful life (RUL) for a system working under different operating
conditions represents a big challenge to the researchers in the condition-based maintenance (CBM)
domain. The reason is that the relationship between the covariates that represent those operating
conditions and the RUL is not fully understood in many practical cases, due to the high degree of
correlation between such covariates, and their dependence on time. It is also difficult or even
impossible for the experts to acquire and accumulate the knowledge from a complex system, where
the failure of the system is regarded as the result of interaction and competition between several
failure modes.
This thesis presents systematic CBM prognostic methodologies based on a pattern-based machine
learning and knowledge discovery approach called Logical Analysis of Data (LAD). The proposed
methodologies comprise different implementations of the LAD approach combined with non-
parametric reliability estimation methods. The objective of these methodologies is to predict the
RUL of the monitored system while considering the analysis of single or multiple failure modes.
Three different methodologies are presented; two deal with single failure mode and one deals with
multiple failure modes. The two methodologies for single mode prognostics differ in the way of
representing the data.
The prognostic methodologies in this doctoral research have been tested and validated based on a
set of widely known tests. In these tests, the methodologies were compared to well-known
prognostic techniques; the proportional hazards model (PHM), artificial neural networks (ANNs)
and support vector machines (SVMs). Two datasets were used to illustrate the performance of the
three methodologies: the turbofan engine dataset that is available at NASA prognostic data
repository, and another dataset collected from a real application in the industry. The results of these
comparisons indicate that each of the proposed methodologies provides an accurate prediction for
the RUL of the monitored system.
This doctoral research concludes that the LAD approach has attractive merits and advantages that
add benefits to the field of prognostics. It is capable of dealing with the CBM data that are
correlated and time-varying. Another advantage is its generation of an interpretable knowledge that
is beneficial to the maintenance personnel.
vii
TABLE OF CONTENTS
DEDICATE ................................................................................................................................... III
ACKNOWLEDGEMENTS .......................................................................................................... IV
RÉSUMÉ ........................................................................................................................................ V
ABSTRACT .................................................................................................................................. VI
TABLE OF CONTENTS .............................................................................................................VII
LIST OF TABLES ..................................................................................................................... XIII
LIST OF FIGURES ..................................................................................................................... XV
LIST OF ABBREVIATIONS AND NOTATIONS................................................................... XVI
LIST OF APPENDICES ........................................................................................................... XXII
CHAPTER 1 INTRODUCTION ............................................................................................... 1
1.1 Taxonomy of maintenance strategies ............................................................................... 1
1.2 Problem statement ............................................................................................................ 5
1.3 Limitations of current prognostic methods ...................................................................... 6
1.4 The advantages of using LAD as knowledge discovery approach ................................... 7
1.5 Research objectives .......................................................................................................... 7
1.5.1 General objective .......................................................................................................... 7
1.5.2 Specific research objectives ......................................................................................... 8
1.6 Originality and success ..................................................................................................... 8
1.7 Deliverables ...................................................................................................................... 9
1.8 Social impacts and economic benefits ........................................................................... 10
1.9 Thesis organization ........................................................................................................ 11
CHAPTER 2 BACKGROUND AND LITERATURE REVIEW ........................................... 12
2.1 CBM definition and standards........................................................................................ 12
viii
2.2 Types of CBM data ........................................................................................................ 13
2.3 Data Mining and Knowledge Discovery in CBM .......................................................... 14
2.3.1 Big Data in CBM ........................................................................................................ 14
2.3.2 Data Mining and machine learning in CBM .............................................................. 14
2.4 CBM Components .......................................................................................................... 15
2.4.1 Data acquisition .......................................................................................................... 15
2.4.2 Data processing .......................................................................................................... 16
2.4.3 Maintenance decision-making procedure (diagnosis and prognosis) ........................ 21
2.5 Fault prognostics in CBM .............................................................................................. 22
2.6 Reliability-based prognosis ............................................................................................ 23
2.6.1 Reliability (survival) function .................................................................................... 23
2.6.2 Survival function estimation: Parametric techniques ................................................. 24
2.6.3 Non-Parametric survival curve: Kaplan-Meier estimator .......................................... 24
2.6.4 Prognosis: Remaining useful life estimation .............................................................. 24
2.7 Updated survival curve using the condition monitoring data ........................................ 25
2.7.1 The effects of the CM data on the survival curve ...................................................... 25
2.7.2 Proportional hazards model ........................................................................................ 25
2.8 Multiple Failure Modes Prognostics in CBM ................................................................ 26
2.9 Cumulative Incidence Function (CIF): A non-parametric technique for multiple failure
modes 26
2.10 Current Prognostic Methods: A Literature Review ........................................................ 27
2.10.1 Diagnostic/Prognostic decision approaches ........................................................... 27
2.11 Model-Based Prognostic Approaches ............................................................................ 29
2.12 Data-Driven Prognostics Approaches ............................................................................ 30
2.12.1 Artificial Intelligence-based methods .................................................................... 30
ix
2.12.2 Statistical-based methods ....................................................................................... 34
2.12.3 Other approches: logical analysis of data ............................................................... 39
2.13 LAD: Historical perespective ......................................................................................... 40
2.13.1 Knowledge discovery in the form of extracted patterns ........................................ 40
2.14 Two-class LAD decision model ..................................................................................... 41
2.14.1 Stages of two-class LAD ........................................................................................ 41
2.14.2 Data binarization .................................................................................................... 41
2.14.3 Pattern generation ................................................................................................... 41
2.14.4 Definition and characteristics of patterns ............................................................... 42
2.14.5 Pattern generation methods .................................................................................... 43
2.14.6 MILP-based method ............................................................................................... 43
2.14.7 Pattern selection ..................................................................................................... 45
2.14.8 Theory formation: discriminant function ............................................................... 46
2.15 Multi-class LAD decision model ................................................................................... 47
2.15.1 Pattern generation for multi-class LAD decision model ........................................ 47
2.15.2 Scoring function for multi-class LAD decision model .......................................... 48
2.16 LAD's applications ......................................................................................................... 48
2.16.1 Application of LAD in medical diagnosis and prognosis ...................................... 48
2.16.2 Other applications .................................................................................................. 49
2.16.3 Applications of LAD in CBM diagnostics ............................................................. 50
2.17 Application of LAD in CBM Prognostics: Proposed methodologies .................................. 50
CHAPTER 3 ARTICLE 1 : REMAINING USEFUL LIFE PREDICTION USING
PROGNOSTIC METHODOLOGY BASED ON LOGICAL ANALYSIS OF DATA AND
KAPLAN–MEIER ESTIMATION ............................................................................................... 52
3.1 Abstract .......................................................................................................................... 53