THÈSE
THÈSE
en vue de l’obtention du
DOCTORAT DE L’UNIVERSITÉ DE TOULOUSE
DOCTORAT DE L’UNIVERSITÉ DE TOULOUSE
délivré par
Institut National Polytechnique de Toulouse
Présentée et soutenue par
Ferhat TAMSSAOUET
le 09/09/2020
Towards system-level prognostics : Modeling,
uncertainty propagation and system remaining
useful life prediction
École doctorale :
EDSys : École Doctorale Systèmes
Unité de recherche :
Laboratoire de Génie de Production de l’ENI de Tarbes (LGP-ENIT)
Directeurs de Thèse :
M. Kamal MEDJAHER Professeur, ENI de Tarbes Directeur
Mme. T. P. Khanh NGUYEN Maître de Conférences, ENI de Tarbes Co-encadrante
Membres du jury :
Mme. Anne BARROS Professeur, CentraleSupélec Rapporteure
M. Enrico ZIO Professeur, Mines ParisTech et Rapporteur
Ecole Polytechnique de Milan
M. Mustapha OULADSINE Professeur, Aix Marseille Université Examinateur
Mme. Louise
TRAVÉ-MASSUYÈS Directrice de recherche, LAAS-CNRS Examinatrice
M. Marcos ORCHARD Professeur, Université du Chili Invité
Acknowledgments
Undertaking this Ph.D. has been a truly life-changing experience for me, and it would not
have been possible to do without the support and guidance that I received from many people.
Firstly, I would like to express my sincere gratitude to my thesis supervisor Kamal Med-
jaher for his support and motivation during my thesis. This work owes a great deal to his
vast expertise and insightful comments. My sincere thanks also go to my thesis co-supervisor
Khanh Thi Phuong Nguyen, for her support and her availability. I will always remember her
exceptional working capacity and high scientific rigor.
I want to thank Anne Barros and Enrico Zio for accepting the invitation to be part of my
committee and reviewing the present manuscript. I appreciate their extensive and extremely
detailed examination of this work. Warm words of thanks go to Mustapha Ouladsine and
Louise Travé-Massuyès for accepting the invitation and for their constructive comments and
suggestions.
I would also like to give special thanks to Marcos Orchard, and I am grateful for his kind
invitation to the Energy Center of the University of Chile. I would also like to thank Francisco
Jaramillo, Heraldo Rozas, Lisbel Barzaga, Boris, Kylie, Thomas, and Alué, who made my
stay in Chile pleasant and highly rewarding.
I want to thank all current and former members of the LGP, especially: Houda Sarih, Linda
Elmhadhbi, Mohamed Salem, Moncef Soualhi, Romy Ratolojanahary, and Amor Khlaief. A
big and heartfelt thanks to Camille, Danilo, Maël, Margot, Martin, Rabab, and all Lapinos
club members for their friendship: Ayoub, Barbara, Fabio, Guillaume, Louis, Marie, Pauline,
Nicolas, Yacin, and Yves. My gratitude also goes to my lifelong friends: Adnan, Faiçal, Lamia,
Massi, Meriem, and Syphax. Last but not least, I would like to say tanmirt to my parents
and my brothers for supporting me spiritually throughout the writing of this thesis and my
life in general.
Ferhat Tamssaouet
07/10/2020
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ii
Prediction is very difficult,
especially about the future.
Niels Bohr
Contents
General introduction 1
1 Literature review and problem statement 7
1.1 Introduction..................................... 7
1.2 MaintenanceandPHM............................... 8
1.2.1 Maintenance ................................ 8
1.2.2 Prognostics and Health Management . . . . . . . . . . . . . . . . . . . 12
1.2.3 Failure prognostics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.3 System-level prognostics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.3.1 Definition of system-level prognostics . . . . . . . . . . . . . . . . . . . 16
1.3.2 Advantages of SLP on CLP for complex systems . . . . . . . . . . . . 17
1.3.3 Literature review on system-level prognostics . . . . . . . . . . . . . . 17
1.3.4 Challenges related to system-level prognostics . . . . . . . . . . . . . . 25
1.4 Literature discussion and positioning . . . . . . . . . . . . . . . . . . . . . . . 30
1.4.1 Downsides of existing approaches . . . . . . . . . . . . . . . . . . . . . 30
1.4.2 Positioning, contributions and proposed approach . . . . . . . . . . . . 31
1.5 Conclusion ..................................... 33
2 System degradation modeling: Inoperability Input-output Model 35
2.1 Introduction..................................... 35
2.2 IIMbackground................................... 36
2.2.1 Input-output model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.2.2 Inoperability input-output model . . . . . . . . . . . . . . . . . . . . . 38
2.2.3 Dynamic inoperability input-output model . . . . . . . . . . . . . . . 40
2.2.4 Beyond the state of the art . . . . . . . . . . . . . . . . . . . . . . . . 41
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iv Contents
2.3 System-level modeling based on the IIM: application to prognostics . . . . . . 42
2.4 Advantages of the use of the IIM in system-level prognostics. . . . . . . . . . 49
2.4.1 Discussion of the advantage of the proposed IIM compared to the state-
spacemodel. ................................ 49
2.4.2 Degradation interactions modeling using IIM . . . . . . . . . . . . . . 50
2.5 Conclusion ..................................... 51
3 System health state estimation and SRUL prediction 53
3.1 Introduction..................................... 53
3.2 Construction of system degradation model . . . . . . . . . . . . . . . . . . . . 54
3.2.1 Functional and dysfunctional system analysis . . . . . . . . . . . . . . 54
3.2.2 Model parameter estimation . . . . . . . . . . . . . . . . . . . . . . . . 56
3.3 System health state estimation and prediction . . . . . . . . . . . . . . . . . . 63
3.3.1 System health state filtering . . . . . . . . . . . . . . . . . . . . . . . . 64
3.3.2 Inoperability uncertainty prediction . . . . . . . . . . . . . . . . . . . 67
3.3.3 SRUL determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.4 Methodology for joint parameter estimation and SRUL prediction . . . . . . 76
3.5 Conclusion ..................................... 79
4 Post-prognostics decision: From mission profile effects analysis to SRUL
maximization 81
4.1 Introduction..................................... 81
4.2 Effect of mission profile on system degradation . . . . . . . . . . . . . . . . . 82
4.2.1 System description and its degradation modeling . . . . . . . . . . . . 83
4.2.2 Mission profile effect on the SRUL . . . . . . . . . . . . . . . . . . . . 86
4.3 SRUL maximization through mission profile optimization . . . . . . . . . . . 91
4.4 Conclusion ..................................... 96
5 Application of the proposed methodology on real industrial case study:
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