Ph.D
Group : Large-scale Heterogeneous DAta and Knowledge
Découverte de connaissances pour la maintenance avionique, une approche d'apprentissage de concepts non supervisée
Starts on 01/06/2016
Advisor : REYNAUD, Chantal
Funding : Convention industrielle de formation par la recherche
Affiliation : vide
Laboratory : LRI - LaHDAK
Defended on 04/06/2019, committee :
Directrice de thèse :
- Mme REYNAUD Chantal, PR1 Université Paris-Sud
Rapporteurs :
- Mme LAMOLLE Myriam, Professeur Université Paris 8
- M. ESPINASSE Bernard, Professeur Aix-Marseille Université
Examinateurs :
- M. LE THANH Nhan, Professeur Université de Nice
- M. INSAURRALDE Carlos, Maître de Conférences Bristol - Univ. of the West of England
- Mme MA Yue, Maître de Conférences Université Paris-Sud
- Mme LORTAL Gaëlle, Ingénieur de Recherche Thales Research and Technology
Président :
- M. DEFUDE Bruno, Professeur Professeur Telecom SudParis
Research activities :
Abstract :
In this thesis we explore the problem of signature analysis in avionics maintenance, to identify failures in faulty equipment and suggest corrective actions to resolve the failure. The thesis takes place in the context of a CIFRE convention between Thales R&T and the Université Paris-Sud, thus it has both a theoretical and an industrial motivation.
The signature of a failure provides all the information necessary to understand, identify and ultimately repair a failure. Thus when identifying the signature of a failure it is important to make it explainable.
We propose an ontology based approach to model the domain, that provides a level of automatic interpretation of the highly technical tests performed in the equipment. Once the tests can be interpreted, corrective actions are associated to them.
The approach is rooted on concept learning, used to approximate description logic concepts that represent the failure signatures.
Since these signatures are not known in advance, we require an unsupervised learning algorithm to compute the approximations. In our approach the learned signatures are provided as description logics (DL) definitions which in turn are associated to a minimal set of axioms in the A-Box. These serve as explanations for the discovered signatures. Thus providing a glass-box approach to trace the reasons on how and why a signature was obtained.
Current concept learning techniques are either designed for supervised learning problems, or rely on frequent patterns and large amounts of data. We use a different perspective, and rely on a bottom-up construction of the ontology. Similarly to other approaches, the learning process is achieved through a refinement operator that traverses the space of concept expressions, but an important difference is that in our algorithms this search is guided by the information of the individuals in the ontology.
To this end the notions of justifications in ontologies, most specific concepts and concept refinements, are revised and adapted to our needs.
The approach is then adapted to the specific avionics maintenance case in Thales Avionics, where a prototype has been implemented to test and evaluate the approach as a proof of concept.
