Skip to Main content Skip to Navigation
Journal articles

Expert system dedicated to condition-based maintenance based on a knowledge graph approach: Application to an aeronautic system

Abstract : Condition Based Maintenance (CBM) has become the focus of many research topics over the past decades. This is mostly related to the development of new machine learning algorithms and the ever increasing capacity to collect data allowing failures to be detected and the system’s remaining lifetime to be estimated while requiring few or no expert knowledge. However, current machine learning based CBM solutions have limitations. They require an extensive and relevant data set to train on and are performed at the component level, not system-wide. Conversely, Expert Systems (ES) do not have these restrictions but should be used on systems with available expert knowledge and are currently suffering from efficiency, scalability and applicability limits. In this paper, an ES solution for CBM based on an heterogeneous information network will be presented to address the efficiency, scalability and applicability issues of modern ES. An application to an aircraft system will be used as case study to illustrate the process and performance of this solution for anomaly detection and diagnostics.
Document type :
Journal articles
Complete list of metadata

https://hal-mines-albi.archives-ouvertes.fr/hal-03333514
Contributor : Imt Mines Albi Imt Mines Albi <>
Submitted on : Monday, September 20, 2021 - 6:35:36 PM
Last modification on : Tuesday, September 21, 2021 - 10:22:05 AM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2022-02-19

Please log in to resquest access to the document

Identifiers

Collections

Citation

Alexandre Sarazin, Jérémy Bascans, Jean-Baptiste Sciau, Jiefu Song, Bruno Supiot, et al.. Expert system dedicated to condition-based maintenance based on a knowledge graph approach: Application to an aeronautic system. Expert Systems with Applications, Elsevier, 2021, 186, pp.1-10/115767. ⟨10.1016/j.eswa.2021.115767⟩. ⟨hal-03333514⟩

Share

Metrics

Record views

24