Exploring a Knowledge-Based Approach for Predictive Maintenance of Aircraft Engines: Studying Fault Propagation through Spatial and Topological Component Relationships
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Predictive maintenance has become a highly favored application in Industry 4.0, particularly in complex systems with requirements for reliability, robustness, and performance. Aircraft engines are among these systems, and several studies have been conducted to try to estimate their remaining lifespan. The C-MAPSS dataset provided by NASA has greatly served the scientific community, and several works based on physical models and data-driven approaches have been proposed. However, several limitations related to data quality or data availability of failures persist, and integrating domain knowledge can help address these challenges. In this article, we are currently implementing a new approach based on knowledge coupled with qualitative spatial reasoning to study the propagation of faults within system components until complete shutdown. Region Connection Calculus (RCC8) formal model will be used to describe the component relationships, drawing inspiration from the C-MAPSS dataset.
How to Cite
##plugins.themes.bootstrap3.article.details##
Predictive maintenance, remaining useful life, prognostic and health management, knowledge representation, c-mapss, industrie 4.0
Baader, F., Horrocks, I., & Sattler, U. (2005). Description logics as ontology languages for the semantic web. In D. Hutter & W. Stephan (Eds.), Mechanizing mathematical reasoning: Essays in honor of jorg h. siekmann on the occasion of his 60th birthday (pp. 228–248). Berlin, Heidelberg: Springer Berlin Heidelberg. Barry, I., & Hafsi, M. (2023, December). Towards hybrid predictive maintenance for aircraft engine: Embracing an ontological-data approach. In 20th acs/ieee international conference on computer systems and applications. Giza, Egypt.
Barry, I., Hafsi, M., & Mian Qaisar, S. (2023). Boosting regression assistive predictive maintenance of the aircraft engine with random-sampling based class balancing. In 13th International Conference on Information Systems and Advanced Technologies.
Bl´azquez, M., Fern´andez-L´opez, M., Garc´ıa-Pinar, J., & Gomez-Perez, A. (1998, 01). Building ontologies at the knowledge level using the ontology design environment.
Cao, Q., Giustozzi, F., Zanni-Merk, C., de Bertrand de Beuvron, F., & Reich, C. (2019). Smart condition monitoring for industry 4.0 manufacturing processes: An ontology-based approach. Cybernetics and Systems, 50(2), 82–96.
Cao, Q., Samet, A., Zanni-Merk, C., De Bertrand de Beuvron, F., & Reich, C. (2019, 01). An ontology-based approach for failure classification in predictive maintenance using fuzzy c-means and swrl rules. Procedia Computer Science, 159, 630-639.
Cao, Q., Zanni-Merk, C., Samet, A., Reich, C., De Bertrand de Beuvron, F., Beckmann, A., & Giannetti, C. (2022a). Kspmi: A knowledge-based system for predictive maintenance in industry 4.0. Robotics and Computer-Integrated Manufacturing, 74, 102281. Cao, Q., Zanni-Merk, C., Samet, A., Reich, C., De Bertrand de Beuvron, F., Beckmann, A., & Giannetti, C. (2022b, 04). Kspmi: A knowledgebased system for predictive maintenance in industry
4.0. Robotics and Computer-Integrated Manufacturing, 74, 102281. Cardoso, D., & Ferreira, L. (2021). Application of predictive maintenance concepts using artificial intelligence tools. Applied Sciences, 11(1). Chhetri, T. R., Kurteva, A., Adigun, J., & Fensel, A. (2022,
01). Knowledge graph based hard drive failure prediction. Sensors, 22, 985. Confalonieri, R., & Guizzardi, G. (2023). On the multiple roles of ontologies in explainable ai. Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J., & Barbosa, J. (2020). Machine learning and reasoning for predictive maintenance in industry 4.0: Current status and challenges. Computers in Industry, 123, 103298. Dangut, M. D., Jennions, I. K., King, S., & Skaf, Z. (2022, mar). A rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approach. Neural Comput. Appl., 35(4), 2991–3009. de Pater, I., Reijns, A., & Mitici, M. (2022). Alarm-based predictive maintenance scheduling for aircraft engines with imperfect remaining useful life prognostics. Reliability Engineering & System Safety, 221, 108341. Fern´andez-L´opez, M., Gomez-Perez, A., & Juristo, N. (1997,
03). Methontology: from ontological art towards ontological engineering. Engineering Workshop on Ontological Engineering (AAAI97). Fraske, T. (2022). Industry 4.0 and its geographies: A systematic literature review and the identification of new research avenues. Digital Geography and Society, 3, 100031. Hou, J., Qiu, R., Xue, J., Wang, C., & Jiang, X.-Q. (2020). Failure prediction of elevator running system based on knowledge graph. In Proceedings of the 3rd international conference on data science and information technology (pp. 53–58). Kumar, K. D. (2021). Remaining useful life prediction of aircraft engines using hybrid model based on artificial intelligence techniques. In 2021 ieee international conference on prognostics and health management (icphm) (pp. 1–10). Ladron-de Guevara-Munoz, M. C., Alonso-Garcia, M., de Cozar-Macias, O. D., & Blazquez-Parra, E. B. (2023). The place of descriptive geometry in the face of industry 4.0 challenges. Symmetry, 15(12). Li, X., Zhang, F., Li, Q., Zhou, B., & Bao, J. (2023). Exploiting a knowledge hypergraph for modeling multinary relations in fault diagnosis reports. Advanced Engineering Informatics, 57, 102084. Lima, G., Costa, R., & Moreno, M. F. (2019, 11). An introduction to artificial intelligence applied to multimedia. ArXiv, abs/1911.09606.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.