Structured Data Ontology for AI in Industrial Asset Condition Monitoring
Author:
Affiliation:
1. Department of Mechanical Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
2. Business Analytics and Information Systems, University of San Fransisco, San Fransisco, CA 94117, USA
Abstract
Funder
Natural Sciences and Engineering Research Council of Canada ALLRP
Ontario Centre of Innovation
Publisher
MDPI AG
Link
https://www.mdpi.com/2224-2708/13/2/23/pdf
Reference22 articles.
1. (2023, May 30). ISO 17359:2018(en); Condition Monitoring and Diagnostics of Machines—General Guidelines. Available online: https://www.iso.org/obp/ui/#iso:std:iso:17359:ed-3:v1:en.
2. Casellas, N. (2011). Legal Ontology Engineering: Methodologies, Modelling Trends, and the Ontology of Professional Judicial Knowledge, Springer. Law, Governance and Technology Series.
3. CYC: A mid-term report;Guha;Appl. Artif. Intell.,1991
4. Mueen, A., Keogh, E., Zhu, Q., Cash, S., and Westover, B. (May, January 30). Exact Discovery of Time Series Motifs. Proceedings of the 2009 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, Sparks, Nevada.
5. Applications of machine learning to machine fault diagnosis: A review and roadmap;Lei;Mech. Syst. Signal Process.,2020
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