Primena veštačke inteligencije u pouzdanosti i održavanju

Ključne reči: veštačka inteligencija, pouzdanost, pogodnost održavanja, održavanje

Sažetak


Uvod/cilj: Krajem 2019. godine Vlada Republike Srbije usvojila je Strategiju razvoja veštačke inteligencije u Republici Srbiji za period 2020-2025. godine. S tim uvezi, u ovom radu je predstavljen pregled trenutnih primena aplikacija veštačke inteligencije u oblasti pouzdanosti i održavanja, kao i budućih primena.

Metode: Istraživanje je realizovano zahvaljujući dostupnoj literaturi, uglavnom iz baze podataka Science Direct, korišćenjem apstrakta, a u nekim slučajevima i čitavih radova.

Rezultati: Rezultat ovog istraživanja je pregled primena aplikacija veštačke inteligencije u oblasti pouzdanosti i održavanja u poslednjih trideset godina. Takođe, pokazano je da sistem AI može biti nepouzdan i da mu je potrebno održavanje.

Zaključak: Veštačka inteligencija može se primeniti i u pouzdanosti i održavanju. Iz dostupne literature može se zaključiti da se AI češće primenjuje u održavanju nego u pouzdanosti. Napredak u AI je neizbežan, pa je važno razumeti njegove potencijale za primenu u pouzdanosti i održavanju, kao i moguće zamke.

Biografija autora

Slavko J. Pokorni, Visoka škola strukovnih studija za informacione tehnologije, Beograd, Republika Srbija

 

 

 

 

 

Reference

Alsina, E.F., Chica, M., Trawinski, K. & Regattieri, A. 2018. On the use of machine learning methods to predict component reliability from data-driven industrial case studies. The International Journal of Advanced Manufacturing Technology, 94, pp.2419-2433. Available at: https://doi.org/10.1007/s00170-017-1039-x.

Bathaee, Y. 2018. The Artificial Intelligence Black Box and the Failure of Intent and Causation. Harvard Journal of Law & Technology, 31(2) [online]. Available at: https://jolt.law.harvard.edu/assets/articlePDFs/v31/The-Artificial-Intelligence-Black-Box-and-the-Failure-of-Intent-and-Causation-Yavar-Bathaee.pdf [Accessed: 15.01.2021].

Bauer, E. & Adams, R. 2012. Reliability and Availability of Cloud Computing. Hoboken, NJ: John Wiley & Sons. ISBN: 978-1-118-17701-3.

Bhargava, C. 2019. AI Techniques for Reliability Prediction for Electronic Components. Hershey, PA: IGI Global. Available at: https://doi.org/10.4018/978-1-7998-1464-1. ISBN: 9781799814641

Blache, K.M. 2017. AI and Reliability: How Much, How Fast? Efficient Plant, 14 November [online] Available at: https://www.efficientplantmag.com/2017/11/ai-reliability-much-fast/ [Accessed: 15.01.2021].

Blier, N. 2020. Stories of AI Failure and How to Avoid Similar AI Fails. Lexalytics, 30 January [online]. Available at: https://www.lexalytics.com/lexablog/stories-ai-failure-avoid-ai-fails-2020 [Accessed: 15.01.2021].

Cheng, Z., Jia, X., Gao, P., Wu, B. & Wang, J. 2008. A framework for intelligent reliability centered maintenance analysis. Reliability Engineering & System Safety, 93(6), pp.806-814. Available at: https://doi.org/10.1016/j.ress.2007.03.037.

Copeland, B.J. 1998. Artificial intelligence In: Encyclopedia Britannica. London: Encyclopedia Britannica, Inc. [online]. Available at: https://www.britannica.com/technology/artificial-intelligence [Accessed: 15.01.2021].

Diryag, A., Mitić, M. & Miljković, Z. 2014. Neural Networks for Prediction of Robot Failures. In: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 228(8), pp.1444-1458. Available at: https://doi.org/10.1177/0954406213507704.

-European Commission. 2019. A definition of AI: Main capabilities and scientific disciplines. Brussels: European Commission Independent High-Level Expert Group on Artificial Intelligence [online]. Available at: https://ec.europa.eu/digital-single-market/en/news/definition-artificial-intelligence-main-capabilities-and-scientific-disciplines [Accessed: 15.01.2021].

Foresti, R., Rossi, S., Magnani, M., Lo Bianco, C.G. & Delmonte, N. 2020. Smart society and artificial intelligence: big data scheduling and theglobal standard method applied to smart maintenance. Engineering, 6(7), pp.835-846. Available at: https://doi.org/10.1016/j.eng.2019.11.014.

-Government of the Republic of Serbia. 2019. Strategy for the Development of Artificial Intelligence in the Republic of Serbia for the period 2020-2025 [online]. Available at: https://www.srbija.gov.rs/tekst/en/149169/strategy-for-the-development-of-artificial-intelligence-in-the-republic-of-serbia-for-the-period-2020-2025.php [Accessed: 15.01.2021].

Heaven, D. 2019. Why deep-learning AIs are so easy to fool. Nature, 09 October [online]. Available at: https://www.nature.com/articles/d41586-019-03013-5 [Accessed: 15.01.2021].

-ISO. 2020. ISO/IEC TR 24028:2020 Information technology — Artificial intelligence — Overview of trustworthiness in artificial intelligence [online]. Available at: https://www.iso.org/standard/77608.html?browse=tc [Accessed: 15.01.2021].

Kobbacy, K.A.H. 2012. Application of Artificial Intelligence in Maintenance Modelling and Management. IFAC Proceedings Volumes, 45(31), pp.54-59. Available at: https://doi.org/10.3182/20121122-2-ES-4026.00046.

Lee, W.J., Wu, H., Yun, H., Kim, H., Jun, M.B.G. & Sutherland, J.W. 2019. Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data. Procedia CIRP, 80, pp.506-511. Available at: https://doi.org/10.1016/j.procir.2018.12.019.

-Merriam-Webster. 2020. Artificial intelligence. In: Merriam-Webster.com dictionary [online]. Available at: https://www.merriam-webster.com/dictionary/artificial%20intelligence [Accessed: 15.01.2021].

-Microsoft. 2020. Azure AI guide for predictive maintenance solutions [online]. Available at: https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/predictive-maintenance-playbook [Accessed: 15.01.2021].

Otto, S. 2019. Artificial intelligence for predictive maintenance. Aerospace Manufacturing and Design, 6 Febrauary [online]. Available at: https://www.aerospacemanufacturinganddesign.com/article/artificial-intelligence-for-predictive-maintenance. Accessed: 15.01.2021.

Pokorni, S. 2016. Reliability prediction of electronic equipment: problems and experience. In: 7th International Scientific Conference on Defensive Technologies OTEH 2016, Belgrade, pp.695-700, 6-7 October. ISBN 978-86-81123-82-9.

Pokorni, S. 2018. Reliability of Internet of Things. In: 8th International Scientific Conference on Defensive Technologies OTEH 2018, Belgrade, pp.567-570, 11-12 October [online]. Available at: http://www.vti.mod.gov.rs/oteh18/elementi/rad/027.htm. ISBN 978-86-81123-88-4. [Accessed: 15.01.2021].

Pokorni, S. 2020. Artificial Intelligence in Reliability and Maintainability. In: 9th International Scientific Conference on Defensive Technologies OTEH 2020, Belgrade, 15-16 October [online]. Available at: http://www.vti.mod.gov.rs/oteh/elementi/rad/114.pdf [Accessed: 15.01.2021].

Richards, D.W. 1989. Smart BIT: A plan for intelligent built-in test. IEEE Aerospace and Electronic Systems Magazine, 4(1), pp.26-29. Available at: https://doi.org/10.1109/62.16985.

Siladić, M., Pokorni, S. & Rašuo, B. 2003. Possibility of improvement of jet engine diagnostic by applying neural networks (in Serbian). In: Proceeding of XLVII ETRAN Conference, Herceg Novi, June 8-13, Vol. I.

Singh, C. & Wang, L. 2008. Role of Artificial Intelligence in the Reliability Evaluation of Electric Power Systems. Turkish Journal of Electrical Engineering and Computer Science, 16(3), [online]. Available at: http://journals.tubitak.gov.tr/elektrik/issues/elk-08-16-3/elk-16-3-2-0808-7.pdf [Accessed: 15.01.2021].

-University of Cambridge. 2016. Enhancing the reliability of artificial intelligence [online]. Available at: https://phys.org/news/2016-10-reliability-artificial-intelligence.html [Accessed: 15.01.2021].

-Uptake. 2020. How AI is Making Predictive Maintenance a Reality for the Industrial IoT [online]. Available at: https://www.uptake.com/blog/how-ai-is-making-predictive-maintenance-a-reality-for-the-industrial-iot [Accessed: 15.01.2021].

Yao, Y., Yang, Y., Wang, Y. & Zhao, X. 2019. Artificial intelligence-based hull structural plate corrosion damage detection and recognition using convolutional neural network. Applied Ocean Research, 90(art.number:101823). Available at: https://doi.org/10.1016/j.apor.2019.05.008.

Zhao, Y., Li, T., Zhang, X. & Zhang, C. 2019. Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future. Renewable and Sustainable Energy Reviews, 109, pp.85-101. Available at: https://doi.org/10.1016/j.rser.2019.04.021.

Objavljeno
2021/06/23
Rubrika
Originalni naučni radovi