USING ARTIFICIAL INTELLIGENCE APPLICATIONS IN SENSOR RELIABILITY CALCULATIONS
Keywords:
artificial intelligence, sensor reliability, machine learning, failure prediction, predictive maintenance, data mining, Digital twin, IoT, industrial sensor systemsAbstract
This article presents the results of an analytical research on the use of artificial intelligence for sensor reliability calculations. Modern sensors play a key role in cyber-physical systems, the Internet of Things, industrial automation, and medicine, where measurement reliability determines operational efficiency and safety. Traditional statistical reliability assessment methods (Weibull models, exponential laws, Markov chains) often prove insufficient in complex systems and large data volumes. In recent years, artificial intelligence methods have been actively used to improve the accuracy of sensor reliability prediction. Machine learning and deep learning enable the analysis of large data sets, failure prediction, anomaly detection, and sensor degradation modeling. The use of AI in reliability calculations paves the way for a transition from reactive to predictive maintenance, reducing costs and increasing equipment service life.
References
1. Jardine, A. K. S., Lin, D., & Banjevic, D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 2006. 20(7), 1483-1510.
2. Zhang, Y., Wang, K., Yang, T. Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Systems Journal, 2019. 13(3), 2213-2227
3. Lei, Y., Li, N., Guo, L. Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 2018, 104, 799-834.
4. Wang, T., Yu, J., Siegel, D., & Lee, J. A similarity-based prognostics approach for remaining useful life estimation of engineered systems. Proceedings of the International Conference on Prognostics and Health Management. 2008.
5. IEEE Sensors Journal, IEEE Transactions on Industrial Informatics, IEEE Internet of Things Journal (2020-2024 articles on the application of AI in sensor systems).
6. A. Abid, M.T. Khan, J. Iqbal. A review on fault detection and diagnosis techniques: basics and beyond. Artif. Intell. Rev., 54 (2021), pp. 3639-3664, doi:10.1007/s10462-020-09934-2.
7. M.Z. Ali, M. Shabbir, X. Liang, Y. Zhang. Machine learning-based fault diagnosis for single- and multi-faults in induction motors using measured stator currents and vibration signals. IEEE Trans. Ind. Appl., 55 (2019), pp. 2378-2391, doi:10.1109/TIA.2019.2895797.
8. A.N. Angelopoulos, S. Bates. Conformal prediction: a gentle introduction. Found. Trends® Mach. Learn., 16 (2023), pp. 494-591, doi:10.1561/2200000101.
9. F.A. Assis, A.J.C. Coelho, L.D. Rezende, A.M. Leite da Silva, L.C. Resende Unsupervised machine learning techniques applied to composite reliability assessment of power systems. Int. Trans. Electr. Energy Syst., 31 (2021), doi:10.1002/2050-7038.13109.
10. A. Ayodeji, M.A. Amidu, S.A. Olatubosun, Y. Addad, H. Ahmed. Deep learning for safety assessment of nuclear power reactors: reliability, explainability, and research opportunities. Prog. Nucl. Energy, 151 (2022), Article 104339, 10.1016/j.pnucene.2022.104339.
11. M. das C. Moura, E. Zio, I.D. Lins, E. Droguett. Failure and reliability prediction by support vector machines regression of time series data. Reliab. Eng. Syst. Saf., 96 (2011), pp. 1527-1534, 10.1016/j.ress.2011.06.006.
12. G. Ou, Y.L. Murphey. Multi-class pattern classification using neural networks. Pattern Recogn., 40 (2007), pp. 4-18, 10.1016/j.patcog.2006.04.041.
13. M. Payette, G. Abdul-Nour. Machine learning applications for reliability engineering: a review. Sustainability, 15 (2023), p. 6270, 10.3390/su15076270
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Muradova Alevtina Aleksandrovna, Khidirova Charos Murodillayevna, Makhkamov Farkhod Aloviddin o‘g‘li
This work is licensed under a Creative Commons Attribution 4.0 International License.
