The article presents an adaptive algorithm for forming training and test datasets for the ANFIS system, used to diagnose the technical condition of electrical equipment. A key feature of the proposed approach is the consideration of temporal dependencies and anomalous data, which enhances the accuracy and completeness of identifying faulty equipment states. The process of testing the algorithm on synthetic data, including vibration, temperature, current, and voltage parameters, is described. The conducted analysis shows that adaptive data partitioning improves the system's ability to identify anomalies compared to the classical method of dataset partitioning. The algorithm is highly applicable for equipment diagnostics in industries where it is crucial to account for dynamic changes in parameters and rare anomalous events.To assess the algorithm's efficiency, it was compared with traditional dataset partitioning methods. The experiment demonstrated that the proposed method enhances the accuracy of classifying anomalous equipment states. Additionally, the algorithm reduces the likelihood of false positives when detecting faults. A notable feature of the development is its ability to adapt to various types of equipment, making it a universal solution for diagnostics in different industrial sectors. The algorithm's future applications are related to its integration into predictive maintenance and monitoring systems, which will increase equipment reliability and reduce repair and maintenance costs.
1. Genin V., Markarova E. Technical diagnostics of power grid equipment. Vestnik Chuvashskogo universiteta. 2017;(1):213–217. (In Russ.).
2. Gagarina L.G., Rubtsov Yu.V. Features of the development of a method for classifying flat QFN packages for use as part of design-for-manufacturing systems in microelectronics industry. Proceedings of Universities. Electronics. 2022;27(3):322–332. (In Russ.).
3. Saushev A.V., Sherstnev D.A., Shirokov N.V. Analysis of methods of diagnostics of high voltage apparatus. Vestnik gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S.O. Makarova. 2017;9(5):1073–1085. (In Russ.).
4. Korobeinikov A.B., Sarvarov A.S. Analysis of Existing Methods for Diagnostics of Electric Motors and Perspectives of Their Development. Electrotechnical Systems and Complexes. 2015;(1):4–9. (In Russ.).
5. Kuznetsov M.Yu., Kozhevnikov A.V. Intelligent method for determining the remaining life electrical equipment. Modern scientific researches and innovations. 2013;(12). (In Russ.). URL: https://web.snauka.ru/issues/2013/12/29800
6. Bazyl’ I.M. Prediction of the technical state of electrical power systems. News of the Tula state university. Technical sciences. 2011;(6-1):89–93. (In Russ.).
7. Akbar S.,Vaimann T., Asad B., Kallaste A., Sardar M.U., Kudelina K. State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions. Energies. 2023;16(17). https://doi.org/10.3390/en16176345
8. Kudelina K., Vaimann T., Asad B., Rassõlkin A., Kallaste A., Demidova G. Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning. Applied Sciences. 2021;11(6). https://doi.org/10.3390/app11062761
9. Capisani L.M., Ferrara A., Ferreira de Loza A., Fridman L.M. Manipulator Fault Diagnosis via Higher Order Sliding-Mode Observers. IEEE Transactions on Industrial Electronics. 2012;59(10):3979–3986. https://doi.org/10.1109/TIE.2012.2189534
10. Cheng F., He Q.P., Zhao J. A novel process monitoring approach based on variational recurrent autoencoder. Computers & Chemical Engineering. 2019;129. https://doi.org/10.1016/j.compchemeng.2019.106515
11. Camarena-Martinez D., Osornio-Rios R., Romero-Troncoso R.J., Garcia-Perez A. Fused Empirical Mode Decomposition and MUSIC Algorithms for Detecting Multiple Combined Faults in Induction Motors. Journal of Applied Research and Technology. 2015;13(1). https://doi.org/10.1016/S1665-6423(15)30014-6
12. Dai X., Gao Z. From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis. IEEE Transactions on Industrial Informatics. 2013;9(4):2226–2238. https://doi.org/10.1109/TII.2013.2243743
13. Sobczyk T.J., Tulicki J., Weinreb K., Mielnik R., Sułowicz M. Characteristic Features of Rotor Bar Current Frequency Spectrum in Cage Induction Machine with Inner Faults. In: 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 27–30 August 2019, Toulouse, France. IEEE; 2019. pp. 115–120. https://doi.org/10.1109/DEMPED.2019.8864907
Chirkov Andrew Vladimirovich
ORCID |
National Research University "Moscow Institute of Electronic Technology"
Moscow, Russian Federation
