Алгоритм формирования обучающих и тестовых выборок для анализа характера данных
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Algorithm of formation of training and test samples for data character analysis

idChirkov A.V.

UDC 681.5
DOI: 10.26102/2310-6018/2024.47.4.014

  • Abstract
  • List of references
  • About authors

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.

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Chirkov Andrew Vladimirovich

ORCID |

National Research University "Moscow Institute of Electronic Technology"

Moscow, Russia

Keywords: ANFIS, neuro-fuzzy model, adaptive dataset formation, equipment diagnostics, time series, anomalous data, industrial diagnostics, electrical equipment

For citation: Chirkov A.V. Algorithm of formation of training and test samples for data character analysis. Modeling, Optimization and Information Technology. 2024;12(4). URL: https://moitvivt.ru/ru/journal/pdf?id=1663 DOI: 10.26102/2310-6018/2024.47.4.014 (In Russ).

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Full text in PDF

Received 15.10.2024

Revised 24.10.2024

Accepted 06.11.2024