Software tool for implementing an intelligent method for analyzing wind turbine blade defects with limited dataset
DOI:
https://doi.org/10.30837/2522-9818.2026.2.029Keywords:
wind turbines; defects; neuro-fuzzy systems; Wang–Mendel; PythonAbstract
The subject of the study is methods and software tools for intelligent diagnostics of wind turbine blade defects based on neuro-fuzzy models capable of operating effectively in conditions of limited, fuzzy, or partially defined input data. The goal of the study is to develop a software implementation and conduct an experimental study of a neuro-fuzzy Wang–Mendel network for classifying wind turbine blade defects, and to justify its feasibility as the basis of an intelligent system for monitoring the technical condition of energy facilities. The main tasks are to analyze modern methods of defect classification, develop the architecture of a neuro-fuzzy model, create a software package for training and classification, implement an algorithm for automatically forming a fuzzy rule base, optimize the parameters of membership functions, and conduct experimental studies to assess the effectiveness of the proposed approach. The study used methods of fuzzy logic, neuro-fuzzy systems, mathematical modeling, data classification, parameter optimization, and software implementation of intelligent systems in Python using modern data processing and numerical computing libraries. The Wang–Mendel algorithm provides automatic formation of fuzzy rules based on training data and adaptation of model parameters to increase classification accuracy. As a result, an intelligent software complex for classifying wind turbine blade defects was developed, which provides effective data processing and decision-making based on fuzzy rules. Experimental studies were conducted on a training sample of 160 records and a test sample of 64 records, describing the probabilities of the presence of various types of defects. Conclusions: according to the training results, an accuracy of 91% was achieved on the training sample and 94% on the test sample with a low level of mean square error, which confirms the high efficiency, generalization ability, and practical suitability of the proposed model for use in intelligent systems for monitoring the technical condition of wind turbines in conditions of limited data and resources.Downloads
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