Cascade Neuro-Fuzzy System Architecture for Classification of Renewable Energy Facilities Defects
Keywords:
intelligent monitoring, renewable energy, defect classification, neuro-fuzzy systems, cascade architecture, FLVQ, Fuzzy BSB, Wang–Mendel methodAbstract
The article proposes a hybrid cascaded neuro-fuzzy system for classifying defects in renewable energy facilities based on multisensor data. The system architecture implements a sequence of computational levels (from sensor acquisition and preprocessing to result integration) and combines a compact feature representation with adaptive decision-making logic. The formation of a compact feature space is carried out using a modified convolutional neural network, after which the initial classification is performed using the hypersector FLVQ method. In cases of increased uncertainty, the expert modules Fuzzy BSB and the modified Wang–Mendel method are activated, which ensures robustness and explainability of the results. Experimental studies have shown that the proposed system provides consistently high Accuracy values (over 94%), balanced Precision and Recall indicators for all defect classes. Analysis of the discrepancy matrix showed that the main errors occur between the “erosion” and “corrosion” classes, which is explained by the similarity of their textural characteristics. The results obtained confirm the effectiveness of the cascade architecture and its feasibility for practical application in automated monitoring systems for renewable energy facilities.
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