Certain Investigations on Advanced Signal Processing and K-Medoid Cluster for Faults Quality Analysis in Textile Industry

Fabric fault detection in textile manufacturing requires accurate and efficient methods to ensure reliable quality control under varying production conditions. Image and signal processing techniques are utilized to enhance defect-related features through noise reduction, contrast adjustment, and improved data representation for analysis. An integrated approach combining feature enhancement and K-medoids clustering is employed to classify defective and non-defective fabric regions, providing robustness against noise, variability, and outliers commonly present in industrial environments. The clustering process relies on representative medoid patterns, enabling consistent separation of fault characteristics from normal fabric structures. A point-to-multipoint communication mechanism is incorporated to support efficient transmission of inspection data from acquisition units to monitoring systems, facilitating real-time fault detection and analysis. Experimental evaluation conducted on damaged fabric samples demonstrates detection accuracy of up to 96%, along with reduced processing time and operational cost compared to conventional fault detection techniques. Machine learning transforms business management by enabling data-driven decision making, optimizing operations, improving efficiency, and supporting scalable and adaptive organizational processes. The integration of enhancement, feature extraction, and clustering improves the reliability and consistency of defect identification while minimizing dependency on manual inspection processes. This framework supports scalable deployment in textile production environments and contributes to automated quality assessment by ensuring faster, more accurate, and cost-effective fault detection.