Biotechnological Applications in Intelligent Nondestructive Testing for Fault Detection in Automated Equipment

Abstract

The nondestructive detection of faults in automated equipment is a critical approach to reducing the high costs associated with equipment downtime and maintenance. This study focuses on the application of biotechnological innovations in enhancing intelligent nondestructive testing (NDT) methods. Specifically, X-ray imaging technology, coupled with biotechnologically inspired algorithms, is utilized to improve fault detection accuracy in automated systems. By leveraging compressed sensing theory, the correlation between pixels in X-ray detection images is analyzed to reduce sampling points, with image sparsity achieved through discrete wavelet transform. High-quality X-ray images are obtained and processed using a novel Sobel-Gabor edge fault identification method, which introduces the Gabor transform into the Sobel operator. This integration enhances feature extraction across multiple angles by utilizing the multi-scale capabilities of the Gabor filter. Additionally, for the detection of tilted faults, fault features are extracted using mathematical geometry methods, with fault presence characterized by changes in tilt rate. The study demonstrates that the Sobel-Gabor algorithm significantly improves recognition success rates, achieving 0.51 for bubble defects, 0.74 for crack defects, and 0.89 for metallic foreign bodies and absence of defects. Although bubble defect recognition remains challenging, the success rate for detecting metallic foreign matter and no defects is notably higher. The incorporation of biotechnological principles, such as bio-inspired algorithms and advanced imaging techniques, into NDT methods offers promising advancements in the reliability and efficiency of fault detection in automated equipment, aligning with the goals of enhancing industrial processes through biotechnology.