Defects Prediction Method for Radiographic Images Based on Random PSO Using Regional Fluctuation Sensitivity

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 12; p. 5679
• Main Authors: Shang, Zhongyu, Li, Bing, Chen, Lei, Zhang, Lei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.06.2023; MDPI
• Subjects: Algorithms; COVID-19; Deep learning; defect prediction; Defects; Design; Efficiency; Entrapment; Entropy; Feature selection; image processing; Machine learning; Mathematical optimization; Methods; Optimization; PSO; radiographic testing; Sensitivity; Statistical methods; turbine blades; Velocity; image processing; radiographic testing; defect prediction; turbine blades; PSO
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23125679

This paper presents an advanced methodology for defect prediction in radiographic images, predicated on a refined particle swarm optimization (PSO) algorithm with an emphasis on fluctuation sensitivity. Conventional PSO models with stable velocity are often beleaguered with challenges in precisely pinpointing defect regions in radiographic images, attributable to the lack of a defect-centric approach and the propensity for premature convergence. The proposed fluctuation-sensitive particle swarm optimization (FS-PSO) model, distinguished by an approximate 40% increase in particle entrapment within defect areas and an expedited convergence rate, necessitates a maximal additional time consumption of only 2.28%. The model, also characterized by reduced chaotic swarm movement, enhances efficiency through the modulation of movement intensity concomitant with the escalation in swarm size. The FS-PSO algorithm's performance was rigorously evaluated via a series of simulations and practical blade experiments. The empirical findings evince that the FS-PSO model substantially outperforms the conventional stable velocity model, particularly in terms of shape retention in defect extraction.