Performance analysis of model for fault diagnosis in radiation-hardened memories using SVDD-PSO

Radiation-hardened memories are extensively used in critical commercial applications such as nuclear power plants, industrial systems, and space missions for reliable data storage and retention. While numerous algorithms have been proposed for diagnosing faults in these memories, most focus less on fault optimization. To address this gap, this paper presents a performance analysis of a fault diagnosis model based on the Support Vector Data Description-Particle Swarm Optimization (SVDD-PSO) algorithm for fault optimization. The proposed fault diagnosis model is designed using specific parameters tailored for radiation-hardened memories. The results demonstrate that the methodology achieves higher accuracy, optimal fitness value, and reduced time penalty when diagnosing fault samples. The model's performance is further evaluated using logistic regression, with a Receiver Operating Characteristic (ROC) curve accuracy of 96%, reflecting a balanced trade-off between the true positive rate (TPR) and false positive rate (FPR). The higher TPR and lower FPR confirm that the proposed model is well-suited for fault diagnosis in radiation-hardened memory systems.