Application of BP Neural Network Based on Particle Swarm Optimization Algorithm in Fault Diagnosis of CT Scanner Bulb Tube

Objective A particle swarm optimization (PSO) algorithm based optimized BP neural network fault diagnosis method for CT scanner bulb tube is proposed for identifying typical faults such as filament open circuit, filament half open circuit, scanning noise and arc discharge.

Methods Key parameters such as anode voltage, current, bulb temperature, scanning time, and current fluctuation amplitude are selected, and the data set is constructed based on 357 sets of fault samples, which are divided into training set and test set. PSO is used to optimize the initial weights and thresholds of BP neural network, and PSO-BP model is constructed for fault classification.

Results The classification accuracies of the model on the training and test sets are 96.25% and 92.31%, respectively, and the Pearson correlation coefficients of each fault type range from 0.894 to 0.971, indicating a strong linear relationship between the fault features and categories. Compared with the traditional methods, the PSO-BP model has obvious advantages in terms of accuracy and robustness.

Conclusion PSO-based BP neural network can effectively improve the diagnostic performance of typical faults of CT bulb tube, which has a good application prospect and provides a new method to support the intelligent diagnosis of CT equipment.