Belt conveyor roller detection method based on genetic algorithm feature selection and stacking learning

Abstract A belt conveyor roller defect detection method based on genetic algorithm feature selection and stacking learning was proposed. Roller defect data from 1620 samples of three types of roller defects was first collected. To improve the detection efficiency of roller defects, a feature selection method based on a genetic algorithm for selecting roller defect features was proposed. The model classification efficiency of roller features after feature selection by the genetic algorithm was found to be higher. The feature subset with the highest fitness had a feature number of 35. A stacking learning model for detecting roller defects was also constructed. The stacking method enhanced the comprehensive performance of the base learner model for detecting roller defects. The stacking learning model finally achieved a classification accuracy of 94.86% on the roller defect dataset. The classification performance of the stacking learning model was better than that of its base learning model. The results showed that the stacking learning model was preferred for detecting roller defects. The proposed conveyor belt roller defect detection method, while achieving high accuracy, may be limited by potential overfitting and sensitivity to environmental conditions. The next steps in refining the conveyor belt roller defect detection method will focus on dataset expansion, algorithm parameter tuning, and environmental robustness testing.