Abstract:Computed tomography camera (CT) spiral machines in the field of medical applications face extremely high difficulties in actual fault localization and detection due to their complex structure and high integration. To address this issue, an analysis was conducted on the fault localization and detection of CT spiral machines, and a multi label ensemble learning method was proposed. This method uses a half search algorithm to obtain fault data of CT spiral machines, while effectively combining existing convolutional neural networks and recurrent neural networks for text representation. Through an adaptive label relationship enhancement method, the dependency relationships between labels are identified, and the imbalanced learning of weighted reduced label sets can effectively eliminate problems such as low model scalability and weak model generalization. The test results of five indicators, including loss value, accuracy, running time, accuracy, and sensitivity, show that the methods proposed in the study have more advantages compared to the other three innovative multi label ensemble learning methods, and the improvement values all exceed 2%. The data of each indicator in the training set are higher than the corresponding values in the test set. The accuracy of the multi label ensemble learning method for spatiotemporal network clustering reduction in the training set and test set is 93.12% and 87.26%, respectively, with recall rates of 86.35% and 84.25%. This method can accurately and quickly identify the types and locations of faults in CT spiral machines, greatly reducing maintenance costs and extending the service life of the equipment.
