The Application Effect of Fault Prediction Model for High Value Parts of Medical Accelerator Based on One-Dimensional Convolutional Neural Network
Objective To construct a high-value component fault prediction model for medical accelerators to achieve prediction of high-value component faults.Methods With the selection of 60 sets of 381 maintenance record data from January 2013 to December 2017 for the Elekta Synergy medical accelerator,these data were randomly assigned to the training set(42 groups)and the testing set(18 groups)in a 7‥3 ratio.With a one-dimensional convolutional neural network used for binary classification modeling,30 sets of data were randomly selected as the validation set to evaluate the performance of the model,and the prediction effect of the model was tested by using the test set data.Results With a maximum training and learning times of 120 times,when the actual number of training exceeds 80 times,the data tended to be stable.In addition,the accuracy of the training and validation sets remained stable at around 90%,while the accuracy of the test set data was above 96%,indicating good model convergence.Conclusion The predicted failure times of high value parts of medical accelerators were close to the actual situation,which provided reliable data support for preventive maintenance and warranty service procurement.
One dimensional convolutional neural networkMedical acceleratorHigh-value partsFault prediction model
NETL
NSTL
万方数据
