基于一维卷积神经网络构建医用直线加速器高价值零件故障预测模型的应用效果
The Application Effect of Fault Prediction Model for High Value Parts of Medical Accelerator Based on One-Dimensional Convolutional Neural Network
傅世楣1
作者信息
- 1. 福州大学(福建福州 350108);福建省肿瘤医院(福建福州 340015)
- 折叠
摘要
目的 构建医用直线加速器高价值零件故障预测模型,以实现对高价值零件故障的预判.方法 选取2013年1月至2017年12月医院在用医科达Synergy医用直线加速器的60组共381个维修记录数据,按照7‥3比例随机分配为训练集(42组)和测试集(18组),采用一维卷积神经网络进行二分类建模,随机选取 30 组数据作为验证集评估模型性能,并采用测试集数据检测模型预测效果.结果 设定最大训练学习次数为 120 次,实际训练次数超过 80 次时数据趋于稳定,训练集和验证集的准确率均稳定于 90%左右,测试集数据准确率均在 96%以上,表明模型收敛较好.结论 该模型预测医用直线加速器高价值零件的故障次数与实际情况接近,为预防性维修和保修服务采购提供了可靠的数据支持.
Abstract
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.
关键词
一维卷积神经网络/医用直线加速器/高价值零件/故障预测模型Key words
One dimensional convolutional neural network/Medical accelerator/High-value parts/Fault prediction model引用本文复制引用
出版年
2024
NETL
NSTL
万方数据