中国医疗设备2024，Vol.39Issue(3)：46-52,7.DOI:10.3969/j.issn.1674-1633.2024.03.008
大型医疗监护设备电池健康状态检测算法研究
Research on Battery Health Status Detection Algorithm for Large Medical Monitoring Equipment
摘要
Abstract
Objective To research a detection algorithm for the health status of batteries in large medical monitoring equipment,aimed at detecting the health status of batteries and addressing issues such as time-varying effects and fault diversity caused by temperature changes,charging and discharging cycles during use.Methods The voltage variation of the battery during charging and discharging was analyzed,and three health factors such as constant voltage drop discharge time,battery internal resistance and constant interval discharge time series were extracted.It was trained into a nonlinear regression model based on nonlinear autoregressive with exogenous inputs model neural network to estimate the battery capacity of large medical monitoring equipment.The backpropagation neural network was improved by particle swarm optimization algorithm to detect the state of health of the battery.Results The experimental results showed that the detection error of this method was small;The correlation between the three health factors and the estimated battery capacity of large medical monitoring equipment was higher than 0.95,and the estimated battery capacity was accurate.Conclusion Through this method,battery problems can be detected in time,and measures can be taken in advance to reduce equipment downtime caused by battery failures and reduce the risk of medical errors.关键词
大型医疗监护设备/电池健康状态/健康因子/非线性自回归模型神经网络Key words
large medical monitoring equipment/battery health status/health factor/NARX neural network分类
医药卫生引用本文复制引用
邱筱岷,王志禹,王小花..大型医疗监护设备电池健康状态检测算法研究[J].中国医疗设备,2024,39(3):46-52,7.基金项目
江苏省卫健委卫生财务研究项目(CW2020098). （CW2020098）
