骨科临床护理医疗器械设备内部高风险故障检测研究  

Rrsearch on the Detection of High-risk Failures Within Orthopedic Clinical Care Medical Device Equipment

作　　者：薛艳霞 孟俊霞 张惠慧 XUE Yan-xia;MENG Jun-xia;ZHNG Hui-hui(Department of Infectious Diseases,the First Hospital of Qiaoxi District,Zhangjiakou City,Zhangjiakou 075000 China;Gaojiaying Central Health Center,Chongli District,Zhangjiakou City,Zhangjiakou 075000 China;Medical Insurance Office of Infectious Disease Hospital of Zhangjiakou City,Zhangjiakou 075000 China)

机构地区：[1]张家口市桥西区第一医院感染科,河北张家口075000 [2]张家口市崇礼区高家营中心卫生院,河北张家口075000 [3]张家口市传染病医院医保办,河北张家口075000

出　　处：《自动化技术与应用》2024年第3期39-42,共4页Techniques of Automation and Applications

摘　　要：骨科临床护理医疗器械内部存在多种智能设备连接结构,在检测高风险故障时,容易获取大量的故障噪声数据,导致故障检测效果不佳,针对该问题,设计一种骨科临床护理医疗器械设备的内部高风险故障检测方法。采集医疗器械设备内部振动信号数据后,分解器械设备振动信号,处理信号参数为估计值,分解降噪处理高风险故障数据,采用梯度下降算法计算器械高风险故障产生的器械损失,构建故障检测算法。实验结果表明:所设计的故障检测方法得到的AUC(AUC数值即为ROC曲线下的面积,ROC曲线为受试者工作特征曲线)参数进一步优化,实际的检测效果较好。There are many kinds of intelligent device connection structures in orthopedic clinical nursing medical devices.When detecting high-risk faults,it is easy to obtain a large number of fault noise data,which leads to poor fault detection effect.Aiming at this problem,a high-risk fault detection method for orthopedic clinical nursing medical devices is designed.After collecting the internal vibration signal data of medical devices,the vibration signal of medical devices is decomposed,and the signal parameters are processed as the estimated values.The high-risk fault data is decomposed and denoised.The gradient descent algorithm is used to calculate the device loss caused by high-risk fault of medical devices,and the fault detection algorithm is constructed.The experimental results show that the AUC parameters obtained by the designed fault detection method are further optimized,and the actual detection effect is better.

关 键 词：骨科临床护理 医疗器械设备 高风险故障 AUC参数 

分 类 号：TP206.3[自动化与计算机技术—检测技术与自动化装置]