基于改进数据挖掘算法的医用康复设备故障行波检测方法  

Fault Traveling Wave Detection Method for Medical Rehabilitation Equipment Based on Improved Data Mining Algorithm

作　　者：孙惠丽[1] SUN Huili(Tianjin Medical Devices Quality Supervision and Testing Center,Tianjin 300384,China)

机构地区：[1]天津市医疗器械质量监督检验中心,天津300384

出　　处：《微型电脑应用》2025年第1期304-308,共5页Microcomputer Applications

摘　　要：伴随着医疗体系的改革与发展,医疗服务水平也在不断提升,对医用康复设备的要求也更加严格。医疗康复设备故障的发生是影响其应用性能的关键所在,提出基于改进数据挖掘算法的医用康复设备故障行波检测方法。深入分析医用康复设备故障行波信号的特性,以此为基础,提取故障行波信号,并消除其内部噪声信号,应用改进数据挖掘算法——小波包算法深度挖掘故障行波信号的潜在信息,即故障行波信号特征矢量,制定医用康复设备故障定位程序,执行制定程序即可实现医用康复设备故障的行波检测与定位。实验数据显示:应用所提方法获得的行波信号挖掘深度系数最大值为0.99,医用康复设备故障定位误差最小值2%,充分证实了所提方法应用性能更佳,适合大力推广与应用。With the reform and development of the medical system,the level of medical service is also improving,and the requirements for medical rehabilitation equipment are more stringent.The occurrence of medical rehabilitation equipment fault is the key to affect its application performance,this paper proposes a fault traveling wave detection method for medical rehabilitation equipment based on improved data mining algorithm.Deeply analyze the characteristics of the fault traveling wave signal of medical rehabilitation equipment.Based on this,this paper extracts the fault traveling wave signal and eliminates its internal noise signal.The improved data mining algorithm-wavelet packet algorithm is applied to deeply mine the potential information of the fault traveling wave signal,the feature vector of the fault traveling wave signal.The algorithm formulates the fault location procedure of medical rehabilitation equipment,executes the established procedure to realize the detection and location of the fault traveling wave of medical rehabilitation equipment.The experimental data shows that the maximum mining depth coefficient of traveling wave signal obtained by the proposed method is 0.99,and the minimum fault location error of medical rehabilitation equipment is 2%,which fully proves that the proposed method has better application performance and is suitable for vigorous promotion and application.

关 键 词：医用康复设备 故障检测 故障信息挖掘 改进数据挖掘算法 故障行波 检测精度 

分 类 号：TM773[电气工程—电力系统及自动化]