摘要
为了提高产后康复治疗仪故障在线监测能力,提出基于小波变换的产后康复治疗仪故障在线监测方法。构建产后康复治疗仪故障在线监测的信号检测辨识模型,结合故障信号期望函数构建故障监测信号的多分辨间隔采样模型,对采集的产后康复治疗仪故障信号进行谱特征分离,实现对故障信号的增强处理,采用自适应加权控制的方法,对产后康复治疗仪故障监测信号实现频谱特征分解,从而构建故障监测信号的特征提取模型,采用多尺度小波变换结合故障监测信号特征,计算故障信号输出包络特征系数,进行故障信号的在线诊断判决,通过时频特征点聚类分析得到故障特征点聚类结果,采用小波变换方法实现对故障监测过程中的时频转换分析,提高故障检测分辨输出能力。仿真结果表明,采用该方法进行产后康复治疗仪故障监测的实时性较好,对故障特征的准确辨识能力较强,提高了产后康复治疗仪故障在线监测性能。
In order to improve the on-line monitoring capability of the postpartum rehabilitation therapy device failure,an online monitoring method for the postpartum rehabilitation therapy device failure based on wavelet transform is proposed.Construct a signal detection and identification model for the online fault monitoring of postpartum rehabilitation therapy equipment,combine the expected function of the fault signal to construct a multi-resolution interval sampling model of the fault monitoring signal,and perform spectral feature separation of the collected fault signals of the postpartum rehabilitation therapy equipment to realize the enhancement of the fault signal Processing,using the method of adaptive weighted control,to realize the spectral feature decomposition of the fault monitoring signal of the postpartum rehabilitation treatment instrument,thereby constructing the feature extraction model of the fault monitoring signal,using the multi-scale wavelet transform combined with the fault monitoring signal characteristics to calculate the fault signal output envelope The characteristic coefficients are used for online diagnosis and judgment of fault signals.The clustering results of fault characteristic points are obtained through time-frequency characteristic point clustering analysis.Wavelet transform method is used to realize the time-frequency conversion analysis in the fault monitoring process and improve the fault detection resolution output capability.The simulation results show that the use of this method for postpartum rehabilitation therapy instrument fault monitoring has better real-time performance,and has a strong ability to accurately identify fault characteristics,and improves the performance of postpartum rehabilitation therapy instrument fault online monitoring.
作者
乔志红
QIAO Zhihong(Department of Obstetrics and gynecology,Hengshui Fifth People's Hospital,Hengshui Hebei 053000,China)
出处
《自动化与仪器仪表》
2021年第3期214-217,共4页
Automation & Instrumentation
基金
河北省中医药管理局科研计划项目(No.2017323)。
关键词
小波变换
产后康复治疗仪
故障检测
特征聚类分析
故障特征提取
wavelet transform
postpartum rehabilitation therapy instrument
fault detection
feature cluster analysis
fault feature extraction
