摘要
为解决间歇性故障数据采集困难、特征提取困难的问题,提出了基于FSV的模拟电路健康状态识别的新方法。首先建立模拟电路间歇性故障仿真模型;其次,利用FSV算法获取模拟电路故障数据特征值;最后,将提取的特征训练HSMM模型,建立基于HSMM的模拟电路健康状态分类器,从而实现设备健康状态的识别。实验结果表明,该方法能够有效识别模拟电路健康状态,为模拟电路健康状态识别开辟新的途径。
In order to solve the problem of intermittent fault data acquisition and feature extraction,a new method of analog circuit health state recognition based on feature selection verification is proposed.Firstly,the intermittent fault simulation model of analog circuit is established.Secondly,FSV algorithm is used to obtain the fault data eigenvalue of analog circuit.Finally,the extracted features are trained in HSMM model,and a classifier of analog circuit health state based on HSMM is established to realize the recognition of equipment health state.The experimental results show that this method can effectively identify the health status of analog circuits,and open up a new way for the health status identification of analog circuits.
作者
过攀科
李玉晓
GUO Panke;LI Yuxiao(School of Science,Jiangxi University of Science and Technology,Ganzhou 341000,China)
出处
《现代信息科技》
2020年第10期28-31,共4页
Modern Information Technology
基金
江西省教育厅科学技术研究项目(GJJ170526)。
