文献详情
Performance Assessment and Prediction for Superheterodyne Receivers Based on Mahalanobis Distance and Time Sequence Analysis
文献类型期刊
作者Sun, Jinwen[1];Lu, Chen[2];Wang, Manxi[3];Yuan, Hang[4];Qi, Le[5]
机构
通讯作者Qi, L (reprint author), Beihang Univ, Sch Reliabil & Syst Engn, Beijing, Peoples R China.; Qi, L (reprint author), Sci & Technol Reliabil & Environm Engn Lab, Beijing, Peoples R China.
来源信息年:2017  
期刊信息INTERNATIONAL JOURNAL OF ANTENNAS AND PROPAGATION影响因子和分区  ISSN:1687-5869
增刊正刊
摘要The superheterodyne receiver is a typical device widely used in electronics and information systems. Thus effective performance assessment and prediction for superheterodyne receiver are necessary for its preventative maintenance. A scheme of performance assessment and prediction based on Mahalanobis distance and time sequence analysis is proposed in this paper. First, a state observer based on radial basis function (RBF) neural network is designed to monitor the superheterodyne receiver and generate the estimated output. The residual error can be calculated by the actual and estimated output. Second, time-domain features of the residual error are then extracted; after that, the Mahalanobis distance measurement is utilized to obtain the health confidence value which represents the performance assessment result of the most recent state. Furthermore, an Elman neural network based time sequence analysis approach is adopted to forecast the future performance of the superheterodyne receiver system. The results of simulation experiments demonstrate the robustness and effectiveness of the proposed performance assessment and prediction method.
收录情况SCIE(WOS:000405714800001)  
所属部门可靠性与系统工程学院
DOI10.1155/2017/6458954
百度学术Performance Assessment and Prediction for Superheterodyne Receivers Based on Mahalanobis Distance and Time Sequence Analysis
语言外文
被引频次11
人气指数114
浏览次数114
基金State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, China; Fundamental Research Funds for the Central Universities [YWF-17-BJ-J-42]; National Natural Science Foundation of China [51575021, 51605014]
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