基于天气雷达标准输出控制器的故障诊断方法研究
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摘要
根据天气雷达标准输出控制器采集的雷达关键指标数据,提出了一种具有自学习且半监督作用的异常检测与支持向量机(SVM)的联合故障诊断方法,实现天气雷达运行状态评估与故障检测定位。针对采集数据,首先使用异常检测算法建立概率模型,计算样本落入正常范围的概率,实现非正常样本的识别;其次,以所得样本的概率值作为支持向量机模型的新增特征,建立SVM分类器,对故障进行诊断。经实验表明与传统的逻辑回归、神经网络的分类方法相比,在小样本且各类别训练数据正负偏离过大的情况下,此方法能够更准确、高效地诊断雷达故障。
Based on the Weather Radar data Standard Output Controller(WRSOC), acombination fault detection algorithm is proposed in this paper,which is anomaly detection and support vector machine(SVM) with self-learning and semi-supervisory function, andit realizes the operation state evaluation and fault detection of weather radar. Firstly,building probability model with anomaly detection algorithm, calculate the probability of samples falling into the normal range, and realize the recognition of abnormal samples. Secondly, using the probability value of samples as the new feature of support vector machine model, the SVM classifier model is established to diagnose faults. Experiments show that this method can diagnose radar faults more accurately and efficiently than traditional logistic regression and neural network in the case of small samples and large deviation of training data.
Based on the Weather Radar data Standard Output Controller(WRSOC), acombination fault detection algorithm is proposed in this paper,which is anomaly detection and support vector machine(SVM) with self-learning and semi-supervisory function, andit realizes the operation state evaluation and fault detection of weather radar. Firstly,building probability model with anomaly detection algorithm, calculate the probability of samples falling into the normal range, and realize the recognition of abnormal samples. Secondly, using the probability value of samples as the new feature of support vector machine model, the SVM classifier model is established to diagnose faults. Experiments show that this method can diagnose radar faults more accurately and efficiently than traditional logistic regression and neural network in the case of small samples and large deviation of training data.
引文
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[2] 王晗中,杨江平,张爱元.现代雷达装备综合智能故障诊断系统设计[J].现代雷达,2008,30(11):22-25.
[3] 王玉松.基于SVM的雷达故障预诊断技术研究[J].舰船电子工程,2011,31(8):149-151.
[4] 顾佳,安帅,张杜玮.基于异常检测算法的动车组牵引电机故障预测[J].设备管理与维修,2018,422(8):184-185.
[5] 尚朝轩,韩壮志,胡文华.基于状态监测与信息融合的雷达装备故障趋势预测[J].火力与指挥控制,2011,36(2):152-155.
[6] 庄夏.一种基于增强学习神经网络的雷达故障诊断方法[J].现代雷达,2017(12):15-19.
[7] 陈世杰,连可,王厚军.采用多信号流图模型的雷达接收机故障诊断方法[J].电子科技大学学报,2009,38(1):87-91.
[8] Chen Y W,Lin C J.Combining SVMs with Various Feature Selection Strategies[J].Feature Extraction,2006,207.
[9] Liao W,Rosenhahn B,Yang M Y.Gaussian Process for Activity Modeling and Anomaly Detection[J].ISPRS Annals of Photogrammetry,Remote Sensing & Spatial Informa,2015,II-3/W5:467-474.
[10] ChandolaV,Banerjee A,Kumar V.Anomaly Detection:A Survey[J].ACM Computing Surveys,2009,41(3).
[11] 李鹏.测量雷达智能诊断技术研究[D].沈阳:东北大学,2009.
[12] 王杰,何建新.故障树分析法在新一代天气雷达故障诊断中的应用[J].南京信息工程大学学报(自然科学版),2013,5(2):147-153.
[13] 曾涛,刘伟,龚熙.省级气象装备保障一体化系统设计[J].成都信息工程大学学报,2018,33(5):544-547.
[14] 刘昉,丁明星,张先俊,等.新一代天气雷达方位旋转关节电弧引起无源限幅器故障分析[J].成都信息工程大学学报,2017,32(2):141-146.