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作 者:胡雷[1] 胡茑庆[1] 秦国军[1] 夏鲁瑞[1]
机构地区:[1]国防科技大学机电工程与自动化学院,湖南长沙410073
出 处:《国防科技大学学报》2010年第2期119-123,共5页Journal of National University of Defense Technology
基 金:国家自然科学基金资助项目(50675219);湖南省杰出青年科学基金资助项目(08JJ1088)
摘 要:为了在故障样本稀缺、故障模式不完备的情况下监控涡轮泵状态,并剔除传感器失效故障造成的虚警,提出用于涡轮泵状态监控及传感器故障识别的单类支持向量机新异类检测方法。该方法以正常状态为目标类构建单类支持向量机检测器,用于检测涡轮泵是否出现异常;以传感器故障为目标类构建单类支持向量机检测器,用于判断检测到的异常是否属于传感器故障。对涡轮泵试车数据的分析结果表明了该方法的有效性。In order to monitor the condition of a liquid rocket engine turbopump in the case of lacking fault samples and prior knowledge about fault modes, and to eliminate the false alarms caused by sensor faults, a novelty detection method based on one-class support vector machine (OCSVM) is introduced. An OCSVM is trained on the basis of normal samples, and is used as turbopump condition monitor. Another OCSVM is trained on the basis of sensor fault samples and is used as sensor fault detector. Turbopump condition monitor detects whether novel events may occur, while sensor fault detector identifies whether novel events detected are caused by sensor faults. The validity of this turbopump condition monitoring and sensor fault recognition method is verified with historical test data.
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