Online monitoring methods have been widely used in many major devices, however the normal and abnormal states of equipment are estimated mainly based on the monitoring results whether monitored parameters exceed the s...Online monitoring methods have been widely used in many major devices, however the normal and abnormal states of equipment are estimated mainly based on the monitoring results whether monitored parameters exceed the setting thresholds. Using these monitoring methods may cause serious false positive or false negative results. In order to precisely monitor the state of equipment, the problem of abnormality degree detection without fault sample is studied with a new detection method called negative potential field group detectors（NPFG-detectors）. This method achieves the quantitative expression of abnormality degree and provides the better detection results compared with other methods. In the process of Iris data set simulation, the new algorithm obtains the successful results in abnormal detection. The detection rates for 3 types of Iris data set respectively reach 100%, 91.6%, and 95.24% with 50% training samples. The problem of Bearing abnormality degree detection via an abnormality degree curve is successfully solved.展开更多
为了提高实时性和精确度,提出一种利用角点动能检测群体异常行为的方法.首先,利用金字塔Lucas-Kanade光流法计算FAST(Features from Accelerated Segment Test)角点光流,筛选出运动的角点;然后,利用k均值方法聚类图像中的角点,自适应地...为了提高实时性和精确度,提出一种利用角点动能检测群体异常行为的方法.首先,利用金字塔Lucas-Kanade光流法计算FAST(Features from Accelerated Segment Test)角点光流,筛选出运动的角点;然后,利用k均值方法聚类图像中的角点,自适应地调整正常行为角点动能,定义每一类的局部异常程度为角点平均动能与正常时的比值,整体运动异常程度为局部异常程度之和;最后,如果整体异常程度大于异常阈值为异常行为,否则为正常行为.实验结果表明:该方法能够检测出多种群体异常行为且实时性强于Harris、SIFT(Scale-Invariant Feature Transform)和SURF(Speed Up Robust Features)角点,精确度高于光流法、社会力法和图分析法.展开更多
主要研究设备异常度的检测问题。在无故障样本的情况下,如何快速检测设备异常度已经成为状态检测的重要问题。为此,提出一种用于设备异常和异常度检测的信息匹配检测方法——可变阈值信息检测器(Variable threshold information detecto...主要研究设备异常度的检测问题。在无故障样本的情况下,如何快速检测设备异常度已经成为状态检测的重要问题。为此,提出一种用于设备异常和异常度检测的信息匹配检测方法——可变阈值信息检测器(Variable threshold information detector,VTI-detector),在分析分散增量理论并提出数据分布相亲有限信息密度概念的基础上,计算了每个正常训练样本对自己样本(正常样本)的匹配阈值,建立了带有匹配阈值信息的矩阵,即确立VTI-detector。最后利用确立的VTI-detector,结合相亲有限信息密度的概念,提出了设备异常度的计算公式。以UCI数据库中的Iris数据集为例,将所提出的异常检测方法与其他三种常用的异常检测方法进行对比,显示VTI-detector具有比其他方法更好的检测性能。利用异常度公式在线计算轴承正常和各种故障状态的异常度,并以异常度曲线的形式进行显示,结果表明故障异常度检测效果明显,具有较好的应用前景。展开更多
基金Supported by National Natural Science Foundation of China(Grant No.51175316)Specialized Research Fund for the Doctoral Program of Higher Education,China(Grant No.20103108110006)Basic Research Project of Shanghai Science and Technology Commission,China(Grant No.11JC1404100)
文摘Online monitoring methods have been widely used in many major devices, however the normal and abnormal states of equipment are estimated mainly based on the monitoring results whether monitored parameters exceed the setting thresholds. Using these monitoring methods may cause serious false positive or false negative results. In order to precisely monitor the state of equipment, the problem of abnormality degree detection without fault sample is studied with a new detection method called negative potential field group detectors（NPFG-detectors）. This method achieves the quantitative expression of abnormality degree and provides the better detection results compared with other methods. In the process of Iris data set simulation, the new algorithm obtains the successful results in abnormal detection. The detection rates for 3 types of Iris data set respectively reach 100%, 91.6%, and 95.24% with 50% training samples. The problem of Bearing abnormality degree detection via an abnormality degree curve is successfully solved.
文摘为了提高实时性和精确度,提出一种利用角点动能检测群体异常行为的方法.首先,利用金字塔Lucas-Kanade光流法计算FAST(Features from Accelerated Segment Test)角点光流,筛选出运动的角点;然后,利用k均值方法聚类图像中的角点,自适应地调整正常行为角点动能,定义每一类的局部异常程度为角点平均动能与正常时的比值,整体运动异常程度为局部异常程度之和;最后,如果整体异常程度大于异常阈值为异常行为,否则为正常行为.实验结果表明:该方法能够检测出多种群体异常行为且实时性强于Harris、SIFT(Scale-Invariant Feature Transform)和SURF(Speed Up Robust Features)角点,精确度高于光流法、社会力法和图分析法.
文摘主要研究设备异常度的检测问题。在无故障样本的情况下,如何快速检测设备异常度已经成为状态检测的重要问题。为此,提出一种用于设备异常和异常度检测的信息匹配检测方法——可变阈值信息检测器(Variable threshold information detector,VTI-detector),在分析分散增量理论并提出数据分布相亲有限信息密度概念的基础上,计算了每个正常训练样本对自己样本(正常样本)的匹配阈值,建立了带有匹配阈值信息的矩阵,即确立VTI-detector。最后利用确立的VTI-detector,结合相亲有限信息密度的概念,提出了设备异常度的计算公式。以UCI数据库中的Iris数据集为例,将所提出的异常检测方法与其他三种常用的异常检测方法进行对比,显示VTI-detector具有比其他方法更好的检测性能。利用异常度公式在线计算轴承正常和各种故障状态的异常度,并以异常度曲线的形式进行显示,结果表明故障异常度检测效果明显,具有较好的应用前景。
