中文摘要：

针对在无故障样本情况下如何快速检测设备异常度问题,在引入自己空间边界样本概念的基础上,提出一种自适应超环检测器.在描述自适应超环检测器生成算法的基础上,以Iris数据集为例进行分析,发现与已有的异常检测方法相比,自适应超环检测器异常检测方法在区分有较清晰类边界数据时,具有更好检测性能.利用自适应超环检测器异常检测方法分析轴承状态数据,不仅能反映出轴承的各种状态,而且能通过设备的异常程度反映出同类故障的轻重程度.基于自适应超环检测器的设备异常度检测方法,是在学习设备正常运行数据的基础上,寻找自己空间的边界样本,结合其方位信息与自己样本半径,建立能完全覆盖状态空间的自适应超环检测器,不需要设备运行的故障数据,它适合对故障数据缺乏的设备进行有效的异常检测.

英文摘要：

The adaptive hyper-ring detector is presented based on introduce the term boundary samples of self-space,which can detect the abnormal degree of equipment rapidly without fault sample.After description of the generating algorithms of adaptive super-ring detector,take the Iris data set as an example for analysis,and then find the equipment abnormal degree detection approach based on adaptive hyper-ring detector shows a better detection performance by comparison with other commonly used anomaly detection methods where the data sets has a clear boundary.It not only reflects the various states of bearing,but also reflects the fault degree pass the abnormal degree of the same equipment failure when analyzed the bearing state data used this method.The adaptive hyper-ring detector can detect the faults of equipment by learning normal data without fault data,who built with boundary samples,their boundary location information and the self-radius,which can completely cover the state space.It can efficiently detect the faults of the equipment that lacks fault data.