针对在无故障样本情况下如何快速检测设备异常度问题，在约简自己空间边界样本数量的基础上，提出一种约简边界样本界面检测器。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，约简边界样本界面检测器是一种具有高检测率高误报警率的异常检测方法，而且具有很强的数据压缩功能，尤其是在区分有较清晰类边界数据时，具有更好的检测性能。利用约简边界样本界面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。约简边界样本界面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，找到自己空间的边界样本，并根据一定规则将其约简后，结合其方位信息与训练样本半径，进行设备状态检测，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。

The interface detector with reduction boundary samples(RI-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. Take the Iris data set as examples for analysis, and then find that RI-detector is an abnormal detection method with high detection rate, high false alarm rate and strong compression capability for data. RI-detector shows a better detection performance by comparison with other commonly used anomaly detection methods; especially 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 RI-detector. The RI-detector can detect the faults of equipment by learning normal data without fault data, which is built with reduction boundary samples, their boundary location information and the self-radius. It can efficiently detect the faults of the equipment that lacks fault data.