Abstract: 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.

Key words: adaptive hyper-ring detector;abnormal degree;anomaly detection;bearing, Analytical Model, Complex Warping, Reverse C-warped, The Spline Finite Element Model