Railway vehicle bearings risk monitoring based on normal region estimation for no-fault data situations

Abstract A risk monitoring method based on normal region estimation (NRE) is systematically proposed for the actual situation of the lack of fault data in the condition identification and monitoring of railway vehicle bearings. First, the basic concept of normal domain theory is expounded, and the formal expression of normal domain is given. Secondly, the academic thoughts and implementation steps of risk monitoring based on NRE are summarized. Then, two algorithms based on convex hull and support vector data description (SVDD) are proposed respectively to solve the core problem of boundary estimation. Finally, the rolling-bearing vibration acceleration data was used for the experiment, and the performance of the two algorithms is compared. The results show that both algorithms are effective. In contrast, the convex hull algorithm is faster, and the SVDD algorithm is smoother and more flexible. In practical applications, the two algorithms can be selected according to different requirements of real time and accuracy.