摘要：Most fault detection methods based on the assumption of working in stationary or approximate stationary conditions are limited under varying operation conditions, for that the frequency aliasing phenomenon is inevitable in the spectrum. Therefore, in order to handle the problem of fault diagnosis under non-stationary conditions, researchers have proposed numerous methods and some achievements have been obtained. In this article, a new feature extraction method is proposed for fault diagnosis of rolling bearings under varying speed conditions. Based on the assumption that the energy will increase when balls cross over fault position, frequency values are divided by instantaneous speed and arranged in the descending order of corresponding amplitude to form a new fault feature array, that is, the ratio of frequency to instantaneous speed reconfiguration arrays. Thereafter, the Euclidean distance classifier is utilized for recognition. The efficacy of the proposed method is demonstrated by simulated and experimental data. Categorized results show that the new approach is capable of handling the bearing fault classification under varying speed conditions.
