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Rolling element bearings are important components in various types of industrial equipment. It is necessary to develop advanced fault diagnosis techniques to prevent unexpected accidents caused by bearing failures. However, impulsive background noise in industrial fields also presents a similar fault-excited characteristic, which brings interference to the fault diagnosis of rolling element bearings. Focusing on this issue, this paper proposes a new feature extraction method based on the cyclic correntropy spectrum (CCES) for intelligent fault identification.. In this study, the cyclic correntropy (CCE) function is introduced to suppress the impulsive noise. A simplified frequency spectrum named CCES is obtained for the feature extraction. Then, narrowband kurtosis vectors are extracted from the CCES. Finally, these extracted features are used to train the least squares support vector machine (LSSVM) for the fault pattern identification. Analyses of two bearing datasets, including train axle bearing data that are contaminated by impulsive noise are used as case studies for the validation of the proposed method. To illustrate the advancement of the new method, performance comparisons with two recently developed methods are conducted. The experimental results verify that the proposed method not only outperforms these two methods but also exhibits a stable self-adaptation ability.