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To precisely determine the type of bearing fault, the paper has proposed the solution by combining first-order difference of signals and coarse-graining. When a bearing fault occurs, vibration signals are often accompanied by impact features and meanwhile, greater difference among adjacent points of signals corresponds to more evident impact features. Compared with original vibration signals, the first-order difference of signals works better in measuring this kind of impact feature. Therefore, the solution proposed in the paper takes first-order difference in place of original signals and combines with coarse-graining algorithm for subsequent treatment. Compared with average value of signals, the variance of signal can represent the discreteness of signal which describes the impact feature of bearing fault from the other side. Therefore, the solution has coarse-graining operation for difference signals in improved coarse-graining method with variance as criterion to further highlight impact features of rolling bearing. Secondly, a feature set has been established to comprehensively represent fault information of bearing with the average of coarse-graining sequence as feature parameter. Finally, the type of bearing fault is identified according to the fault feature set and support vector machine (SVM). To verify the effectiveness and universality of solution proposed by the paper, a comparison is given to proposed solution and classical method according to dataset of faults of rotor and rolling bearing, and public dataset. The result indicates that with different rotate speeds and types of faults, the proposed solution is more precise to identify the type of bearing faults; in two different datasets, the average identification rate of the proposed solution is 96.43% (aeroengine–rotor–rolling bearing fault dataset) and 100% (public dataset) for unknown samples. [ABSTRACT FROM AUTHOR]