Abstract: Research on fault state identification of rolling bearings is of critical significance for ensuring normal operation and production safety of rotating machinery, enhancing equipment reliability, and mitigating economic losses. Aiming at the problem of rolling bearing fault state identification, a diagnostic method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and particle swarm optimization-support vector machine (PSO-SVM) is proposed. First, CEEMDAN is used to decompose the bearing vibration signal to obtain multiple Intrinsic Mode Functions (IMF) and a residual term. Then, the appropriate IMF is selected to extract five signal entropy indexes including power spectral entropy, energy entropy, approximate entropy, sample entropy, and fuzzy entropy, to construct the fault feature set; and finally combine with the PSO-SVM torealize the fault state identification. To verify the effectiveness and reliability of the model, fault diagnosis experiments were conducted for SKF6203 bearings with normal, outer-ring fault, inner-ring fault and rolling-body fault, and for ER-16K bearings with normal, outer-ring fault, inner-ring fault, rolling-body fault and inner-ring-outer-ring composite fault, the correlation between signal entropy features and fault signals was analyzed, and the superiority of the algorithm was verified through comparative tests. The results show that CEEMDAN can effectively realize the preprocessing of bearing signals and improve the signal-to-noise ratio; the fault feature set composed of five entropy indexes can effectively represent the essence of bearing faults; the accuracy of this fault diagnosis method for fault state identification on the Case Western Reserve University bearing dataset and the Southeastern University bearing dataset was 94% and 92.8%, and with small fluctuation in accuracy. The combination of CEEMDAN, entropy features, and PSO-SVM achieves effective identification of rolling bearing faults in rotating machinery, the fault diagnosis method can provide a strong guarantee for the reliable operation of numerous rotating machines in coal preparation plants and promote the intelligent construction process of coal preparation plants.