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A fault diagnosis method of rolling bearing based on particle filter state estimation is proposed. The method mainly includes two steps: fault model establishment and fault identification. In the fault model establishment part, the corresponding autoregressive model is established according to the vibration signals of the rolling bearings in different fault states, which is used as the fault model; In the fault recognition part, the corresponding model in the normal state is transformed into the state space model, and the particle filter is designed. Then, the related features of the residuals are extracted by estimating the different fault states. Combined with the model parameters, the BP neural network identification algorithm is used to identify the faults. Finally, the validity of the method is verified by the vibration data of rolling bearing of Case Western Reserve University.