Abstract: Aiming at the problem that the fault signal of rolling bearings is weak and vulnerable to noise interference, resulting in low accuracy of fault diagnosis, a fault diagnosis method for rolling bearings optimized by the Northern Eagle Algorithm （NGO） using stochastic resonance （SR） and Extreme Learning Machine （ELM） is proposed. Firstly, taking the negative value of the signal-to-noise ratio of the output signal as the fitness function, NGO is used to adaptively optimize the key parameters of SR, and the optimized SR is used to enhance the weak fault signal. Secondly, extract the fuzzy entropy of signals of different fault types as fault features; Finally, the fuzzy entropy features are input into the extreme learning machine for fault classification to complete fault diagnosis. Through experimental analysis, the proposed method can achieve a relatively high accuracy rate of fault diagnosis.