Bearing fault signal extraction is susceptible to strong background noise in the working environment,especially in the early fault signal detection,bearing fault signal is submerged by noise,resulting in limited detection.In view of the traditional adaptive stochastic resonance theory in bearing fault signal detection parameters optimization of a single defect,put forward a cuckoo algorithm to optimize stochastic resonance parameters based on the bearing fault detection algorithms,this method takes the output signal-to-noise ratio as fitness function,the theory of stochastic resonance in the two coordinate parameter optimization,get a set of optimal parameters,Adaptive stochastic resonance is best matched with input signal,noise and nonlinear system.Finally,through simulation comparison,the singal detection result of the proposed algorithm is better than that of the tradition stochastic resonance method.Experimental data of bearing fault diagnosis show that the detection error of bearing fault signals achieved by this algorithm is 0.15%.Experimental results show that the proposed method has the adwantages of high accuracy and good reliability,which is of great significance to the accurate detection of bearing faults and the stable operation of industrial equipment.