Aiming at the problem that the fault information of rolling bearings is difficult to extract from the non-stationary vibration signals from complex noise background

and the poor classification precision of traditional methods

a fault diagnosis method of rolling bearings based on energy feature extraction of ensemble empirical mode decomposition（ EEMD） and classification diagnosis of an optimized extreme learning machine（ ADCSELM） is proposed. Firstly

based on the adaptive decomposition ability of EEMD to nonlinear and non-stationary signals

the bearing fault signals are decomposed into an intrinsic mode function set（ IMFs） which containing full of fault features

and the fault feature vectors are extracted. Then

the cuckoo search algorithm（ ADCS）

which is optimized by an adaptive dynamic search step strategy

is used to determine connection weights and the hidden layer threshold of the ELM. The optimal weights and thresholds can be obtained by training the ELM with the extracted fault feature vectors. Finally

the bearing fault diagnosis simulation experiments are carried out based on the ADCS-ELM to verify the effectiveness of the proposed method. Results show that

compared with the BP

LVQ and ELM methods

ADCS-ELM can improve the classification accuracy of the four typical bearing faults

and also have faster diagnosis speed.