Research on Intelligent Diagnosis of Bearing Faults Based on Improved ChaosNet with Small Samples
A new method of bearing fault diagnosis based on improved neurochaos learning(neurochaos learning +AdaBoost,NL-AdaBoost)is proposed.First,the fast fourier transform(FFT)of the time-domain vibration signal is performed to extract the frequency-domain features,and the one-dimensional feature sam-ples are obtained by splicing the time-frequency-domain signals;then,the input signal generates excitation to the chaotic GLS neurons to form ChaoFEX features,which are fed to the integrated learning classifier(Ada-Boost);subsequently,the bearing fault feature samples,and do k-fold cross-validation on the sample set to obtain the optimal hyperparameter values of the model,which are applied to the test set for model classifica-tion capability validation;finally,in a small-sample comparison experiment,the macro F1-score of the model is compared with four common deep learning algorithms.The experimental results demonstrate that under the low training sample condition,NL-AdaBoost,the model has good accuracy and generalization ability.
small sample trainingneural chaos learningrolling bearingfault diagnosis
NETL
NSTL
万方数据
维普
