Unbalanced Data Bearing Fault Diagnosis Based on SC-DCGAN
In order to address the problem of low diagnostic accuracy of minority class samples due to data unbalance in the rolling bearing fault diagnosis process,a diagnosing method for imbalance data was proposed based on statistical feature condition generative adversarial network.The statistical characteristics of vibration signals were introduced into the condition generative adversarial network to obtain a new fusion condition model,which could guide the generator to generate more data matching the real sample distribution to bal-ance the data set,and then the convolutional network model was used to classify and identify the balanced dataset.Experiments were conducted on the bearing dataset from a equipment fault diagnosis key laboratory,considering various unbalanced ratios.The results show that compared with other models,the proposed method can effectively handle the unbalanced fault classification problem and the identi-fication ability of the minority class samples is improved.
class imbalancefault diagnosiscondition generative adversarial networkstatistical feature
NETL
NSTL
万方数据
