Cross-condition quantitative diagnosis method for bearing faults based on IDRSN-ECDAN

Abstract

The development of cross-condition quantitative diagnosis methods for estimating bearing faults based on deep transfer learning technology is considered of great importance for practical applications. However, the existing intelligent quantitative diagnostic methods suffer from noise interference in the vibration data and require the utilization of historical data. Therefore, in this work, a cross-condition quantitative diagnostic method for estimating the bearing faults based on an improved deep residual shrinkage network—entropy conditional domain adversarial network (IDRSN-ECDAN) was proposed. First, a sub-network was added to the residual module to construct a residual shrinkage module, which reduced the noise interference in vibration signals. Next, DropBlock layers were added to the deep residual shrinkage network, and the Adamax optimizer was adopted to improve the diagnostic ability of the model further. Finally, the IDRSN was combined with the ECDAN to transfer the effective information from the source domain data to the target domain through adversarial training. The proposed method was used to systematically analyse a bearing dataset with 15 different fault sizes from a doubly-fed wind turbine test platform, and its superiority was demonstrated through performing several experiments on cross-condition bearing fault quantitative diagnosis.