Motor fault diagnosis using attention mechanism and improved adaboost driven by multi-sensor information

Fault diagnosis driven by the single signal has been widely used in motor fault diagnosis, but it can not meet the diagnostic requirements of complex motor system. In this study, a motor fault diagnosis method using attention mechanism and improved AdaBoost driven by multi-sensor information is proposed. Firstly, the corresponding frequency domain feature information is obtained by Hilbert transform and Fourier transform in different signals. The improved AdaBoost multi-classification classifier is then used to train signals from different sources and obtain sub classifier results. Finally, a dynamic weight distribution matrix is used to obtain the final diagnosis results with sub classifiers. The proposed method is verified by current, magnetic and vibration signals. The results show that the proposed method dynamically evaluates sensitivity of different detection signals to different faults. Compared with the conventional method, the proposed method can enhance the robustness, generalization ability and accuracy of fault diagnosis.

Automated summary

A motor fault diagnosis method using attention mechanism and improved AdaBoost driven by multi-sensor information is proposed in this study, which can dynamically evaluate the sensitivity of different detection signals to different faults. Compared with the conventional method, the proposed method can enhance the robustness, generalization ability, and accuracy of fault diagnosis.