To address the limitation of single acceleration sensor signals in effectively reflecting the health status of rolling bearings,a rolling bearing fault diagnosis method based on the fusion of data-level and feature-le...To address the limitation of single acceleration sensor signals in effectively reflecting the health status of rolling bearings,a rolling bearing fault diagnosis method based on the fusion of data-level and feature-level information was proposed.First,according to the impact characteristics of rolling bearing faults,correlation kurtosis rules were designed to guide the weight distribution of multi-sensor signals.These rules were then combined with a weighted fusion method to obtain high-quality data-level fusion signals.Subsequently,a feature-fusion convolutional neural network(FFCNN)that merges the one-dimensional(1D)features extracted from the fused signal with the two-dimensional(2D)features extracted from the wavelet time-frequency spectrum was designed to obtain a comprehensive representation of the health status of rolling bearings.Finally,the fused features were fed into a Softmax classifier to complete the fault diagnosis.The results show that the proposed method exhibits an average test accuracy of over 99.00%on the two rolling bearing fault datasets,outperforming other comparison methods.Thus,the method can be effectively utilized for diagnosing rolling bearing faults.展开更多
基金The National Natural Science Foundation of China(No.U22A20178)National Key Research and Development Program of China(No.2022YFB3404800)Jiangsu Province Science and Technology Achievement Transformation Special Fund Program(No.BA2023019).
文摘To address the limitation of single acceleration sensor signals in effectively reflecting the health status of rolling bearings,a rolling bearing fault diagnosis method based on the fusion of data-level and feature-level information was proposed.First,according to the impact characteristics of rolling bearing faults,correlation kurtosis rules were designed to guide the weight distribution of multi-sensor signals.These rules were then combined with a weighted fusion method to obtain high-quality data-level fusion signals.Subsequently,a feature-fusion convolutional neural network(FFCNN)that merges the one-dimensional(1D)features extracted from the fused signal with the two-dimensional(2D)features extracted from the wavelet time-frequency spectrum was designed to obtain a comprehensive representation of the health status of rolling bearings.Finally,the fused features were fed into a Softmax classifier to complete the fault diagnosis.The results show that the proposed method exhibits an average test accuracy of over 99.00%on the two rolling bearing fault datasets,outperforming other comparison methods.Thus,the method can be effectively utilized for diagnosing rolling bearing faults.
