Fault diagnosis of rolling bearings based on multi-source data fusion
Addressing the issue that single sensor data is insufficient to fully characterize the fault state information of rolling bearings,leading to suboptimal fault diagnosis results,a fault diagnosis method for rolling bearings based on multi-source data fusion is proposed.To enable the model to fully characterize the equipment's operational state information,vibration and current signals are combined through channel concatenation to construct multi-channel data.Meanwhile,to suppress irrelevant information interference in multi-channel data,a Convolutional Block Attention Module(CBAM)is introduced into the Self-Calibrated Convolutional(SCConv)neural network for adaptive weighting of different channel data.In a series of comparative experiments,the proposed method achieved a classification accuracy of 100%,demonstrating excellent robustness and adaptability.
rolling bearingsfault diagnosismulti-source data fusionconvolutional block attentionSelf-Calibrated Convolutional
万方数据
维普
