Bearings fault diagnosis method based on multi-pathed hierarchical mixture-of-experts model
In response to the issue of low accuracy in handling complex work conditions in rolling bearing fault diagnosis,a Multi-Task Learning(MTL)model naming as Multi-pathed Hierarchical Mixture-of-Experts(MHMoE),and the corresponding hierarchical training mode were proposed.In this model,by combining multi-stage,multi-task joint training,a hierarchical information sharing mode was achieved.The model's generalization and fault recognition accuracy were further improved on the basis of the ordinary MTL mode,enabling the model to perform tasks on both complex and simple datasets excellently.Meanwhile,by incorporating the bottleneck layer structure of one-dimensional ResNet,the depth of the network was ensured while avoiding issues such as vanishing and exploding gradients,so as to extract relevant features of the dataset fully.Experimental results on the Paderborn University bearing fault dataset(PU)as the test dataset demonstrate that under varying degrees of working complexity,compared to the OMoE(One-gate Mixture-of-Experts)-ResNet18 model without MTL,the proposed model has the accuracy improved by 5.45 to 9.30 percentage points.Compared to the models such as Ensemble Empirical Mode Decomposition Hilbert spectral transform(EEMD-Hilbert),MMoE(Multi-gate Mixture-of-Experts),and Multi-Scale multi-Task Attention Convolutional Neural Network(MSTACNN),the proposed model has the accuracy improved by 3.21 to 16.45 percentage points at least.
万方数据
维普
