| 要約: | To address the issues of information redundancy, limited feature representation, and empirically set parameters in rolling bearing fault diagnosis, this paper proposes a Multi-Entropy Screening and Optimization Temporal Convolutional Network (MESO-TCN). The method integrates feature filtering, network modeling, and parameter optimization into a unified diagnostic framework. Specifically, ensemble empirical mode decomposition (EEMD) is combined with a hybrid entropy criterion to preprocess the raw vibration signals and suppress redundant noise. A kernel-extended temporal convolutional network (ETCN) is designed with multi-scale dilated convolution to extract diverse temporal fault patterns. Furthermore, an improved whale optimization algorithm incorporating a firefly-inspired mechanism is introduced to adaptively optimize key hyperparameters. Experimental results on datasets from Xi’an Jiaotong University and Southeast University demonstrate that MESO-TCN achieves average accuracies of 99.78% and 95.82%, respectively, outperforming mainstream baseline methods. These findings indicate the method’s strong generalization ability, feature discriminability, and engineering applicability in intelligent fault diagnosis of rotating machinery.
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