Adaptive Fusion Transfer Learning-based Digital Multitwin-assised Intelligent Fault Diagnosis

With the development of digital twin technology, mechanical failures can be more comprehensively described through the interaction between sensors and twin models, which thereby demonstrate significant potential in fault diagnosis. However, due to the limitations of modeling methods, digital twin model construction poses challenges and exhibits poor generalization, making it difficult to apply across domains. To address these issues, this paper proposes a digital multitwin fusion diagnostic algorithm based on transfer learning. The digital multitwin comprise the source domain mechanistic twin, the target domain distributional feature 1D Auxiliary Classifier GAN (1D-ACGAN) twin, and the target domain time-series feature shared weight stacking LSTM (SWSLSTM) twin. First, based on the fault features collected by multiple sensors in the mechanism twin, a multichannel, multiscale mid-fusion diagnostic network is constructed. Second, the network is hierarchically transferred to the target domain network using a frozen transfer technique, automatically finding the optimal network parameters. Furthermore, to enhance the data quality, a data twin composed of the SWSLSTM twin and 1D-ACGAN twin is proposed to capture the dependency relationships of target domain data features. Finally, the migrated network adaptively adjusts the generated samples of the data twin based on the designed indicators, achieving high-precision cross-domain application of the multitwin diagnostic model. The proposed algorithm has been validated utilising data from triple-piston pumps and openly accessible bearing datasets, achieving diagnostic accuracies of 91.6% and 95.28%, respectively. These results substantiate the efficacy of the algorithm, demonstrating its superior generalisation capability and robustness in comparison to extant classical methodologies.