Deep capsule networks based on dual heterogeneous feature resonance fusion for mechanical fault diagnosis

Feature extraction and fusion are important for the fault diagnosis and prediction of rotating machinery. While traditional deep learning networks can learn single attribute of features, they still face difficulties in capturing heterogeneous features with distinct attributes during the fusion process. To solve this problem, a novel type of capsule networks (CapsNets) based on dual heterogeneous feature resonance fusion is presented for heterogeneous feature extraction and fault diagnosis. Firstly, a dual-scale deformable convolution network is proposed to extract dual heterogeneous features. Then, an adaptive heterogeneous feature adjustment mechanism is presented to adjust the weights of heterogeneous features and identify discriminative features. Next, a resonance fusion mechanism is constructed to coordinate and select correlated heterogeneous features in both structural and spatial dimensions, avoiding information conflicts in feature fusion. Lastly, the heterogeneous resonance gain features are introduced into the CapsNet for fault diagnosis and classification tasks. The superiority of the proposed network lies in its ability to integrate and coordinate global and local information, enhancing the correlation between heterogeneous features for improved performance. Comparative experiments on multiple datasets with the state-of-the-art methods demonstrate that the proposed method excels in extracting and fusing dual heterogeneous features under complex operating conditions and noise interference.