An Improved Fault Diagnosis Method and Its Application in Compound Fault Diagnosis for Paper Delivery Structure Coupling

The coupling torque signal contains essential information about the operating condition of the motor‐follower mechanical system. Artificial Intelligence methods have been effective in diagnosing coupling faults. However, due to internal and external excitations caused by the operating environment and neighboring components, existing coupling fault diagnosis models often struggle with poor feature extraction, low recognition accuracy, and weak generalization performance. To overcome these limitations, a multihead self‐attention mechanism‐enhanced empirical mode decomposition (EEMD)–convolutional neural network (CNN)–bidirectional long short‐term memory (BiLSTM) model is proposed. Empirical mode decomposition (EMD) is first applied to extract spatial features from the data. Next, the multihead self‐attention mechanism captures essential internal characteristics. CNN is then used for further spatial feature extraction, and BiLSTM is employed to extract temporal features, enabling effective spatiotemporal feature fusion. Torque and vibration signals of three typical coupling faults—loosening, rough contact, and misalignment—are collected using a motor‐coupling‐paper handling mechanism testbed and compared with traditional models, including long short‐term memory (LSTM), CNN, and Random Forest. The proposed method shows an average F 1‐Score improvement of 31.43%, 40.07%, and 31.71%, respectively, indicating superior noise robustness and better generalization. Furthermore, the dimensionality reduction results of each network layer are visualized using the T‐stochastic neighbor embedding (T‐SNE) method, which reveals clear feature patterns and confirms the model’s reliability and effectiveness.