With the enhancement of safety performance requirements in the car manufacturing field, the quality standards for the sheet molding process have imposed higher demands. However, during the pressure-building phase of pump-controlled hydraulic die cushion systems, the combined effects of high-order dynamics, system uncertainties, and strong nonlinearities pose significant challenges to maintaining precise control and dynamic response performance of the blank holder force (BHF). To address these challenges, we propose an intelligent model predictive control (MPC) strategy that synergistically integrates an extended state observer (ESO) for disturbance compensation with parameters optimized by a genetic algorithm (GA). The mathematical model and state-space model of the system are established. Subsequently, the ESO is integrated with MPC to enable active compensation for internal and external disturbances. The GA is employed to optimize the controller parameters within the MPC framework. Finally, a simulation testbed for the pump-controlled hydraulic die cushion experimentally validates the process. Experimental results demonstrate that compared to MPC and conventional PID control, the proposed strategy achieves significant reductions in pressure overshoot (0.87% and 1.8% at 100 bar; 3.3% and 5.9% at 200 bar), pressure-building time (13.9% and 31.4% at 100 bar; 6.7% and 11.5% at 200 bar), and stroke length (10.5% and 32% at 100 bar; 11.5% and 28.1% at 200 bar). This validates its effectiveness in enhancing both control precision and dynamic response performance, providing a reliable solution for large-scale applications of pump-controlled hydraulic die cushions in high-dynamic stamping scenarios.

In contemporary industrial machinery, bearings are a vital component, so the ability to diagnose bearing faults is extremely important. Current methodologies face challenges in feature extraction and perform suboptimally in environments with high noise levels. This paper proposes an enhanced, multimodal, feature-fusion-bearing fault diagnosis model. Integrating a 1DCNN-dual MLP framework with an enhanced two-way cross-attention mechanism enables in-depth feature fusion. Firstly, the raw fault time-series data undergo fast Fourier transform (FFT). Then, the original time-series data are input into a multi-layer perceptron (MLP) and a one-dimensional convolutional neural network (1DCNN) model. The frequency-domain data are then entered into the other multi-layer perceptron (MLP) model to extract deep features in both the time and frequency domains. These features are then fed into a serial bidirectional cross-attention mechanism for feature fusion. At the same time, a GCOS learning rate scheduler has been developed to automatically adjust the learning rate. Following fifteen independent experiments on the Case Western Reserve University bearing dataset, the fusion model achieved an average accuracy rate of 99.83%. Even in a high-noise environment (0 dB), the model achieved an accuracy rate of 90.66%, indicating its ability to perform well under such conditions. Its accuracy remains at 86.73%, even under 0 dB noise and variable operating conditions, fully demonstrating its exceptional robustness.

Fault diagnosis in axial piston pumps is key to ensuring the proper operation of a hydraulic system. Slipper wear, as a typical fault in piston pumps, is challenging to accurately diagnose because the faults are very similar for different forms and degrees of wear. The achievement of accurate fault diagnosis of different forms and degrees of wear in the slipper will greatly improve the reliability of axial piston pump operation and, at the same time, provide new ideas for research into similar fault diagnosis problems in other rotating machinery. Therefore, a method of fault diagnosis based on the following symmetric dot pattern (SDP) and multi-channel densely connected convolutional networks (DenseNet) is proposed in this paper. The method applies an SDP transformation to transform the slipper failure signal into an SDP image, which achieves the fusion of triaxial vibration signals and enriches the signal features. The inception module is improved by replacing the original structure with larger convolutional kernels in multiple branches and decomposing the larger convolutional kernels. The inception module, the convolutional block attention module (CBAM), and the DropBlock method are introduced into DenseNet to improve feature extraction capability, computational efficiency, and model generalization ability. Experiments are performed on several slipper wear fault SDP image datasets, and all the indices produced by the proposed method are higher than those of the traditional convolutional neural networks, which fully proves the effectiveness and superiority of the procedure.