Combining generalized autocorrelation-difference function (GADF) and residual neural network (ResNet) can realize the composite fault diagnosis of axial piston pump. Here's the general way to do this: 1. Data collection: collect the vibration or acoustic data of the axial piston pump during operation under various working conditions and fault conditions. Make sure the data is labeled with the appropriate failure type for training and testing purposes. 2. Preprocessing: Apply necessary preprocessing techniques to the raw vibration data, such as filtering to remove noise and artifacts, and normalization to standardize the data. This step ensures that the data is in a format suitable for subsequent analysis. 3. Feature extraction using GADF: This method extracts features from the preprocessed vibration signal using the generalized autocorrelation-difference function (GADF). GADF is a time-frequency analysis technique that captures the time-frequency characteristics of a signal. The system converts time-domain signals into two-dimensional images, highlighting patterns associated with faults. A GADF image represents the frequency distribution of a signal over time and can be used as an input feature for fault diagnosis. 4. Data preparation: Divide the data set into training set, validation set and test set. Make sure that each group contains an appropriate distribution of samples across different failure types. Randomize the data to avoid any bias during training. 5. ResNet Architecture: Design and implement a Residual Neural Network (ResNet) architecture for fault diagnosis tasks. ResNet is a deep learning model that incorporates residual connections to alleviate the vanishing gradient problem and enable efficient training of deep networks. The ResNet architecture usually consists of multiple residual blocks, each containing convolutional layers, batch normalization, and activation functions. 90-L-130-KP-5-AB-80-L-4-F1-F-06-GBA-40-40-24 90L130KP5AB80L4F1F06GBA404024 90L130-KP-5-AB-80-L-4-F1-H-03-EBA-29-29-24 90L130KP5AB80L4F1H03EBA292924 90-L-130-KP-5-AB-80-L-4-F1-H-03-EBA-29-29-24 90L130KP5AB80L4F1H03EBA292924 90L130-KP-5-AB-80-L-4-F1-H-03-GBA-29-29-24 90L130KP5AB80L4F1H03GBA292924 90-L-130-KP-5-AB-80-L-4-F1-H-03-GBA-29-29-24 90L130KP5AB80L4F1H03GBA292924 90-L-130-KP-5-AB-80-P-3-F1-F-03-GBA-38-38-24 90L130KP5AB80P3F1F03GBA383824 90-L-130-KP-5-AB-80-P-3-F1-F-03-GBA-42-42-24 90L130KP5AB80P3F1F03GBA424224 90L130-KP-5-AB-80-P-3-F1-H-03-GBA-35-35-24 90L130KP5AB80P3F1H03GBA353524 90-L-130-KP-5-AB-80-P-3-F1-H-03-GBA-35-35-24 90L130KP5AB80P3F1H03GBA353524 90L130-KP-5-AB-80-P-3-F1-H-06-GBA-35-35-24 90L130KP5AB80P3F1H06GBA353524 6. Model training: use the GADF image as input and the corresponding fault label as output to train the ResNet model. Employ a suitable optimization algorithm, such as Stochastic Gradient Descent (SGD) or Adam, to minimize classification errors. Monitor the performance of the model on the validation set and apply techniques such as early stopping to prevent overfitting. 7. Model evaluation: Use the test set to evaluate the trained ResNet model. Performance metrics such as accuracy, precision, recall, and F1-score are calculated to evaluate the model's ability to correctly classify different fault types. The results are compared with other diagnostic techniques or expert knowledge to verify the effectiveness of the composite fault diagnosis method. 8. Fine-tuning and optimization: Adjust hyperparameters such as learning rate, batch size or network architecture according to the evaluation results, and fine-tune the ResNet model. Optimize the model for better performance and generalization. 9. Interpretability and Visualization: Analyze the features and intermediate representations learned in the ResNet model to gain insight into the fault detection process. Visualize feature maps or activation patterns to understand which failure-related patterns the model is capturing. This analysis helps validate the diagnostic power of the model and enhances the interpretability of the results. 10. Deployment and integration: Deploy the trained ResNet model into a diagnostic system or real-time monitoring device for continuous fault detection in axial piston pumps. Integrate models with appropriate sensor networks, data acquisition systems, and decision-making algorithms to enable automated fault diagnosis and facilitate timely maintenance operations. 11. Continuous Improvement: Based on new data, field operation feedback or other fault types, the composite fault diagnosis system is continuously updated and improved. Incorporate new failure cases into the training dataset to improve the model's ability to detect a wider range of failures and improve its diagnostic accuracy. 12. Class Imbalance Handling: If a dataset has an unbalanced class distribution where some fault types are underrepresented, techniques are applied to address the class imbalance problem. This can include oversampling techniques (e.g., SMOTE) to increase samples from the minority class or undersampling techniques (e.g., stochastic undersampling) to reduce samples from the majority class. Alternatively, consider using class weights during training to give more weight to the minority of classes. 13. Transfer Learning: Explore the possibility of using transfer learning to improve fault diagnosis performance. Transfer learning involves using a pretrained model on a large dataset or related task and fine-tuning it on the target dataset. By leveraging knowledge and learned features from pretrained models, you can achieve better performance with smaller datasets or improved generalization to unseen fault types. 14. Ensemble learning: Consider using ensemble learning techniques to further improve fault diagnosis performance. Ensemble methods combine multiple models to collectively make predictions, which often improves accuracy and robustness. Techniques such as bagging, boosting, or stacking can be applied to combine multiple ResNet models or different machine learning models trained on subsets of the dataset. 15. Feature Fusion: Study the fusion of GADF-based features with other types of features or sensor data for more comprehensive fault diagnosis. For example, you can combine GADF features with statistical features, time-domain features, or other domain-specific features extracted from other sensor signals such as pressure or temperature. Such feature fusion can capture different aspects of fault characteristics and provide more informative representation for accurate diagnosis. 16. Interpretability: Develop techniques to interpret and explain fault diagnosis results obtained from composite models. This may involve methods such as saliency maps, attention mechanisms, or gradient-based attribution methods to highlight regions or features in the GADF image that contribute most to the classification decision. Interpretable models help build trust and understand the diagnostic process, especially in safety-critical applications. 90-L-130-KP-5-AB-80-P-3-F1-H-06-GBA-35-35-24 90L130KP5AB80P3F1H06GBA353524 90L130-KP-5-AB-80-R-3-F1-H-06-GBA-35-35-24 90L130KP5AB80R3F1H06GBA353524 90-L-130-KP-5-AB-80-R-3-F1-H-06-GBA-35-35-24 90L130KP5AB80R3F1H06GBA353524 90L130-KP-5-AB-80-R-4-F1-H-03-GBA-20-20-20 90L130KP5AB80R4F1H03GBA202020 90-L-130-KP-5-AB-80-R-4-F1-H-03-GBA-20-20-20 90L130KP5AB80R4F1H03GBA202020 90L130-KP-5-AB-80-S-3-C8-H-03-GBA-40-40-26 90L130KP5AB80S3C8H03GBA404026 90-L-130-KP-5-AB-80-S-3-C8-H-03-GBA-40-40-26 90L130KP5AB80S3C8H03GBA404026 90-L-130-KP-5-AB-80-S-3-F1-H-03-GBA-42-42-24 90L130KP5AB80S3F1H03GBA424224 90L130-KP-5-BB-80-D-3-F1-L-00-GBA-26-26-24 90L130KP5BB80D3F1L00GBA262624 90-L-130-KP-5-BB-80-D-3-F1-L-00-GBA-26-26-24 90L130KP5BB80D3F1L00GBA262624 17. Continuous monitoring and predictive maintenance: Combine the fault diagnosis system with the continuous monitoring device to realize real-time monitoring of the axial piston pump. By continuously analyzing vibration signals or other sensor data, the system can provide early warning of potential failure or degradation, enabling proactive maintenance operations and minimizing downtime. 18. Fault Severity Estimation: Extend fault diagnosis methods to not only detect fault types, but also estimate the severity or degradation level of detected faults. This may involve additional regression tasks to predict quantitative measures of failure severity, such as vibration amplitude, energy loss, or degree of wear. Severity estimates provide insight into the health of an axial piston pump and aid in decisions about maintenance or repair actions. 19. Maintenance Decision Support: Develop a decision support system that utilizes fault diagnosis results, severity estimates, and historical maintenance data to optimize maintenance planning, resource allocation, and spare parts management. By integrating a fault diagnosis system with a maintenance management system, you can improve the efficiency and effectiveness of your maintenance operations. By considering these additional points, you can further enhance the composite fault diagnosis of axial piston pump based on GADF and ResNet. This comprehensive approach provides a powerful tool for accurate fault detection, predictive maintenance, and decision support in the operation and maintenance of axial piston pumps.