The state identification of hydraulic pump leakage based on wavelet decomposition and deep learning involves the use of advanced signal processing techniques and deep learning algorithms to detect and classify different types of hydraulic pump leakage. An overview of the process follows: 1. Data collection: Collect vibration or sound signal data of hydraulic pumps under various operating conditions, including normal and different leakage situations. These signals can be collected using sensors connected to the pump or through other monitoring techniques. 2. Preprocessing: Preprocessing the collected signal data to remove noise and artifacts that may interfere with the analysis. This step may include filtering, signal normalization, and data conditioning to improve the quality of the input data. 3. Wavelet decomposition: apply wavelet decomposition to decompose the preprocessed signal into different frequency bands. Wavelet decomposition provides multiresolution analysis of signals and allows time and frequency domain information to be captured. The resulting wavelet coefficients represent signals in different frequency ranges. 90-R-055-KA-5-NN-80-R-3-C6-C-03-GBA-30-14-28 90R055KA5NN80R3C6C03GBA301428 90-R-055-KA-5-NN-80-R-3-C6-C-03-GBA-32-14-28 90R055KA5NN80R3C6C03GBA321428 90-R-055-KA-5-NN-80-R-3-C6-C-03-GBA-35-35-22 90R055KA5NN80R3C6C03GBA353522 90-R-055-KA-5-NN-80-R-3-C6-C-03-GBA-38-38-24 90R055KA5NN80R3C6C03GBA383824 90-R-055-KA-5-NN-80-R-3-C6-C-03-GBA-42-42-24 90R055KA5NN80R3C6C03GBA424224 90R055-KA-5-NN-80-R-3-C6-C-05-GBA-35-35-30 90R055KA5NN80R3C6C05GBA353530 90-R-055-KA-5-NN-80-R-3-C6-C-05-GBA-35-35-30 90R055KA5NN80R3C6C05GBA353530 90-R-055-KA-5-NN-80-R-4-C6-C-03-GBA-42-42-24 90R055KA5NN80R4C6C03GBA424224 90-R-055-KA-5-NN-80-R-4-S1-C-03-GBA-29-29-24 90R055KA5NN80R4S1C03GBA292924 90-R-055-KA-5-NN-80-S-3-C6-C-00-GBA-42-42-24 90R055KA5NN80S3C6C00GBA424224 90-R-055-KA-5-NN-80-S-3-C6-C-03-GBA-20-20-24 90R055KA5NN80S3C6C03GBA202024 90R055-KA-5-NN-80-S-3-C6-C-03-GBA-26-26-24 90R055KA5NN80S3C6C03GBA262624 90-R-055-KA-5-NN-80-S-3-C6-C-03-GBA-26-26-24 90R055KA5NN80S3C6C03GBA262624 90R055-KA-5-NN-80-S-3-C6-C-03-GBA-38-38-20 90R055KA5NN80S3C6C03GBA383820 90-R-055-KA-5-NN-80-S-3-C6-C-03-GBA-38-38-20 90R055KA5NN80S3C6C03GBA383820 90-R-055-KA-5-NN-80-S-3-C6-C-03-GBA-42-42-24 90R055KA5NN80S3C6C03GBA424224 90-R-055-KA-5-NN-80-S-3-C6-C-05-GBA-35-35-30 90R055KA5NN80S3C6C05GBA353530 90R055-KA-5-NN-80-S-3-C6-D-03-GBA-26-26-24 90R055KA5NN80S3C6D03GBA262624 90-R-055-KA-5-NN-80-S-3-C6-D-03-GBA-26-26-24 90R055KA5NN80S3C6D03GBA262624 90R055-KA-5-NN-80-S-3-S1-C-03-GBA-42-42-24 90R055KA5NN80S3S1C03GBA424224 4. Feature extraction: relevant features are extracted from wavelet coefficients to capture unique features associated with different leakage states. These features can include statistical measures, energy distributions, or other relevant parameters that highlight differences between normal and leaky signals. 5. Training data preparation: A labeled dataset is prepared by associating the extracted features with the corresponding leaky states. Datasets can be accurately labeled using expert or domain knowledge. Split datasets into training and validation sets to efficiently train and evaluate deep learning models. 6. Deep learning model training: Utilize deep learning architectures, such as convolutional neural networks (CNN) or recurrent neural networks (RNN), to train classification models on labeled datasets. The model learns to recognize patterns and features in input data that indicate a specific leak state. 7. Model evaluation: Evaluate the trained deep learning model using the validation dataset. Measure its performance metrics, such as accuracy, precision, recall, or F1-score, to evaluate its effectiveness in identifying different leak states. If necessary, tune the model's hyperparameters and architecture to optimize its performance. 8. Test and Deploy: Apply the trained model to new, unseen data collected from hydraulic pumps to predict leak status. The model will analyze the input signal, extract features, and classify them into appropriate leaky categories based on the learned patterns. 9. Continuous monitoring and maintenance: Implement a monitoring system that continuously collects and analyzes hydraulic pump signals in real time. Deploy trained models in surveillance systems to detect and classify leak status. The model is regularly updated and retrained with new data to improve its accuracy and adaptability. 10. Model optimization: Fine-tune deep learning models to improve their performance and generalization capabilities. This may involve tuning hyperparameters, such as learning rates, regularization techniques, or network architectures, to optimize the model's ability to accurately classify different leaky states. 90-R-055-KA-5-NN-80-S-3-S1-C-03-GBA-42-42-24 90R055KA5NN80S3S1C03GBA424224 90-R-055-KA-5-NN-80-S-3-S1-C-03-GBA-42-42-28 90R055KA5NN80S3S1C03GBA424228 90-R-055-KA-5-NN-80-S-3-S1-D-03-GBA-32-32-22 90R055KA5NN80S3S1D03GBA323222 90-R-055-KA-5-NN-80-S-4-C6-C-03-GBA-20-20-24 90R055KA5NN80S4C6C03GBA202024 90R055-KA-5-NN-80-S-4-C6-C-03-GBA-32-32-24 90R055KA5NN80S4C6C03GBA323224 90-R-055-KA-5-NN-80-S-4-C6-C-03-GBA-32-32-24 90R055KA5NN80S4C6C03GBA323224 90-R-055-KA-5-NN-80-S-4-S1-C-03-GBA-38-38-20 90R055KA5NN80S4S1C03GBA383820 90-R-055-KN-1-AB-80-P-3-S1-D-03-GBA-26-26-24 90R055KN1AB80P3S1D03GBA262624 90-R-055-KN-1-AB-80-P-3-S1-D-03-GBA-35-35-24 90R055KN1AB80P3S1D03GBA353524 90-R-055-KN-1-AB-80-P-4-S1-C-03-GBA-29-29-24 90R055KN1AB80P4S1C03GBA292924 90-R-055-KN-1-BC-60-P-3-S1-D-03-GBA-35-35-26 90R055KN1BC60P3S1D03GBA353526 90-R-055-KN-1-CD-80-P-3-C6-D-03-GBA-26-26-24 90R055KN1CD80P3C6D03GBA262624 90-R-055-KN-1-CD-80-P-3-C6-D-03-GBA-32-32-24 90R055KN1CD80P3C6D03GBA323224 90-R-055-KN-1-CD-80-P-3-C6-D-03-GBA-35-35-24 90R055KN1CD80P3C6D03GBA353524 90-R-055-KN-1-CD-80-P-3-C6-D-04-GBA-29-29-24 90R055KN1CD80P3C6D04GBA292924 90-R-055-KN-1-CD-80-P-3-S1-C-03-GBA-35-35-24 90R055KN1CD80P3S1C03GBA353524 90-R-055-KN-1-CD-80-P-3-S1-D-03-GBA-21-21-24 90R055KN1CD80P3S1D03GBA212124 90-R-055-KN-1-CD-80-P-4-S1-D-04-GBA-29-29-24-F034 90R055KN1CD80P4S1D04GBA292924F034 90-R-055-KN-1-CD-80-S-3-S1-C-03-GBA-42-42-24 90R055KN1CD80S3S1C03GBA424224 90-R-055-KN-1-CD-80-S-4-S1-C-03-GBA-35-35-24 90R055KN1CD80S4S1C03GBA353524 11. Ensemble methods: Explore ensemble methods, such as combining multiple deep learning models or using ensemble learning techniques such as bagging or boosting. Ensembling methods can enhance the robustness of a model and improve its classification accuracy by aggregating predictions from multiple models. 12. Transfer Learning: Consider leveraging transfer learning techniques by using pre-trained models on similar tasks or domains. Transfer learning helps to speed up the training process and improve the performance of recognition models, especially when limited labeled datasets are available. 13. Real-time monitoring: Develop a real-time monitoring system that continuously analyzes the signals from the hydraulic pumps and provides immediate feedback on the presence and severity of pump leaks. This enables proactive maintenance and reduces the risk of unplanned downtime or equipment failure. 14. Integration with maintenance systems: Integrate the identification system with existing maintenance management systems or data collection systems to facilitate efficient maintenance planning and scheduling. This integration allows for seamless transfer of information and automatic generation of maintenance alerts or work orders when pump leaks are detected. 15. Robustness to Environmental Factors: Consider environmental factors that may affect the hydraulic pump signal, such as changing operating conditions, temperature fluctuations, or noise sources. Train deep learning models on various datasets containing different operating conditions to ensure their robustness and adaptability to various scenarios. 90-R-055-KN-1-CD-80-S-4-S1-C-03-GBA-42-42-24 90R055KN1CD80S4S1C03GBA424224 90-R-055-KN-1-NN-60-D-4-S1-L-03-GBA-20-20-28 90R055KN1NN60D4S1L03GBA202028 90-R-055-KN-1-NN-80-L-3-S1-D-03-GBA-17-17-24 90R055KN1NN80L3S1D03GBA171724 90-R-055-KN-1-NN-80-P-3-S1-D-03-GBA-21-21-24 90R055KN1NN80P3S1D03GBA212124 90-R-055-KN-1-NN-80-R-3-S1-D-03-GBA-35-35-24 90R055KN1NN80R3S1D03GBA353524 90-R-055-KN-1-NN-80-R-4-S1-C-03-GBA-35-35-24 90R055KN1NN80R4S1C03GBA353524 90-R-055-KN-1-NN-80-R-4-S1-D-03-GBA-32-32-24 90R055KN1NN80R4S1D03GBA323224 90-R-055-KN-1-NN-80-S-4-S1-C-03-GBA-29-14-20 90R055KN1NN80S4S1C03GBA291420 90-R-055-KN-1-NN-80-S-4-S1-C-03-GBA-35-35-24 90R055KN1NN80S4S1C03GBA353524 90-R-055-KN-2-AB-60-S-3-S1-D-03-GBA-42-42-24 90R055KN2AB60S3S1D03GBA424224 90-R-055-KN-2-AB-80-P-4-C6-B-03-GBA-26-26-22 90R055KN2AB80P4C6B03GBA262622 90-R-055-KN-2-NN-80-L-4-T1-D-05-GBA-26-26-20 90R055KN2NN80L4T1D05GBA262620 90-R-055-KN-2-NN-80-P-3-C6-D-00-GBA-30-30-24 90R055KN2NN80P3C6D00GBA303024 90R055-KN-5-AB-60-P-3-C6-C-04-GBA-42-42-20 90R055KN5AB60P3C6C04GBA424220 90-R-055-KN-5-AB-60-P-3-C6-C-04-GBA-42-42-20 90R055KN5AB60P3C6C04GBA424220 90R055-KN-5-AB-60-R-3-S1-C-03-GBA-17-17-24 90R055KN5AB60R3S1C03GBA171724 90-R-055-KN-5-AB-60-R-3-S1-C-03-GBA-17-17-24 90R055KN5AB60R3S1C03GBA171724 90-R-055-KN-5-AB-80-S-3-C6-C-03-GBA-30-30-24 90R055KN5AB80S3C6C03GBA303024 90-R-055-KN-5-AB-80-S-3-S1-C-03-GBA-35-35-24 90R055KN5AB80S3S1C03GBA353524 90-R-055-KN-5-AB-80-S-3-S1-D-03-GBA-38-38-24 90R055KN5AB80S3S1D03GBA383824 16. Data Augmentation: Augmenting labeled datasets by artificially introducing changes in the input signal. This may involve techniques such as adding random noise, changing signal characteristics, or simulating different leakage scenarios. Data augmentation helps improve a model's ability to generalize and handle changes in real-world conditions. 17. Model Interpretability: Consider incorporating model interpretability techniques to understand the decision-making process of deep learning models. Techniques such as feature importance analysis, saliency maps, or gradient-based attribution methods can provide insight into signal features or regions that contribute most to classification decisions, thereby enhancing model interpretability and trust. 18. Continuous Improvement: Continuously monitor and identify system performance and collect feedback from maintainers. Incorporate this feedback into the model training process to improve and improve the accuracy and fitness of the model over time. By considering these additional factors, the effectiveness and usefulness of a hydraulic pump leak identification system can be increased. The combination of wavelet decomposition and deep learning, coupled with optimization techniques and real-time monitoring, can efficiently and reliably identify leak status, facilitate proactive maintenance and improve the overall performance and reliability of hydraulic pumping systems.