The Empirical Wavelet Transform (EWT) is a relatively new signal processing technique that can be used for fault characterization in a variety of applications, including plunger pump analysis. EWT is particularly effective at handling non-stationary and nonlinear signals, making it suitable for analyzing complex vibration signals associated with piston pump failures. The application of empirical wavelet transform to the extraction of plunger pump fault features can be carried out according to the following steps: 1. Signal collection: Use suitable sensors to collect the vibration signal of the plunger pump. Signals should be captured during normal pump operation as well as under fault conditions for comparative analysis. 2. Preprocessing: Preprocessing the acquired vibration signal to remove any noise or unwanted components that may affect the analysis. Common preprocessing techniques include filtering, resampling, and noise reduction algorithms. 3. EMD: EWT is based on the concept of EMD, so the first step is to use EMD to decompose the collected vibration signals. EMD decomposes a signal into intrinsic mode functions (IMFs), which represent the different scale or oscillatory components in the signal. 90-R-100-KA-1-CD-60-S-4-C7-E-00-GBA-38-38-24 90R100KA1CD60S4C7E00GBA383824 90R100-KA-1-CD-60-S-4-C7-E-00-GBA-38-38-24 90R100KA1CD60S4C7E00GBA383824 90-R-100-KA-1-CD-60-S-4-C7-E-00-GBA-36-36-24 90R100KA1CD60S4C7E00GBA363624 90R100-KA-1-CD-60-S-4-C7-E-00-GBA-36-36-24 90R100KA1CD60S4C7E00GBA363624 90-R-100-KA-1-CD-60-S-3-T2-E-00-GBA-23-23-24 90R100KA1CD60S3T2E00GBA232324 90-R-100-KA-1-CD-60-S-3-S1-E-02-GBA-26-26-30 90R100KA1CD60S3S1E02GBA262630 90-R-100-KA-1-CD-60-S-3-F1-E-03-GBA-42-42-24 90R100KA1CD60S3F1E03GBA424224 90-R-100-KA-1-CD-60-S-3-F1-E-03-GBA-29-29-24 90R100KA1CD60S3F1E03GBA292924 90R100-KA-1-CD-60-S-3-F1-E-03-GBA-29-29-24 90R100KA1CD60S3F1E03GBA292924 90-R-100-KA-1-CD-60-S-3-F1-E-00-GBA-23-23-24 90R100KA1CD60S3F1E00GBA232324 90-R-100-KA-1-CD-60-S-3-C7-F-04-GBA-42-42-28 90R100KA1CD60S3C7F04GBA424228 90R100-KA-1-CD-60-S-3-C7-F-04-GBA-42-42-28 90R100KA1CD60S3C7F04GBA424228 90-R-100-KA-1-CD-60-S-3-C7-E-03-GBA-32-32-20 90R100KA1CD60S3C7E03GBA323220 90-R-100-KA-1-CD-60-R-4-S1-E-03-GBA-32-32-24 90R100KA1CD60R4S1E03GBA323224 90-R-100-KA-1-CD-60-R-4-F1-E-03-GBA-26-26-24 90R100KA1CD60R4F1E03GBA262624 90-R-100-KA-1-CD-60-R-3-T2-E-00-GBA-23-23-24 90R100KA1CD60R3T2E00GBA232324 90-R-100-KA-1-CD-60-P-3-T2-E-00-GBA-23-23-24 90R100KA1CD60P3T2E00GBA232324 90-R-100-KA-1-CD-60-P-3-F1-F-03-GBA-42-42-24 90R100KA1CD60P3F1F03GBA424224 90-R-100-KA-1-CD-60-P-3-F1-E-03-GBA-35-35-20 90R100KA1CD60P3F1E03GBA353520 90-R-100-KA-1-CD-60-P-3-C7-F-03-GBA-42-42-24 90R100KA1CD60P3C7F03GBA424224 4. Adaptive Noise Removal: The IMF obtained from EMD may contain noise or artifacts. Apply adaptive noise cancellation techniques to improve the IMF and enhance fault-related components. 5. Empirical wavelet transform: After obtaining the IMFs, apply EWT to each IMF separately. EWT decomposes each IMF into a set of wavelet functions, which captures the time-frequency information of the signal more accurately than traditional wavelet transform. 6. Feature extraction: Extract relevant features from the EWT coefficients obtained in the previous step. These functions should capture specific failure characteristics or patterns associated with piston pump failures. Common feature extraction techniques include statistical moments, frequency domain analysis, wavelet entropy, or any other suitable method. 7. Fault diagnosis: use the extracted features to identify and classify different fault states of the plunger pump. This step typically involves using machine learning algorithms or pattern recognition techniques to train a model based on labeled data and perform accurate fault diagnosis. 8. Feature selection: After features are extracted, it may be necessary to select a subset of the most relevant features. This step helps to reduce dimensionality and improve the efficiency of fault diagnosis algorithms. Consider using feature selection techniques such as correlation analysis, mutual information, or feature ranking methods to identify the most discriminative features. 90-R-100-KA-1-CD-60-P-3-C7-E-03-GBA-42-42-24 90R100KA1CD60P3C7E03GBA424224 90-R-100-KA-1-CD-60-P-3-C7-E-03-GBA-23-23-24 90R100KA1CD60P3C7E03GBA232324 90-R-100-KA-1-CD-60-L-4-F1-E-03-GBA-35-35-24 90R100KA1CD60L4F1E03GBA353524 90-R-100-KA-1-CD-60-L-4-F1-E-03-GBA-32-32-24 90R100KA1CD60L4F1E03GBA323224 90R100-KA-1-CD-60-L-4-F1-E-03-GBA-32-32-24 90R100KA1CD60L4F1E03GBA323224 90-R-100-KA-1-CD-60-L-4-F1-E-03-GBA-32-26-24 90R100KA1CD60L4F1E03GBA322624 90R100-KA-1-CD-60-L-4-F1-E-03-GBA-32-26-24 90R100KA1CD60L4F1E03GBA322624 90-R-100-KA-1-CD-60-L-4-F1-E-03-GBA-30-30-24 90R100KA1CD60L4F1E03GBA303024 90R100-KA-1-CD-60-L-4-F1-E-03-GBA-30-30-24 90R100KA1CD60L4F1E03GBA303024 90-R-100-KA-1-CD-60-L-4-F1-E-03-GBA-26-26-24 90R100KA1CD60L4F1E03GBA262624 90R100-KA-1-CD-60-L-4-F1-E-03-GBA-26-26-24 90R100KA1CD60L4F1E03GBA262624 90-R-100-KA-1-CD-60-L-4-F1-E-00-GBA-26-26-24 90R100KA1CD60L4F1E00GBA262624 90-R-100-KA-1-CD-60-L-3-T2-E-03-GBA-35-35-24 90R100KA1CD60L3T2E03GBA353524 90-R-100-KA-1-CD-60-L-3-S1-F-03-GBA-29-29-24 90R100KA1CD60L3S1F03GBA292924 90-R-100-KA-1-CD-60-L-3-S1-E-03-GBA-42-42-24 90R100KA1CD60L3S1E03GBA424224 90-R-100-KA-1-CD-60-L-3-F1-F-03-GBA-35-35-24 90R100KA1CD60L3F1F03GBA353524 90-R-100-KA-1-CD-60-L-3-F1-E-03-GBA-35-35-24 90R100KA1CD60L3F1E03GBA353524 90-R-100-KA-1-CD-60-L-3-C7-F-03-GBA-14-14-20 90R100KA1CD60L3C7F03GBA141420 90-R-100-KA-1-CD-60-L-3-C7-D-03-GBA-42-42-24 90R100KA1CD60L3C7D03GBA424224 90-R-100-KA-1-BC-80-S-4-S1-F-00-GBA-23-23-20 90R100KA1BC80S4S1F00GBA232320 9. Fault classification: Using a suitable fault classification algorithm, classify the extracted features into different fault categories. Common techniques include support vector machines (SVM), neural networks, decision trees, or ensemble methods. A classification model is trained using labeled data representing different failure conditions of a plunger pump. 10. Performance evaluation: Evaluate the performance of the fault diagnosis system, including related indicators such as accuracy rate, accuracy rate, and recall rate. To guarantee model generalization, use appropriate evaluation techniques such as cross-validation or hold-out validation using a separate test dataset. 11. Refinement and optimization: According to the evaluation results, the feature extraction and classification algorithms are refined, and the fault diagnosis system is fine-tuned. Experiment with different parameter settings, feature combinations, or alternative algorithms to optimize system performance. 12. Validation and Validation: Validate the diagnostic fault system by applying it to new, unseen data from the plunger pump. The verification system provides accurate and reliable fault diagnosis in real scenarios. Make any necessary adjustments or improvements based on the validation results. 90-R-100-KA-1-BC-80-S-4-F1-E-03-GBA-42-42-24 90R100KA1BC80S4F1E03GBA424224 90-R-100-KA-1-BC-80-S-4-F1-E-03-GBA-35-35-24 90R100KA1BC80S4F1E03GBA353524 90-R-100-KA-1-BC-80-S-3-S1-E-03-GBA-35-35-28 90R100KA1BC80S3S1E03GBA353528 90-R-100-KA-1-BC-80-S-3-S1-E-03-EBC-29-29-24 90R100KA1BC80S3S1E03EBC292924 90-R-100-KA-1-BC-80-S-3-S1-E-00-GBA-23-23-24 90R100KA1BC80S3S1E00GBA232324 90-R-100-KA-1-BC-80-S-3-F1-E-03-GBA-35-35-24 90R100KA1BC80S3F1E03GBA353524 90-R-100-KA-1-BC-80-R-4-T2-E-00-GBA-32-32-24 90R100KA1BC80R4T2E00GBA323224 90-R-100-KA-1-BC-80-R-4-S1-F-09-GBA-38-38-24 90R100KA1BC80R4S1F09GBA383824 90-R-100-KA-1-BC-80-R-4-S1-E-03-GBA-26-26-20 90R100KA1BC80R4S1E03GBA262620 90-R-100-KA-1-BC-80-R-4-F1-E-03-GBA-35-35-24 90R100KA1BC80R4F1E03GBA353524 90-R-100-KA-1-BC-80-R-3-S1-F-00-GBA-35-35-24 90R100KA1BC80R3S1F00GBA353524 90-R-100-KA-1-BC-80-R-3-S1-E-09-GBA-35-35-24 90R100KA1BC80R3S1E09GBA353524 90-R-100-KA-1-BC-80-R-3-F1-E-03-GBA-35-35-24 90R100KA1BC80R3F1E03GBA353524 90-R-100-KA-1-BC-80-R-3-C7-F-03-GBA-42-42-24 90R100KA1BC80R3C7F03GBA424224 90R100-KA-1-BC-80-R-3-C7-F-03-GBA-42-42-24 90R100KA1BC80R3C7F03GBA424224 90-R-100-KA-1-BC-80-P-4-F1-E-00-GBA-17-17-20 90R100KA1BC80P4F1E00GBA171720 90-R-100-KA-1-BC-80-P-4-C7-F-03-GBA-42-42-24 90R100KA1BC80P4C7F03GBA424224 90R100-KA-1-BC-80-P-4-C7-F-03-GBA-42-42-24 90R100KA1BC80P4C7F03GBA424224 90-R-100-KA-1-BC-80-P-4-C7-E-00-GBA-35-35-24 90R100KA1BC80P4C7E00GBA353524 90-R-100-KA-1-BC-80-P-3-S1-F-03-GBA-42-42-24 90R100KA1BC80P3S1F03GBA424224 13. Integration and implementation: After the fault feature extraction and classification system is verified, it will be integrated into the overall monitoring and maintenance system of the plunger pump. This may involve developing user interfaces, establishing data acquisition and processing pipelines, and integrating the system with existing monitoring or control systems. 14. Continuous monitoring and maintenance: Deploy a fault diagnosis system in the operating environment to continuously monitor the performance of the plunger pump. Regularly updating the system with new data improves its performance and ensures its effectiveness in detecting and diagnosing faults in real time. Keep in mind that applying empirical wavelet transform and fault feature extraction methods to piston pump analysis may require expertise in signal processing, fault diagnosis, and machine learning. It is recommended to consult a domain expert or researcher in the field for further guidance and to ensure proper implementation of the method for your specific piston pump application.