Extracting cavitation vibration signals from piston pumps is critical for detecting and diagnosing cavitation, which can cause severe damage and performance degradation. Cavitation occurs when the pressure in a hydraulic fluid falls below the vapor pressure, resulting in the formation and collapse of air bubbles. The following is an overview of the cavitation vibration signal extraction process in a plunger pump: 1. Signal collection: establish a vibration measurement system to collect the vibration signal of the plunger pump. This usually involves attaching accelerometers or vibration sensors to critical locations on the pump, such as the casing, cylinder block or valve plate. Make sure the sensor is properly calibrated and mounted securely. 2. Cavitation identification: Familiar with the symptoms and characteristics of plunger pump cavitation. Cavitation often manifests as high-frequency impulsive vibrations with distinctive characteristics, such as spike-like peaks or repetitive shocks. Knowing these features will help in identifying and extracting cavitation signatures. 3. Preprocessing: Preprocessing is performed on the acquired vibration signal to enhance the cavitation signal and remove any noise or unwanted components. Apply appropriate signal processing techniques, such as filtering, to isolate frequency ranges associated with cavitational vibrations. Low-pass or band-pass filters effectively remove low-frequency noise while preserving high-frequency cavitation signals. 90-R-075-DD-2-CD-80-S-3-S1-C-GB-GBA-45-35-24 90R075DD2CD80S3S1CGBGBA453524 90R075-DD-2-NN-80-R-4-S1-C-GB-GBA-38-38-24 90R075DD2NN80R4S1CGBGBA383824 90-R-075-DD-2-NN-80-R-4-S1-C-GB-GBA-38-38-24 90R075DD2NN80R4S1CGBGBA383824 90R075-DD-5-AB-60-L-3-S1-E-GB-GBA-35-35-24 90R075DD5AB60L3S1EGGBGBA353524 90-R-075-DD-5-AB-60-L-3-S1-E-GB-GBA-35-35-24 90R075DD5AB60L3S1EGBGBA353524 90-R-075-DD-5-AB-60-P-4-S1-C-G8-GBA-35-35-24 90R075DD5AB60P4S1CG8GBA353524 90R075-DD-5-AB-60-S-4-T2-D-G8-FAC-42-42-24 90R075DD5AB60S4T2DG8FAC424224 90-R-075-DD-5-AB-60-S-4-T2-D-G8-FAC-42-42-24 90R075DD5AB60S4T2DG8FAC424224 90-R-075-DD-5-AB-80-P-4-S1-D-GB-GBA-32-32-24 90R075DD5AB80P4S1DGBGBA323224 90-R-075-DD-5-AB-80-R-3-S1-D-G8-GBA-29-29-24 90R075DD5AB80R3S1DG8GBA292924 90-R-075-DD-5-AB-80-S-4-S1-C-GB-GBA-35-35-24 90R075DD5AB80S4S1CGBGBA353524 90R075-DD-5-BB-60-S-3-C7-D-GB-GBA-35-35-24 90R075DD5BB60S3C7DGBGBA353524 90-R-075-DD-5-BB-60-S-3-C7-D-GB-GBA-35-35-24 90R075DD5BB60S3C7DGBGBA353524 90R075-DD-5-BC-60-L-3-S1-E-GB-GBA-35-35-24 90R075DD5BC60L3S1EGGBGBA353524 90-R-075-DD-5-BC-60-L-3-S1-E-GB-GBA-35-35-24 90R075DD5BC60L3S1EGBGBA353524 90-R-075-DD-5-BC-60-S-4-C7-D-GB-GBA-26-26-24 90R075DD5BC60S4C7DGBGBA262624 90-R-075-DD-5-BC-60-S-4-C7-D-GB-GBA-35-35-24 90R075DD5BC60S4C7DGBGBA353524 90R075-DD-5-BC-60-S-4-S1-D-GB-GBA-40-40-24 90R075DD5BC60S4S1DGBGBA404024 90-R-075-DD-5-BC-60-S-4-S1-D-GB-GBA-40-40-24 90R075DD5BC60S4S1DGBGBA404024 90R075-DD-5-BC-80-L-4-S1-E-GB-GBA-35-35-24 90R075DD5BC80L4S1EGGBGBA353524 4. Time-frequency analysis: Time-frequency analysis is performed on the preprocessed vibration signal to determine the existence and time characteristics of cavitation. Techniques such as the short-time Fourier transform (STFT), continuous wavelet transform (CWT), or Hilbert-Huang transform (HHT) can reveal the time-varying frequency content of the signal and help isolate cavitation-induced vibrations. 5. Feature extraction: Extract relevant features from the time-frequency representation of the cavitation vibration signal. These features should capture the unique patterns or energy distributions associated with cavitation. Examples of its characteristics include peak amplitudes, frequency bands, spectral moments or entropy measures. In order to facilitate subsequent analysis and classification, the most representative cavitation features are selected. 6. Threshold and event detection: thresholding techniques are applied to detect important cavitation events or peaks in the extracted features. The threshold can be set based on the statistical properties of the cavitation signal or by exploiting domain knowledge. This step helps to distinguish cavitation-related signals from background noise or non-cavitation vibrations. 7. Analysis and Interpretation: Analyze the extracted cavitation vibration signals and related features to gain insight into the severity, location and potential causes of cavitation. Results are compared to established standards or reference values to determine the severity of cavitation and its impact on pump performance. Correlate cavitation signals with other operating parameters such as flow rate, pressure or temperature to understand the underlying conditions leading to cavitation. 8. Integration and alarm: The cavitation vibration signal extraction is integrated into the overall monitoring system of the plunger pump. Develop algorithms or decision rules to trigger alerts or alarms when cavitation is detected above a predefined threshold. This enables timely intervention and preventive measures to mitigate the adverse effects of cavitation on the pump. 90-R-075-DD-5-BC-80-L-4-S1-E-GB-GBA-35-35-24 90R075DD5BC80L4S1EGBGBA353524 90R075-DD-5-BC-80-P-4-S1-E-GB-GBA-35-35-24 90R075DD5BC80P4S1EGGBGBA353524 90-R-075-DD-5-BC-80-P-4-S1-E-GB-GBA-35-35-24 90R075DD5BC80P4S1EGBGBA353524 90R075-DD-5-CD-60-L-3-C7-E-GB-GBA-35-35-24 90R075DD5CD60L3C7EGGBA353524 90-R-075-DD-5-CD-60-L-3-C7-E-GB-GBA-35-35-24 90R075DD5CD60L3C7EGBGBA353524 90R075-DD-5-CD-60-P-4-C7-D-GB-GBA-42-42-28 90R075DD5CD60P4C7DGBGBA424228 90-R-075-DD-5-CD-60-P-4-C7-D-GB-GBA-42-42-28 90R075DD5CD60P4C7DGBGBA424228 90R075-DD-5-CD-60-S-4-C7-D-GB-GBA-40-40-24 90R075DD5CD60S4C7DGBGBA404024 90-R-075-DD-5-CD-60-S-4-C7-D-GB-GBA-40-40-24 90R075DD5CD60S4C7DGBGBA404024 90R075-DD-5-CD-80-S-3-S1-C-GB-GBA-45-35-24 90R075DD5CD80S3S1CGBGBA453524 90-R-075-DD-5-CD-80-S-3-S1-C-GB-GBA-45-35-24 90R075DD5CD80S3S1CGBGBA453524 90R075-DD-5-CD-80-S-3-S1-E-GB-GBA-42-32-24 90R075DD5CD80S3S1EGGBGBA423224 90-R-075-DD-5-CD-80-S-3-S1-E-GB-GBA-42-32-24 90R075DD5CD80S3S1EGBGBA423224 90R075-DD-5-CD-80-S-4-S1-E-G1-GBA-42-42-20 90R075DD5CD80S4S1EG1GBA424220 90-R-075-DD-5-CD-80-S-4-S1-E-G1-GBA-42-42-20 90R075DD5CD80S4S1EG1GBA424220 90-R-075-DD-5-NN-60-P-4-S1-D-G8-GBA-35-35-24 90R075DD5NN60P4S1DG8GBA353524 90R075-DD-5-NN-60-S-4-C6-C-GF-GBA-20-20-24 90R075DD5NN60S4C6CGFGBA202024 90-R-075-DD-5-NN-60-S-4-C6-C-GF-GBA-20-20-24 90R075DD5NN60S4C6CGFGBA202024 90-R-075-DD-5-NN-60-S-4-C7-E-GB-GBA-35-35-24 90R075DD5NN60S4C7EGBGBA353524 90R075-DD-5-NN-80-L-4-S1-D-GB-GBA-42-42-24 90R075DD5NN80L4S1DGBGBA424224 9. Feature enhancement: Enhance the extracted cavitation vibration features to highlight the unique features of cavitation. This can be done by applying signal processing techniques such as wavelet denoising, envelope analysis or temporal averaging. These technologies help to emphasize cavitation-related components while reducing noise and interference from other sources. 10. Pattern Recognition and Classification: Utilizes pattern recognition and classification techniques to differentiate between normal pump operation and cavitation events. Train a machine learning model using labeled data containing normal and cavitation vibration signals. Common classification algorithms for support vector machines include (SVM), random forests, or neural networks. The new vibration signal can then be classified as either a normal vibration signal or an indicator of cavitation using the trained model. 11. Severity assessment: Evaluate the severity of cavitation based on the extracted features and classification results. Develop a severity index or index that quantifies the intensity or extent of cavitation. These metrics can take into account factors such as the magnitude of cavitation vibrations, the frequency content, or the duration of cavitation events. Severity assessment provides valuable information for maintenance decisions and helps prioritize actions to mitigate cavitation-related problems. 12. Validation and Validation: Verify the validity of the extracted cavitation vibration signature and classification model by comparison with direct observation or other independent diagnostic techniques. Experiments were performed under controlled conditions to induce cavitation and verify the accuracy and reliability of the extraction and classification methods. 90-R-075-DD-5-NN-80-L-4-S1-D-GB-GBA-42-42-24 90R075DD5NN80L4S1DGBGBA424224 90-R-075-DD-5-NN-80-P-4-S1-D-GB-GBA-32-32-24 90R075DD5NN80P4S1DGBGBA323224 90R075-DD-5-NN-80-P-4-S1-E-G8-GBA-42-42-30 90R075DD5NN80P4S1EG8GBA424230 90-R-075-DD-5-NN-80-P-4-S1-E-G8-GBA-42-42-30 90R075DD5NN80P4S1EG8GBA424230 90R075-DD-5-NN-80-S-3-C7-E-GB-GBA-42-42-24 90R075DD5NN80S3C7EGBGBA424224 90-R-075-DD-5-NN-80-S-3-C7-E-GB-GBA-42-42-24 90R075DD5NN80S3C7EGBGBA424224 90R075-DD-5-NN-80-S-3-S1-D-GB-GBA-35-35-24 90R075DD5NN80S3S1DGBGBA353524 90-R-075-DD-5-NN-80-S-3-S1-D-GB-GBA-35-35-24 90R075DD5NN80S3S1DGBGBA353524 90-R-075-HF-1-AB-60-P-3-C7-D-03-GBA-35-35-24 90R075HF1AB60P3C7D03GBA353524 90R075-HF-1-AB-60-P-3-C7-D-03-GBA-35-35-24 90R075HF1AB60P3C7D03GBA353524 90-R-075-HF-1-AB-60-R-3-S1-D-03-GBA-35-35-24 90R075HF1AB60R3S1D03GBA353524 90-R-075-HF-1-AB-60-R-3-S1-E-00-GBA-23-23-24 90R075HF1AB60R3S1E00GBA232324 90-R-075-HF-1-AB-60-S-3-S1-D-03-GBA-42-42-24 90R075HF1AB60S3S1D03GBA424224 90-R-075-HF-1-AB-60-S-3-S1-E-03-GBA-38-38-24 90R075HF1AB60S3S1E03GBA383824 90R075-HF-1-AB-60-S-4-S1-D-00-GBA-38-38-24 90R075HF1AB60S4S1D00GBA383824 90-R-075-HF-1-AB-60-S-4-S1-D-00-GBA-38-38-24 90R075HF1AB60S4S1D00GBA383824 90-R-075-HF-1-AB-80-D-4-S1-L-03-GBA-35-35-24 90R075HF1AB80D4S1L03GBA353524 90R075-HF-1-AB-80-L-4-C6-E-00-GBA-32-32-32 90R075HF1AB80L4C6E00GBA323232 90-R-075-HF-1-AB-80-L-4-C6-E-00-GBA-32-32-32 90R075HF1AB80L4C6E00GBA323232 90-R-075-HF-1-AB-80-P-3-S1-D-03-GBA-35-35-24 90R075HF1AB80P3S1D03GBA353524 13. Continuous monitoring and trend analysis: Implement a continuous monitoring system for the plunger pump to capture and analyze vibration signals in real time. Analyze the trend of extracted cavitation features over time to detect changes in cavitation severity or incidence. Trend analysis enables proactive maintenance by identifying potential problems and initiating corrective actions before problems escalate. 14. Integration with maintenance system: Integrate cavitation vibration signal extraction and analysis into the overall maintenance system of the plunger pump. This integration supports automated monitoring, data logging and alarm generation. It can also facilitate the integration of cavitation detection with other maintenance strategies, such as condition-based or predictive maintenance. 15. Record and report: record the whole process of cavitation vibration signal extraction, feature extraction, classification model and verification results. Keep records of extracted features, classification performance, and any other diagnostic results. Documentation is regularly updated and reviewed as new insights or improvements emerge. Remember to tailor extraction procedures and analytical techniques to the specific characteristics of your plunger pump and operating environment. It is recommended to consult a domain expert, researcher or pump manufacturer for further guidance and to ensure proper implementation of cavitation vibration signal extraction methods.