A Reverse Vibration Suppression Method for Reciprocating Pump Pipelines Based on Joint Algorithm Control

Abstract

This invention discloses a reverse vibration suppression method for reciprocating pump pipelines based on a joint control algorithm. First, pipeline vibration data is acquired through simulation and experimentation, and a GRU neural network model is trained to predict vibration trends based on real-time operating conditions and output pre-control signals in advance. This drives a three-axis vibration suppression actuator to generate a reverse force for initial active suppression. Then, based on feedback of the remaining vibration data after suppression, fuzzy PID adaptive control is employed to adjust the proportional, integral, and derivative parameters online in real time, generating a precise control voltage for secondary fine-tuning of the remaining vibration. This invention combines the intelligent predictive feedforward control of the GRU neural network with the adaptive feedback control of fuzzy PID, forming a closed-loop control strategy of "prediction-initial suppression-feedback-fine-tuning." This significantly improves the vibration suppression accuracy, response speed, and system stability of the reciprocating pump pipeline, effectively reducing pipeline fatigue and leakage risks, and ensuring the safe and long-term operation of the system.

Description

Technical Field

[0001] This invention relates to a method for vibration reduction in reciprocating pump pipelines, and more particularly to a reverse vibration suppression method for reciprocating pump pipelines based on a joint algorithm control. Background Technology

[0002] In reciprocating pump systems in industries such as petrochemicals, oil and gas extraction, and marine propulsion, the commonly used method for pipeline vibration control is passive protection using conventional vibration damping components selected by technicians based on their field experience. However, due to the periodic suction and discharge characteristics of reciprocating pumps, strong fluid pulsations occur within the pipeline. Furthermore, the complex pipeline layout, diverse pipe diameters, and concentrated stress at connection points with the pump body and equipment interfaces make pipeline vibration control extremely challenging, demanding high levels of experience in pulsation suppression and on-site judgment from technicians. Traditional experience-based vibration control methods not only struggle to accurately match the pipeline's pulsation frequency, resulting in unstable vibration damping effects and potential hazards such as pipeline fatigue cracking and interface leaks, but also require long commissioning cycles for vibration damping solutions, severely impacting the overall operational reliability and production efficiency of the reciprocating pump system. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a reverse vibration suppression method for reciprocating pump pipelines based on joint algorithm control, which can effectively suppress the vibration of reciprocating pump pipelines. This method can achieve autonomous reverse suppression of reciprocating pump pipeline vibration and reduce safety hazards during the operation of reciprocating pump pipelines.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The present invention provides a method for reverse vibration suppression of reciprocating pump pipelines based on a joint control algorithm, comprising the following steps: Step 1: Create a 3D model of the reciprocating pump pipeline in 3D modeling software, and then import the created 3D model of the reciprocating pump pipeline into the pipeline structure simulation software. Step 2: Simulate the introduction of oil and gas into the reciprocating pump pipeline in the pipeline structure simulation software, and record the amplitude of the reciprocating pump pipeline at each position during the vibration process until the amplitude of the reciprocating pump pipeline approaches the set target range. Determine the position with the largest amplitude by comparing the amplitude of each position of the reciprocating pump pipeline. Step 3: Based on the maximum amplitude position obtained in Step 2, set up multiple working conditions in the simulation software and perform pipeline vibration simulation. Under each working condition, from the start of oil and gas introduction until the pipeline vibration amplitude tends to the target range, record the simulated vibration displacement data of the vibration displacement in the x, y, and z directions at the maximum amplitude position as a function of time. Combine the working condition parameters, sampling time, and corresponding simulated vibration displacement data for each working condition into an array for recording, save as simulation data, and enter into the simulation parameter library. The various operating parameters mentioned include at least changing the type, flow rate, and velocity of the oil and gas introduced into the pipeline; Step 4: Install the triaxial vibration sensor at the position of maximum amplitude in the reciprocating pump pipeline. Connect the triaxial vibration sensor to the computer. During the experimental test, adjust the actual operating conditions and start from the time the oil and gas enter the reciprocating pump pipeline until the pipeline vibration amplitude tends to the set target range. The computer collects the measured vibration displacement data of the x, y, and z directions output by the triaxial vibration sensor in real time as the vibration displacement changes over time. Then, the operating conditions, sampling time, and corresponding measured vibration displacement data are combined into an array and recorded as measured historical data, which serves as a real sample of the actual pipeline vibration behavior. Step 5: Using the simulation data obtained in Step 3 and the measured historical data in Step 4 as the basis for training and prediction, introduce the GRU intelligent neural network to construct a pipeline vibration prediction and active vibration suppression control model based on operating conditions and time. This pipeline vibration prediction and active vibration suppression control model is used to predict the vibration trend during the operation of the reciprocating pump pipeline and generate a pre-control electrical signal in advance to drive the three-axis vibration suppression actuator installed at the position of maximum vibration amplitude in the pipeline to actively suppress the vibration of the reciprocating pump pipeline. Step 6: Install a triaxial vibration damping actuator on the reciprocating pump pipeline. Using the GRU intelligent neural network trained in Step 5, output a pre-control electrical signal according to the real-time operating conditions during the operation of the reciprocating pump pipeline to drive the triaxial vibration damping actuator to generate a damping force opposite to the direction of pipeline vibration, so as to achieve active suppression of pipeline vibration. At the same time, collect the residual vibration data of the pipeline during the vibration damping process to characterize the vibration damping effect. Step 7: Based on the feedback of residual vibration data after initial vibration suppression, fuzzy PID adaptive control is adopted to further adjust and suppress the vibration of the reciprocating pump pipeline, and generate a control voltage signal for driving the triaxial vibration suppression actuator. Step 8: Drive the triaxial vibration damping actuator to actively dampen vibration based on the control voltage output of the fuzzy PID adaptive control.

[0005] The beneficial effects of this invention are as follows: This invention uses a GRU neural network to predict vibration and suppress it in advance, combined with fuzzy PID to precisely control residual vibration, thereby achieving active closed-loop control of reciprocating pump pipeline vibration. It significantly improves vibration reduction accuracy and stability, shortens the commissioning cycle, effectively reduces the risk of pipeline fatigue cracking and interface leakage, and ensures the operational reliability and production efficiency of the reciprocating pump system. Detailed Implementation

[0006] The present invention will now be described in detail: This invention is a method for reverse vibration suppression in reciprocating pump pipelines based on a joint control algorithm, comprising the following steps: Step 1: Create a 3D model of the reciprocating pump pipeline in 3D modeling software, and then import the created 3D model of the reciprocating pump pipeline into the pipeline structure simulation software. Step 2: Simulate the introduction of oil and gas into the reciprocating pump pipeline in the pipeline structure simulation software, and record the amplitude of the reciprocating pump pipeline at each position during the vibration process until the amplitude of the reciprocating pump pipeline approaches the set target range. Determine the position with the largest amplitude by comparing the amplitude of each position of the reciprocating pump pipeline. Step 3: Based on the maximum amplitude position obtained in Step 2, set up multiple operating conditions in the simulation software and perform pipeline vibration simulation for each condition. Under each operating condition, from the start of oil and gas introduction until the pipeline vibration amplitude approaches the target range, record the simulated vibration displacement data of the maximum amplitude position in the x, y, and z directions as a function of time. Combine the operating condition parameters, sampling time (preferably 1 ms), and corresponding simulated vibration displacement data for each operating condition into an array, save it as simulation data, and enter it into the simulation parameter library.

[0007] The various operating parameters mentioned include at least changing the type, flow rate, and velocity of the oil and gas introduced into the pipeline.

[0008] Step 4: Install the triaxial vibration sensor at the position of maximum amplitude in the reciprocating pump pipeline and connect the triaxial vibration sensor to the computer. During the experimental test, adjust the actual operating conditions and, starting from the moment oil and gas are introduced into the reciprocating pump pipeline until the pipeline vibration amplitude tends to the set target range, the computer collects the measured vibration displacement data in the x, y, and z directions output by the triaxial vibration sensor in real time as the vibration displacement changes over time. Then, the operating parameters, sampling time (the sampling interval is preferably 1ms), and the corresponding measured vibration displacement data are combined into an array and recorded, and saved as historical measurement data as a true sample of the actual pipeline vibration behavior.

[0009] Step 5: Using the simulation data obtained in Step 3 and the measured historical data from Step 4 as training and prediction data, a GRU intelligent neural network is introduced to construct a pipeline vibration prediction and active vibration suppression control model based on operating conditions and time. This model is used to predict vibration trends during the operation of the reciprocating pump pipeline and generate pre-control electrical signals in advance to drive a three-axis vibration suppression actuator installed at the position of maximum vibration amplitude in the pipeline to actively suppress the vibration of the reciprocating pump pipeline. The specific process is as follows: Step 501, Data Input and Model Training: Simulation data under various operating conditions are merged with corresponding measured historical data to form a training dataset, which is then input into the GRU intelligent neural network for offline training. Through training, the GRU network establishes a condition-time-vibration response mapping model from "operating parameters and time series input" to "triaxial vibration displacement output at the position of maximum pipe amplitude".

[0010] Step 502, Real-time vibration prediction and displacement estimation: During actual pipeline operation, from the moment oil and gas are introduced into the reciprocating pump pipeline, the GRU intelligent neural network receives real-time operating parameters (including oil and gas type, flow rate, and velocity) and time series information. Based on the trained model, the GRU network predicts the time-varying vibration displacement in the x, y, and z directions at the location of maximum pipeline amplitude as the sampling time increases from the moment oil and gas are introduced into the reciprocating pump pipeline until the pipeline vibration amplitude approaches the set target range.

[0011] Step 503, Pre-control signal generation and active vibration damping drive: The GRU network converts the predicted triaxial vibration displacement into corresponding drive electrical signals in real time, and performs phase inversion processing on the drive electrical signals to generate a pre-control electrical signal. The pre-control electrical signal is sent to the triaxial vibration damping actuator installed on the pipeline, driving the vibration damping actuator to generate a damping force opposite to the predicted vibration direction, thereby actively canceling the pipeline vibration before the vibration occurs or in the early stage of vibration, and realizing preliminary vibration damping control.

[0012] This step aims to construct a "predictive-counteracting" active vibration suppression intelligent control system. Through a trained GRU neural network, the system predicts future transient vibration states based on real-time operating conditions during pipeline operation and generates a precise control force in advance that counteracts the impending vibration, thus achieving closed-loop control from "passive response" to "active suppression."

[0013] Step Six: Install a triaxial vibration damping actuator on the reciprocating pump pipeline. Utilizing the GRU intelligent neural network trained in Step Five, output a pre-control electrical signal based on real-time operating conditions during the pipeline's operation. This signal drives the triaxial vibration damping actuator to generate a damping force opposite to the pipeline's vibration direction, thus achieving initial active suppression of pipeline vibration. Simultaneously, residual vibration data of the pipeline is collected during the damping process to characterize the damping effect. The specific process is as follows: Step 601, Installation of triaxial vibration damping actuator and system startup: At the location with the largest vibration amplitude in the reciprocating pump pipeline, two triaxial vibration damping actuators are symmetrically installed and connected to the computer. After installation, oil and gas are introduced into the reciprocating pump pipeline, and the timing starts from the moment the oil and gas are introduced. The system enters the real-time vibration monitoring and active vibration damping working state. Step 602, Real-time operating condition identification, vibration prediction and active vibration suppression control: During pipeline operation, triaxial vibration sensors installed on the pipeline collect real-time vibration displacement data in the x, y, and z directions at the location of maximum pipeline amplitude and send the data to a computer. The computer identifies the current pipeline operating condition based on real-time acquired operating parameters (including oil / gas type, flow rate, and velocity) and inputs the real-time operating parameters and time series into the GRU intelligent neural network trained in step five. Based on its internally established "operating condition-time-vibration response" mapping model, the GRU intelligent neural network predicts the vibration displacement in the x, y, and z directions of the pipeline in the next sampling period and generates corresponding pre-control electrical signals. These pre-control electrical signals, after phase inversion processing, are output by the computer and applied to the triaxial vibration damping actuator, driving the actuator to generate corresponding damping forces in the -x, -y, and -z directions, thereby actively canceling pipeline vibration and achieving initial vibration damping.

[0014] Step 603: Residual Vibration Data Acquisition and Recording: While the active vibration suppression control is being executed, a triaxial vibration sensor continuously measures the vibration displacement data in the x, y, and z directions at the point where the pipeline vibration amplitude is at its maximum after initial vibration suppression, from the moment the oil and gas enters the reciprocating pump pipeline until the pipeline vibration amplitude approaches the set target range. This triaxial vibration displacement data after vibration suppression is used as the pipeline residual vibration information under different operating conditions. Then, the corresponding operating parameters, sampling time, and residual vibration displacement data output by the triaxial vibration sensor are combined into an array and recorded. This recorded data serves as residual vibration data, used to evaluate the vibration suppression effect or as reference data for subsequent optimization of control strategies.

[0015] Step 7: Based on the feedback of residual vibration data after initial vibration suppression, fuzzy PID adaptive control is used to further adjust and suppress the vibration of the reciprocating pump pipeline, generating a control voltage signal for driving the triaxial vibration suppression actuator; the specific steps are as follows: Modify the following formulas and letters. Step 701: From the moment the oil and gas are introduced into the reciprocating pump pipeline until the vibration amplitude of the reciprocating pump pipeline tends to the set target range, the remaining vibration data measured by the triaxial vibration sensor is input as feedback information to the fuzzy PID adaptive control system. The computer uses the electrical signal output by the triaxial vibration sensor as the feedback signal and compares the feedback signal with the average value of the preset vibration target range to obtain the vibration control deviation value e(t) and the deviation change rate of the deviation value over time. .

[0016] Step 702, using the vibration control deviation value e(t) and the deviation change rate... As the two input variables of the fuzzy PID control system, the fuzzy PID control system determines the vibration control deviation value e(t) and the rate of change of deviation according to a pre-set fuzzy control rule base. Fuzzy inference and defuzzification are performed to adjust the PID control parameters in real time, thereby achieving the proportional gain. K p Integral coefficient K i and differential coefficients K d Adaptive adjustment.

[0017] The construction of the fuzzy control rule base is based on the system characteristic analysis and residual vibration data after GRU vibration suppression. Based on the residual vibration characteristics under different working conditions collected in step six, the vibration control deviation e(t) and the deviation change rate are determined. The fuzzy domain and linguistic values ​​(e.g., positive large, positive medium, zero, negative medium, negative large) of the PID parameter adjustment amount are used. Based on this, according to the fuzzy combination formed by the vibration control deviation and the rate of change of deviation under their respective fuzzy linguistic values, and combined with the dynamic characteristics of the reciprocating pump pipeline after GRU vibration suppression, the parameter adjustment experience under different vibration suppression stages is transformed into if-then form fuzzy control rules, that is, forming the rule statement "if the input meets a certain fuzzy condition, then the corresponding PID parameter adjustment amount is output", thus constructing an initial fuzzy rule base; the fuzzy combination refers to the vibration control deviation e(t) and the rate of change of deviation. The combination of fuzzy linguistic values ​​corresponds to two types of core parameters: the first type is the vibration control deviation e(t) and the rate of change of deviation, which are input quantities. The first category consists of fuzzy linguistic values ​​(e.g., positive large, positive medium, zero, negative medium, negative large); the second category consists of proportional coefficients used as output quantities. Adjustment amount △K p Integral coefficient adjustment △K i Adjustment amount of differential coefficients △K d The corresponding fuzzy language value.

[0018] Finally, through simulation verification based on residual vibration data and iterative correction during actual operation, unsuitable rules are eliminated, enabling the fuzzy rule library to adapt to vibration suppression requirements under different working conditions, and ultimately forming a fuzzy control rule library that can accurately match the vibration-suppressed system of GRU.

[0019] Step 703: After completing the PID parameter self-tuning, based on the proportional coefficient obtained from the real-time tuning... Kp Integral coefficient Ki Differential coefficients Kd Substituting into the basic formula of PID control:

[0020] In the formula e(t) This represents the real-time vibration control deviation value. Vibration control deviation value e (t) The integral from time 0 to time t (where (where is the integration variable, representing any time from 0 to the current time t). This represents the rate of change of deviation.

[0021] The continuous control quantity is calculated based on the above formula. u(t) The continuous control quantity is conditioned and converted into a voltage signal recognizable by the triaxial vibration damping actuator, thus generating the control voltage and obtaining the control voltage signal for vibration damping control.

[0022] Step 8: Based on the control voltage output of fuzzy PID adaptive control, the triaxial vibration damping actuator is driven to further actively dampen the reciprocating pump pipeline, causing the vibration amplitude of the reciprocating pump pipeline to converge within the set target range. The specific process is as follows: The control voltage signal output by the fuzzy PID control system in step seven is conditioned and converted by the signal processing module and then sent to the triaxial vibration damping actuator. Based on the received control voltage signal, the triaxial vibration damping actuator generates damping displacement or damping force in the x, y, and z directions that is opposite to the direction of the remaining vibration of the pipeline. This further actively suppresses the remaining vibration that still exists after the initial damping, so that the vibration amplitude of the reciprocating pump pipeline quickly converges to within the set target range.

[0023] The above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for reverse vibration suppression in reciprocating pump pipelines based on a joint control algorithm, characterized in that... Includes the following steps: Step 1: Create a 3D model of the reciprocating pump pipeline in 3D modeling software, and then import the created 3D model of the reciprocating pump pipeline into the pipeline structure simulation software. Step 2: Simulate the introduction of oil and gas into the reciprocating pump pipeline in the pipeline structure simulation software, and record the amplitude of the reciprocating pump pipeline at each position during the vibration process until the amplitude of the reciprocating pump pipeline approaches the set target range. Determine the position with the largest amplitude by comparing the amplitude of each position of the reciprocating pump pipeline. Step 3: Based on the maximum amplitude position obtained in Step 2, set up multiple working conditions in the simulation software and perform pipeline vibration simulation. Under each working condition, from the start of oil and gas introduction until the pipeline vibration amplitude tends to the target range, record the simulated vibration displacement data of the vibration displacement in the x, y, and z directions at the maximum amplitude position as a function of time. Combine the working condition parameters, sampling time, and corresponding simulated vibration displacement data for each working condition into an array for recording, save as simulation data, and enter into the simulation parameter library. The various operating parameters mentioned include at least changing the type, flow rate, and velocity of the oil and gas introduced into the pipeline; Step 4: Install the triaxial vibration sensor at the position of maximum amplitude in the reciprocating pump pipeline. Connect the triaxial vibration sensor to the computer. During the experimental test, adjust the actual operating conditions and start from the time the oil and gas enter the reciprocating pump pipeline until the pipeline vibration amplitude tends to the set target range. The computer collects the measured vibration displacement data of the x, y, and z directions output by the triaxial vibration sensor in real time as the vibration displacement changes over time. Then, the operating conditions, sampling time, and corresponding measured vibration displacement data are combined into an array and recorded as measured historical data, which serves as a real sample of the actual pipeline vibration behavior. Step 5: Using the simulation data obtained in Step 3 and the measured historical data in Step 4 as the basis for training and prediction, introduce the GRU intelligent neural network to construct a pipeline vibration prediction and active vibration suppression control model based on operating conditions and time. This pipeline vibration prediction and active vibration suppression control model is used to predict the vibration trend during the operation of the reciprocating pump pipeline and generate a pre-control electrical signal in advance to drive the three-axis vibration suppression actuator installed at the position of maximum vibration amplitude in the pipeline to actively suppress the vibration of the reciprocating pump pipeline. Step 6: Install a triaxial vibration damping actuator on the reciprocating pump pipeline. Using the GRU intelligent neural network trained in Step 5, output a pre-control electrical signal according to the real-time operating conditions during the operation of the reciprocating pump pipeline to drive the triaxial vibration damping actuator to generate a damping force opposite to the direction of pipeline vibration, so as to achieve the initial active suppression of pipeline vibration. At the same time, collect the residual vibration data of the pipeline during the vibration damping process to characterize the vibration damping effect. Step 7: Based on the feedback of residual vibration data after initial vibration suppression, fuzzy PID adaptive control is adopted to further adjust and suppress the vibration of the reciprocating pump pipeline, and generate a control voltage signal for driving the triaxial vibration suppression actuator. Step 8: Drive the triaxial vibration damping actuator based on the control voltage output of the fuzzy PID adaptive control to further actively dampen the vibration of the reciprocating pump pipeline, so that the vibration amplitude of the reciprocating pump pipeline converges to within the set target range.

2. The method for reverse vibration suppression of reciprocating pump pipelines based on a joint control algorithm according to claim 1, characterized in that: Step five specifically includes the following steps: Step 501, Data Input and Model Training: Simulation data containing multiple operating conditions is merged with corresponding measured historical data to form a training dataset, which is then input into the GRU intelligent neural network for offline training. Through training, the GRU network establishes an operating condition-time-vibration response mapping model from "operating condition parameters and time series input" to "three-axis vibration displacement output at the position of maximum pipe amplitude". Step 502, Real-time vibration prediction and displacement estimation: During actual pipeline operation, from the moment oil and gas are introduced into the reciprocating pump pipeline, the GRU intelligent neural network receives current operating parameters and time-series information in real time. Based on a trained model, the GRU network predicts the time-varying vibration displacement in the x, y, and z directions at the location of maximum pipeline amplitude as the sampling time increases, from the moment oil and gas are introduced into the reciprocating pump pipeline until the pipeline vibration amplitude approaches a set target range. Step 503, Pre-control signal generation and active vibration damping drive: The GRU network converts the predicted triaxial vibration displacement into a corresponding drive electrical signal in real time, and performs phase reversal processing on the drive electrical signal to generate a pre-control electrical signal. The pre-control electrical signal is sent to the triaxial vibration damping actuator installed on the pipeline, driving the vibration damping actuator to generate a damping force opposite to the predicted vibration direction, thereby actively canceling the pipeline vibration before the vibration occurs or in the early stage of the vibration, and realizing preliminary vibration damping control.

3. The method for reverse vibration suppression of reciprocating pump pipelines based on a joint control algorithm according to claim 1, characterized in that: Step six specifically includes the following steps: Step 601, Installation of triaxial vibration damping actuator and system startup: At the location with the largest vibration amplitude in the reciprocating pump pipeline, two triaxial vibration damping actuators are symmetrically installed and connected to the computer. After installation, oil and gas are introduced into the reciprocating pump pipeline, and the timing starts from the moment the oil and gas are introduced. The system enters the real-time vibration monitoring and active vibration damping working state. Step 602, Real-time operating condition identification, vibration prediction and active vibration suppression control: During pipeline operation, a triaxial vibration sensor installed on the pipeline collects vibration displacement data in the x, y, and z directions at the location of maximum pipeline amplitude in real time, and sends the data to a computer. The computer identifies the current pipeline operating condition based on the real-time acquired operating parameters, and inputs the real-time operating condition parameters and time series into the GRU intelligent neural network that has been trained in step five. Based on its internally established "operating condition-time-vibration response" mapping model, the GRU intelligent neural network predicts the vibration displacement of the pipeline in the x, y, and z directions in the next sampling period, and generates corresponding pre-control electrical signals accordingly. After phase reversal processing, the pre-control electrical signals are output by the computer and act on the triaxial vibration damping actuator, driving the vibration damping actuator to generate corresponding damping forces in the -x, -y, and -z directions, thereby actively canceling the pipeline vibration and achieving initial vibration damping. Step 603: Residual Vibration Data Acquisition and Recording: While the active vibration suppression control is being executed, a triaxial vibration sensor continuously measures the vibration displacement data in the x, y, and z directions at the point where the pipeline vibration amplitude is at its maximum after initial vibration suppression, from the moment the oil and gas enters the reciprocating pump pipeline until the pipeline vibration amplitude approaches the set target range. The above-mentioned triaxial vibration displacement data after vibration suppression is used as the pipeline residual vibration information under different operating conditions. Then, the corresponding operating parameters, sampling time, and residual vibration displacement data output by the triaxial vibration sensor are combined into an array and recorded. The recorded data is used as residual vibration data.

4. The method for reverse vibration suppression of reciprocating pump pipelines based on a joint control algorithm according to claim 3, characterized in that: Step seven specifically includes the following steps: Step 701: From the moment the oil and gas are introduced into the reciprocating pump pipeline until the vibration amplitude of the reciprocating pump pipeline tends to the set target range, the remaining vibration data measured by the triaxial vibration sensor is input as feedback information to the fuzzy PID adaptive control system. The computer uses the electrical signal output by the triaxial vibration sensor as the feedback signal and compares the feedback signal with the average value of the preset vibration target range to obtain the vibration control deviation value and the deviation change rate of the deviation value over time. Step 702: The vibration control deviation value and the deviation change rate are used as the dual inputs of the fuzzy PID control system. The fuzzy PID control system performs fuzzy reasoning and defuzzification calculation on the vibration control deviation and the deviation change rate according to the pre-set fuzzy control rule library, and adjusts the adjustment amount of the PID control parameters in real time, thereby realizing the adaptive adjustment of the proportional coefficient, integral coefficient and derivative coefficient. Step 703: After completing the PID parameter self-tuning, based on the proportional coefficient, integral coefficient, and derivative coefficient obtained from real-time tuning, substitute them into the basic PID control formula to calculate the continuous control quantity, and perform signal conditioning on the continuous control quantity to convert the control quantity into a voltage signal that can be recognized by the three-axis vibration damping actuator, thereby completing the generation of the control voltage and obtaining the control voltage signal used for vibration damping control.

5. The method for reverse vibration suppression of reciprocating pump pipelines based on a joint control algorithm according to claim 4, characterized in that: Step eight specifically includes the following steps: The control voltage signal output by the fuzzy PID control system in step seven is conditioned and converted by the signal processing module and then sent to the triaxial vibration damping actuator. Based on the received control voltage signal, the triaxial vibration damping actuator generates damping displacement or damping force in the x, y, and z directions that are opposite to the direction of the remaining vibration of the pipeline. This further actively suppresses the remaining vibration that still exists after the initial damping, so that the vibration amplitude of the pipeline quickly converges to within the set target range.

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