Intelligent compressor vibration reduction method and system based on magnetorheological effect

Abstract

The invention relates to an intelligent compressor vibration reduction method and system based on the magneto-rheological effect, and relates to the technical field of compressors. Performing timestamp alignment and filtering processing on the detection physical data to determine standard physical data; performing feature extraction calculation based on the standard physical data to determine external excitation representative intensity, external excitation representative intensity and internal response representative stability, and performing normalization and weighting processing according to the external excitation representative intensity, the external excitation representative intensity and the internal response representative stability to determine a dynamic disturbance index; performing data analysis according to the standard physical data and the dynamic disturbance index to determine an expected damping force; and analyzing according to the expected damping force to determine a coil target current, and controlling the damper to operate according to the coil target current. The compressor vibration reduction device has the good effect of conducting vibration reduction treatment on the compressor.

Description

Technical Field

[0001] This application relates to the field of compressor technology, and in particular to a method and system for intelligent vibration reduction of compressors based on magnetorheological effects. Background Technology

[0002] Compressors serve as critical power units in industrial production and refrigeration systems. During operation, they are prone to low-frequency large-amplitude vibrations and transient shocks due to unbalanced loads, gas pulsations, and mechanical impacts. These vibrations not only affect equipment lifespan and operational reliability but also lead to fatigue damage to pipelines, supports, and connected facilities. Traditional passive vibration reduction measures (such as springs, rubber pads, and fixed dampers) struggle to balance daily vibration suppression and transient energy absorption when facing changes in operating conditions and sudden impacts, and they lack online tuning capabilities.

[0003] Among related technologies, variable dampers based on magnetorheological (MR) effects have become a powerful means of achieving active / semi-active vibration reduction due to their fast response speed and the ability to adjust the damping force through current. However, under complex working conditions, the adjustment of damping force still suffers from problems such as asynchronous multi-source measurements and difficulty in extracting real-time features, making it difficult to achieve stable, robust, and engineerable closed-loop control. As a result, the current compressor vibration reduction effect is poor and there is still room for improvement. Summary of the Invention

[0004] In order to better reduce the vibration of compressors, this application provides a smart vibration reduction method and system for compressors based on magnetorheological effect.

[0005] Firstly, this application provides a smart vibration reduction method for compressors based on magnetorheological effects, employing the following technical solution: A smart vibration reduction method for compressors based on magnetorheological effects includes: Acquire physical data for testing, including vibration acceleration, relative displacement of the damper, and dynamic pressure of the hydraulic cylinder; The detected physical data is timestamped and filtered to determine the standard physical data. Feature extraction calculations are performed based on standard physical data to determine the intensity, severity, and stability of external excitation, and then normalization and weighting are performed based on the intensity, severity, and stability of external excitation and the stability of internal response to determine the dynamic disturbance index. The desired damping force is determined by data analysis based on standard physical data and dynamic disturbance index. The target current of the coil is determined by analysis based on the desired damping force, and the operation of the damper is controlled by the target current of the coil.

[0006] Optionally, the steps of timestamping and filtering the detected physical data to determine standard physical data include: The detected physical data is channel-synchronized according to the PPS or IEEE-1588 / PTP clock to output intermediate physical data. The intermediate physical data is denoised using a Kalman filter, and then glitches are suppressed using a window midpoint filter to determine the standard physical data.

[0007] Optionally, after the dynamic disturbance index is determined, the intelligent vibration reduction method for compressors based on magnetorheological effects also includes: Determine whether the dynamic disturbance index is greater than the preset severe collision threshold; If the dynamic disturbance index is not greater than the severe collision threshold, the damper operation is controlled according to the dynamic disturbance index. If the dynamic disturbance index is greater than the severe collision threshold, the preset hydraulic auxiliary system is controlled to operate to generate a buffering force.

[0008] Secondly, this application provides a compressor intelligent vibration reduction system based on magnetorheological effect, which adopts the following technical solution: A compressor intelligent vibration reduction system based on magnetorheological effect includes: The data acquisition module acquires the physical data from the detection. The data preprocessing module performs timestamp alignment and filtering on the detected physical data to determine standard physical data; The dynamic disturbance index calculation module performs feature extraction calculations based on standard physical data to determine the external excitation representative intensity, external excitation representative intensity, and internal response representative stability. It then performs normalization and weighting processing based on the external excitation representative intensity, external excitation representative intensity, and internal response representative stability to determine the dynamic disturbance index. The desired damping force calculation module performs data analysis based on standard physical data and dynamic disturbance index to determine the desired damping force; The damping execution module analyzes the desired damping force to determine the target current of the coil and controls the operation of the damper based on the target current of the coil.

[0009] Optional, also includes: The auxiliary buffer module is used to control the operation of the preset hydraulic auxiliary system to generate a buffering force.

[0010] Thirdly, this application provides a smart terminal, which adopts the following technical solution: A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed any of the above-mentioned intelligent vibration reduction methods for compressors based on magnetorheological effects.

[0011] Fourthly, this application provides a computer storage medium capable of storing corresponding programs, which has the characteristic of effectively reducing vibration in the compressor, and adopts the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and executed any of the above-mentioned intelligent vibration reduction methods for compressors based on magnetorheological effects.

[0012] In summary, this application includes at least one of the following beneficial technical effects: This application can better analyze the vibration situation of the compressor, so that the damper operation can be adjusted according to the vibration situation, thereby effectively reducing the vibration of the compressor. This application can perform clock synchronization and filtering on the acquired data to ensure data accuracy and improve the precision of damper control. Attached Figure Description

[0013] Figure 1 This is a flowchart of a compressor intelligent vibration reduction method based on magnetorheological effect.

[0014] Figure 2 This is a flowchart of a module for a compressor intelligent vibration reduction method based on magnetorheological effect. Detailed Implementation

[0015] To make the purpose, technical solution, and advantages of this application clearer, the following is combined with Figures 1-2 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0016] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0017] This application discloses an intelligent vibration reduction method for compressors based on magnetorheological effects, referring to... Figure 1 The method flow of the intelligent vibration reduction method for compressors based on magnetorheological effect includes the following steps: Step S100: Acquire detection physical data, including vibration acceleration, relative displacement of the damper, and dynamic pressure of the hydraulic cylinder.

[0018] The detected physical data includes vibration acceleration, relative displacement of the damper, and dynamic pressure of the hydraulic cylinder. Vibration acceleration includes values ​​in three orthogonal directions (X, Y, Z), which can be obtained by installing a multi-axis vibration acceleration sensor near the vibration source on the compressor housing (e.g., near the cylinder) and on the base of the protected equipment (e.g., a precision instrument platform). Vibration acceleration can be measured using... Let t represent the number of t-th ... At time t, the superscript s indicates the s-th sensor location.

[0019] The relative displacement of the damper is the numerical value of the relative displacement of the damper piston rod, and the dynamic pressure of the hydraulic cylinder is the dynamic pressure inside the damper's hydraulic cylinder. This can be measured using a damper state sensor mounted on the magnetorheological damper body. The damper state sensor includes a linear displacement sensor and a pressure sensor. The linear displacement sensor is used to acquire the relative displacement of the damper, and the pressure sensor is used to acquire the dynamic pressure of the hydraulic cylinder. The relative displacement of the damper can be measured using... This indicates that the dynamic pressure of the hydraulic cylinder can be used. express.

[0020] Step S200: Timestamp-align and filter the detected physical data to determine standard physical data.

[0021] Due to variations in wire lengths between the sensors and the data acquisition card (DAQ), and slight differences in the analog-to-digital converter (ADC) circuits of each sampling channel, the directly acquired physical data (raw signals) are not strictly aligned on the time axis. This microsecond-level delay, if left unprocessed, can lead to "mismatches" during subsequent fusion analysis, such as misaligning time points. The violent vibrations were incorrectly correlated with the time. The damper displacement response severely misleads the condition assessment. Furthermore, industrial sites are characterized by significant electromagnetic interference and mechanical noise, affecting the raw signal... , ,as well as The data contains high-frequency noise and pulse interference, so processing is necessary. By aligning the timestamps and filtering the physical data, standard physical data suitable for analyzing compressor vibration can be obtained. The timestamp alignment steps are as follows: Assign a timestamp to each sampled data point. Let the first... The original discrete signal sequence of each sensor is ,in It is in local clock time The goal of this method is to align all sensor data to a global reference time series. The specific alignment algorithm uses linear interpolation, and its processing procedure is as follows: For global reference time points This method requires estimating the first... The values ​​from each sensor at that moment. First, the local time sequence of that sensor. In the middle, find the encirclement The two nearest neighbor sampling points satisfy the following condition: Then, the aligned values ​​are calculated using the linear interpolation formula: In the formula, Representing the Each sensor at global reference time Synchronization estimate at the location; Representing the first Each sensor in local time and The original values ​​collected at the location; This represents a global reference time point for the target. They represent encirclement respectively The two sensors were used for local sampling at specific time points.

[0022] Based on the signal in a short time interval The assumption of gradual change within the time interval allows us to approximate the signal's trajectory as a straight line between two points. By performing this operation on all sensor data across the global time grid, we obtain a set of synchronized sensor datasets that are fully aligned to the time axis: At this point, all data points correspond to the same physical moment. This achieves timestamp alignment, and the data at this point is the intermediate physical data. Based on this, this application uses PPS or IEEE-1588 / PTP clocks for channel synchronization on each data acquisition sensor. The filtering process includes: for vibration acceleration signals... The compressor has a rich frequency composition, and its noise level may vary with the compressor's operating conditions. Therefore, we adopt an adaptive Kalman filter, the core formula of which (discrete-time standard form) is as follows: State prediction (time update) formula: , State update (measurement update) formula: , , in: The estimated value of the system state vector, for vibration signals, can be defined as follows: Or simplified to ; The state transition matrix describes how the state evolves from the previous time step to the current time step. For a uniformly changing model, it can be an integral relationship matrix. The covariance matrix of the state estimation error measures the uncertainty of the estimation. This is the process noise covariance matrix, representing the uncertainty of the system model. The "adaptive" aspect is reflected here: real-time calculation of the rate of change of the vibration signal. and order Related to this, for example When the vibration changes drastically ( (large), increase This means placing more trust in new observational data rather than old models, resulting in faster filter responses; The observed value at the current moment, i.e. ; The observation matrix maps the state space to the observation space. If the state is acceleration, then... ; To measure the noise covariance matrix, it is usually set to a fixed small value to characterize the noise level of the sensor itself; The Kalman gain determines the trade-off between predicted and observed values.

[0023] Therefore, by combining the above formulas, the final filtered vibration acceleration can be determined as follows: For damper displacement and pressure For these signals, the changes are usually relatively smooth, but there may be pulse interference (such as circuit glitches). Moving window value filtering is used, and its algorithm formula is as follows: For a length of A sliding window with an odd number of steps, at time... The data in the window is Filtered output value It is the median of all data within this window, sorted by size: In the formula, The length of the sliding window reflects a trade-off between signal smoothness and response delay. The larger the value, the better the smoothing effect, but the greater the phase lag. Therefore, the specific value is determined by the staff based on the actual situation. This represents the median calculation, which can completely filter out isolated pulse points with extremely large or small amplitudes within the window, while preserving the step edges of the signal relatively well. In this application, the intermediate physical data is also denoised using a Kalman filter, and after denoising, the intermediate physical data is subjected to glitches suppression processing using a window midpoint filter to determine the standard physical data.

[0024] In summary, the signal set of raw, asynchronous, and noisy detection physical data can be... It was transformed into a set of pure, spatiotemporally strictly synchronized standard physical data signals, namely .

[0025] Step S300: Based on standard physical data, feature extraction calculation is performed to determine the representative intensity of external excitation, the representative intensity of external excitation, and the representative stability of internal response. Then, the dynamic disturbance index is determined by normalization and weighting based on the representative intensity of external excitation, the representative intensity of external excitation, and the representative stability of internal response.

[0026] External excitation represents intensity, which is a numerical value reflecting the intensity of external vibration. External excitation represents intensity, which is a numerical value reflecting the characteristics of external vibration changes. Internal response represents stability, which is a numerical value representing the stability of the internal response based on vibration. Dynamic disturbance index is a parameter value reflecting whether the system is stable, that is, a numerical value representing the vibration of the compressor. This value is between 0 and 1. The closer it is to 1, the more unstable the system is.

[0027] The analysis of external vibration is achieved using vibration acceleration data. Therefore, vibration acceleration is used to determine the representative intensity and representative strength of the external excitation. The method for determining the representative intensity of the external excitation is as follows: In discrete time series Above, we take a length of A sliding window (e.g., containing data from the past 0.1 seconds) is used to calculate the root mean square value within that window to smooth out instantaneous fluctuations. In the formula, Indicates at time Average intensity of vibrational energy over a period of time, window length The choice needs to strike a balance between rapid response and smooth stability, and the specific choice will be determined by the staff based on the actual situation.

[0028] The formula for calculating the intensity of external stimulus is as follows: In Equation 2.2 Reflects the sampling interval The abrupt acceleration of internal vibration, its absolute value The larger the value, the more severe the vibration and impact, and the greater the threat to the system's instantaneous disturbance. Analysis of the internal response is achieved using the relative displacement of the damper, which includes the following steps: I. Constructing analytic signals and extracting instantaneous phase: To analyze the phase, we first use the Hilbert transform to transform the real displacement signal. Convert into a complex analytic signal ,Right now In the formula, the Hilbert transform... Essentially, it is a linear operator that delays the phase of all frequency components by 90 degrees; therefore, The imaginary part is The orthogonal components, from which we can define the instantaneous phase. : . It describes the angle of the displacement signal in the complex plane, and its variation pattern implies the dynamic characteristics of the response.

[0029] II. Calculation of phase stability factor: A stable response implies a gradual phase change; the time derivative of the phase angle is calculated. To directly reflect the rate of phase change, a normalized stability measure, namely the internal response stability, is defined, which is: In the formula, The larger the value, the more severe the phase jitter and the more unstable the response. This represents the preset phase change rate threshold, an empirical value obtained based on system calibration under normal operating conditions. It represents the maximum allowable steady-state phase change rate; the specific value is determined by the staff based on actual conditions. Normalization and truncation: Normalize the current rate of change to a proportion relative to a threshold, and subtract that value from 1 to obtain an inverse measure of stability (the smaller the value, the less stable the system). The function is set to ensure that the factor is not negative; when the rate of change far exceeds the threshold, the stability factor drops to 0.

[0030] In order to fuse parameters with different physical dimensions, feature normalization is required. The specific steps are as follows: Min-max normalization is performed using the maximum value within the current sliding window. For example, in, It is a small constant that prevents division by zero. At this point... Similar normalization was also performed to obtain .

[0031] Based on the importance of each feature to the system disturbance, different weights are assigned, and linear fusion is performed to determine the dynamic disturbance index. The specific formula is as follows: In the formula, Represents the weighting coefficient, satisfying generally, The weight (corresponding to the intensity of the mutation) may be set too high because shock disturbances are more harmful; It is also crucial in responding to internal instability; The corresponding sustained intensity has a relatively low weight.

[0032] Step S400: Perform data analysis based on standard physical data and dynamic disturbance index to determine the desired damping force.

[0033] The expected damping force is the damping force required for the damper to effectively reduce the current vibration. The specific data analysis method is as follows: Define the control objective of the system: to reduce the actual vibration acceleration of the protected point. Tracking the previous ideal, near-zero expected signal Therefore, tracking error is defined. Its derivative is: In the formula, The deviation between the actual vibration and the desired silent state is the core objective that needs to be minimized. It is the rate of change of the deviation, which contains trend information.

[0034] To achieve fast convergence and strong robustness, the concept of sliding mode control (SMC) is introduced. SMC designs a "sliding surface" to force the system state trajectory to reach and slide along this surface, eventually stabilizing at zero. In this application, a first-order sliding surface is defined. : In the formula, It is a strictly positive design constant, called the sliding surface parameter. When the system state is forcibly constrained to the sliding surface... When, The solution is This means that once the sliding surface is reached, the tracking error... Will at an exponential rate Decay to zero, and dynamic characteristics are completely determined by The decision is unrelated to the inherent uncertainty of the system. Therefore, the design goal of the controller becomes: how to design the control force. , making It rapidly approaches zero and remains so.

[0035] The compressor-damper system involves unknown nonlinear friction, time-varying stiffness, and the complex field-induced dynamics of the magnetorheological fluid itself. Therefore, in order to... Therefore, it is necessary to know the precise mathematical model of the system, so the first intelligent component is introduced: the neural network estimator.

[0036] Suppose that the unknown nonlinear dynamics of the system can be expressed as a perturbation exponent about the system state. and vibration Functions representing its historical values In addition, a control input that can be directly applied. (and final damping force) Its abstract dynamics can be written as: .in, , It is the input vector of the neural network, which integrates the macroscopic stable state and the microscopic motion state of the system. Because... Since the problem is unknown, an online neural network is needed to learn and approximate it. Therefore, a single-hidden-layer feedforward neural network is used, and its mathematical expression is: In the formula, For neural networks to handle real unknown nonlinear functions The estimated value; The output layer weight vector of the neural network is a parameter that needs to be adjusted adaptively online. For is the input vector After the hidden layer activation function (such as the Sigmoid function) The processed output vector, i.e. ,in and It consists of preset fixed input layer weights and biases, responsible for mapping the input to a nonlinear feature space.

[0037] At this point, an estimate of the unknown dynamics is available. A traditional sliding mode control law typically consists of two parts: an equivalent control term (used to counteract known dynamics and allow the system to move along the sliding surface) and a switching control term (used to overcome estimation errors and disturbances and forcibly pull the system back to the sliding surface). The switching term is usually a sign function. This is precisely the root cause of chattering. To eliminate chattering, a second intelligent component is introduced: a fuzzy inference engine, used to smoothly approximate this discontinuous switching. A fuzzy system is defined within this engine. Learn online and generate a smooth, and The relevant robust compensation signal is represented as In the formula, The smooth and robust compensation force output by the fuzzy system; It is an adjustable adaptive parameter vector (corresponding to the consequent parameters of the fuzzy rule); It is obtained after fuzzification, inference, and defuzzification computation, regarding the input. The basis function vector can be understood as a weighted combination of a series of Gaussian functions.

[0038] Construct a Lyapunov candidate function It includes not only the sliding surface It also includes neural network weight estimation errors. and fuzzy parameter estimation error (Among them, super) , (These are ideal, unknown optimal parameters) In the formula, It is a positive definite function, and its rate of change with time If the value is negative definite or semi-negative definite, then the state of the system can be proven. Parameter error and It is globally uniform, bounded, and asymptotically convergent.

[0039] right Find the derivative and substitute it into the abstract dynamics formula. And the control objective, yielding the final adaptive neural network fuzzy sliding mode control law, neural network weight adaptive law, and fuzzy parameter adaptive law, where the adaptive neural network fuzzy sliding mode control law is: In the formula, The second derivative (usually 0) represents the desired acceleration and is used for feedforward compensation; This represents a neural network compensation term used to offset the unknown nonlinear dynamics estimated by the system. This is the key to improving accuracy; Representative proportion feedback item, It is a gain constant that directly drives the sliding surface. Decreasing towards zero determines the basic convergence rate. This represents the fuzzy robustness term, used to smoothly compensate for the approximation error of the neural network. and external disturbances It replaces the traditional dithering switch. .

[0040] The adaptive law of neural network weights is: This formula is a gradient descent type update law. (Learning rate) Its representation is based on the current sliding surface. The size (control bias) and the features currently activated by the neural network (System status) to adjust weights This causes the neural network output to... It can better fit the reality Thus reducing .

[0041] The adaptive law for fuzzy parameters is: Learning rate It is based on the sliding surface and the current fuzzy basis function To adjust the blur parameters This makes the fuzzy compensation force This allows for more accurate compensation of residuals and disturbances. At this point, the formula for expressing the desired damping force is... In the formula, For the desired damping force, This is a coefficient related to the characteristics of the magnetorheological damper, and the specific value is set by the staff.

[0042] Step S500: Analyze the desired damping force to determine the target coil current, and control the damper operation with the target coil current.

[0043] The target current of the coil is the control current required to drive the coil. When the damper operates with the target current, it can generate the desired damping force, thereby achieving vibration reduction of the compressor and effectively reducing its vibration. The relationship between the two can be determined in advance by the staff through multiple experiments, or a corresponding control model can be constructed to solve for the relationship. The solution method is as follows:

[0044] In the formula, The damping force that will be generated The viscous damping force component is related to the piston speed. Proportional ,in is the viscous damping coefficient. For the geometric structure coefficient of the damper; This is the effective length of the piston; Where is the piston radius; The thickness of the annular gap between the piston and the cylinder is derived from the pressure-flow relationship and force balance equation of Bingham plastic fluid in the annular gap in fluid mechanics. The field-induced shear yield stress of the magnetorheological fluid is the magnetic field strength. The function, With coil current Approximately proportional, that is ,in The number of turns of the coil, The specific relationship is usually fitted by experimental data, for example, using hyperbolic tangent or polynomial models: or . The sign function ensures that the direction of the damping force is always opposite to the direction of the piston's velocity.

[0045] In this application, a polynomial model is used. To illustrate with an example, by decomposing the steps, we can obtain: In the formula, This refers to the target current of the coil. When the compressor is subjected to a strong external impact, relying solely on the above adjustments presents two problems: first, a massive instantaneous current is required, placing a burden on the power supply and the coil; second, the enormous energy may exceed its heat dissipation capacity, thus requiring further optimization.

[0046] Step S301: After the dynamic disturbance index is determined, determine whether the dynamic disturbance index is greater than the preset severe collision threshold.

[0047] The severe collision threshold is the minimum dynamic disturbance index that the compressor needs to reach when it is subjected to a large external impact, as set by the staff. The purpose of the judgment is to determine whether the above-mentioned adjustments alone are insufficient.

[0048] Step S3011: If the dynamic disturbance index is not greater than the severe collision threshold, then control the damper operation according to the dynamic disturbance index.

[0049] When the dynamic disturbance index is not greater than the severe collision threshold, it means that the vibration reduction can be carried out using the methods of steps S100-S500 above, so normal analysis and control are sufficient.

[0050] Step S3012: If the dynamic disturbance index is greater than the severe collision threshold, control the preset hydraulic auxiliary system to operate to generate a buffer force.

[0051] When the dynamic disturbance index exceeds the severe impact threshold, it indicates a severe impact. In this case, relying solely on the methods described above is insufficient for vibration reduction. Therefore, a hydraulic auxiliary system is activated to generate a buffering force, enabling both systems to work together to reduce vibration and improve the overall vibration reduction effect. The hydraulic auxiliary system is configured by the operator based on the actual situation. In this application, a hydraulic cylinder is coupled along the same axis as the damper piston rod to serve as the hydraulic auxiliary system. When a severe impact causes the piston rod to move at high speed, the hydraulic cylinder generates a buffering force to share the impact load, thereby reducing the instantaneous pressure on the magnetorheological damper body.

[0052] Reference Figure 2 Based on the same inventive concept, embodiments of the present invention provide a compressor intelligent vibration reduction system based on magnetorheological effect, comprising: The data acquisition module acquires the physical data from the detection. The data preprocessing module performs timestamp alignment and filtering on the detected physical data to determine standard physical data; The dynamic disturbance index calculation module performs feature extraction calculations based on standard physical data to determine the external excitation representative intensity, external excitation representative intensity, and internal response representative stability. It then performs normalization and weighting processing based on the external excitation representative intensity, external excitation representative intensity, and internal response representative stability to determine the dynamic disturbance index. The desired damping force calculation module performs data analysis based on standard physical data and dynamic disturbance index to determine the desired damping force; The damping execution module analyzes the desired damping force to determine the target coil current and controls the damper operation based on the target coil current. The auxiliary buffer module is used to control the operation of the preset hydraulic auxiliary system to generate a buffering force.

[0053] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0054] This invention provides a computer-readable storage medium storing a computer program that can be loaded and executed by a processor to implement a smart vibration reduction method for compressors based on magnetorheological effects.

[0055] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0056] Based on the same inventive concept, embodiments of the present invention provide a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor to implement a smart vibration reduction method for a compressor based on magnetorheological effects.

[0057] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0058] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for intelligent vibration reduction of a compressor based on magnetorheological effect, characterized in that, include: Acquire physical data for testing, including vibration acceleration, relative displacement of the damper, and dynamic pressure of the hydraulic cylinder; The detected physical data is timestamped and filtered to determine the standard physical data. Feature extraction calculations are performed based on standard physical data to determine the intensity, severity, and stability of external excitation, and then normalization and weighting are performed based on the intensity, severity, and stability of external excitation and the stability of internal response to determine the dynamic disturbance index. The desired damping force is determined by data analysis based on standard physical data and dynamic disturbance index. The target current of the coil is determined by analysis based on the desired damping force, and the operation of the damper is controlled by the target current of the coil.

2. The intelligent vibration reduction method for compressors based on magnetorheological effect according to claim 1, characterized in that, The steps involved in timestamp-aligning and filtering the detected physical data to determine the standard physical data include: The detected physical data is channel-synchronized according to the PPS or IEEE-1588 / PTP clock to output intermediate physical data. The intermediate physical data is denoised using a Kalman filter, and then glitches are suppressed using a window midpoint filter to determine the standard physical data.

3. The intelligent vibration reduction method for compressors based on magnetorheological effect according to claim 1, characterized in that, After the dynamic disturbance index is determined, the intelligent vibration reduction method for compressors based on the magnetorheological effect also includes: Determine whether the dynamic disturbance index is greater than the preset severe collision threshold; If the dynamic disturbance index is not greater than the severe collision threshold, the damper operation is controlled according to the dynamic disturbance index. If the dynamic disturbance index is greater than the severe collision threshold, the preset hydraulic auxiliary system is controlled to operate to generate a buffering force.

4. A compressor intelligent vibration reduction system based on magnetorheological effect, used to implement the compressor intelligent vibration reduction method based on magnetorheological effect as described in any one of claims 1-3, characterized in that, include: The data acquisition module acquires the physical data from the detection. The data preprocessing module performs timestamp alignment and filtering on the detected physical data to determine standard physical data; The dynamic disturbance index calculation module performs feature extraction calculations based on standard physical data to determine the external excitation representative intensity, external excitation representative intensity, and internal response representative stability. It then performs normalization and weighting processing based on the external excitation representative intensity, external excitation representative intensity, and internal response representative stability to determine the dynamic disturbance index. The desired damping force calculation module performs data analysis based on standard physical data and dynamic disturbance index to determine the desired damping force; The damping execution module analyzes the desired damping force to determine the target current of the coil and controls the operation of the damper based on the target current of the coil.

5. The intelligent vibration reduction system for compressors based on magnetorheological effect according to claim 4, characterized in that, Also includes: The auxiliary buffer module is used to control the operation of the preset hydraulic auxiliary system to generate a buffering force.

6. A smart terminal, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any one of claims 1 to 3 for intelligent vibration reduction of a compressor based on magnetorheological effect.

7. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1 to 3, which is a smart vibration reduction method for compressors based on magnetorheological effects.

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