A method for on-line detection of particle content and moisture content of hydraulic medium

Abstract

The present application relates to the technical field of online detection of physical and chemical properties of hydraulic medium, and discloses a kind of hydraulic medium particulate and moisture content online detection method, comprising: collecting the complex impedance response data and fluid pressure drop data of hydraulic medium flowing through detection unit, paper pulp fiber sensing matrix with acrylic monomer surface grafting and three-dimensional network structure are arranged in detection unit;Using the sensing matrix to adsorb dispersed water and convert it into bound water, extract the impedance reference level when phase angle offset is stable to determine the moisture content;Using the interception mechanism of the sensing matrix, based on the fluid pressure drop rate to determine the particulate accumulation concentration, the present application eliminates the interference of multiphase flow interface on polarization signal through water phase state conversion, improves the signal-to-noise ratio of detection under complex conditions, realizes the isomorphic perception of physical and chemical analysis and physical interception.

Description

Technical Field

[0001] This invention belongs to the field of online detection technology of the physical and chemical properties of hydraulic media, and particularly relates to an online detection method for particulate matter and moisture content in hydraulic media. Background Technology

[0002] Current hydraulic systems rely on hydraulic media for power transmission and control. The cleanliness of the media is highly correlated with the system's operational reliability. Statistical data shows that over 70% of hydraulic system failures originate from media contamination, with moisture, solid particles, and air being the main contaminants. In the field of hydraulic media monitoring, external optical sensors or capacitive sensors are typically used to measure the concentration of particulate matter and moisture content in the media. Under real-world conditions, the mechanical shearing of the hydraulic pump and the throttling effect of the valve orifice can generate tiny bubbles within the fluid and easily form complex water-in-oil emulsion droplets. When such objectively existing multiphase fluids flow through the detection interface, they cause sudden jumps in the local dielectric constant and generate disordered light path scattering. As a result, the detection signal deviates from the reference level, generating random pulse fluctuations and leading to measurement errors.

[0003] To suppress multiphase flow interference, conventional designs tend to place defoaming devices or static demulsification units at the front end of the sensor. This configuration increases the physical space occupied by the system, and because it prolongs the medium flow path, it causes a lag in the response of monitoring data, which cannot meet the real-time online monitoring requirements. In addition to the limitations of hardware structure isolation, the software control method also has shortcomings. For example, Chinese invention patent application CN102645456A discloses an online oil water content detection system based on the operation of a hydraulic lubrication station. It uses the rotation speed of rotating machinery to reflect the gas content and establishes a neural network to fuse multi-sensor data of capacitance, temperature and rotation speed to eliminate the influence of bubbles. However, the evolution of bubbles is affected by pressure fluctuations, viscosity-temperature characteristics and pipeline geometric induced coupling. The macroscopic rotation speed model is difficult to reflect the microscopic polarization law of the multiphase flow interface. The physical phase of water is not changed, and the detection signal still has nonlinear distortion under the interference of emulsion and discrete bubbles. The generalization ability and stability of the model are limited.

[0004] Therefore, the technical problem to be solved by this invention is how to use composite materials with high water absorption properties to capture free water in hydraulic media and eliminate phase interface interference, and combine the pressure characteristics during the interception process to achieve simultaneous analysis of water content and particle concentration. Summary of the Invention

[0005] The present invention aims to solve the problems of signal interference and low coordination between purification and detection in hydraulic media under multiphase flow conditions.

[0006] In this technical solution, an online detection method for particulate matter and moisture content in hydraulic media includes the following steps: Step S1: Obtain the complex impedance response data of the hydraulic medium flowing through the detection unit and in a dynamic circulation state, as well as the fluid pressure drop data between the inlet and outlet of the detection unit. Step S2: Determine the moisture content based on the complex impedance response data; capture dispersed moisture in the hydraulic medium using the hydrophilic groups inside the sensing matrix, and induce the dispersed moisture to be converted into bound moisture locked inside the sensing matrix network structure through hydrogen bonding; use the central tube and the protective net surrounding the central tube in the detection unit as the inner and outer electrodes, respectively; extract the impedance reference level when the signal phase angle offset in the complex impedance response data reaches the preset stable range through the inner and outer electrodes, and calculate the moisture content based on the mapping relationship between the impedance reference level and the moisture content; Step S3: Determine the cumulative concentration of particulate matter based on fluid pressure drop data; utilize the physical interception effect of the sensing matrix on solid pollutants to obtain the flow rate of the hydraulic medium flowing through the sensing matrix and the dynamic viscosity at the current temperature, monitor the real-time gradient of the fluid pressure drop data over time, and calculate the cumulative concentration of particulate matter based on the real-time gradient, flow rate, and dynamic viscosity according to the preset interception pressure drop mapping rule.

[0007] Preferably, the process of extracting the impedance reference level in step S2 includes: monitoring the phase angle offset of the complex impedance response data within the excitation frequency range of 10Hz to 1MHz; calculating the fluctuation variance of the phase angle offset; and determining the value when the fluctuation variance is less than 0.01 for 500ms consecutively. When the physical phase transition from dispersed water to bound water is completed, the average real part of the complex impedance response data at this time is determined as the impedance reference level.

[0008] Preferably, the sensing matrix is ​​supported by pulp fibers, and the surface of the pulp fibers is grafted with a spatial cross-linked network containing hydrophilic groups; bound water is locked in the spatial cross-linked network through hydrogen bonds to maintain the dielectric environment at the interface between the hydraulic medium and the sensing matrix in a quasi-steady state and to suppress the macroscopic geometric deformation of the sensing matrix during the water absorption process.

[0009] Preferably, before step S1, the method further includes: filling the inner cavity of the hydraulic oil tank with inert gas to maintain the pressure at the top of the hydraulic oil tank in a slightly positive pressure range of 0.02 MPa to 0.05 MPa.

[0010] Preferably, the process of determining the moisture content in step S2 further includes: synchronously acquiring the real-time temperature of the hydraulic medium, and performing temperature compensation on the impedance reference level based on the real-time temperature; using a preset impedance temperature compensation curve, uniformly converting the impedance reference level at different working temperatures to the calibrated impedance value corresponding to 40°C, so as to eliminate the systematic error of the hydraulic medium viscosity-temperature characteristics on the complex impedance response data.

[0011] Preferably, the process of acquiring fluid pressure drop data in step S3 includes: synchronously acquiring the absolute pressure signals at the inlet and outlet of the detection unit; calculating the difference between the absolute pressure signals to generate the original sequence of fluid pressure drop data; and using a median filtering algorithm to filter out high-frequency pressure fluctuations caused by hydraulic pump source pulsation.

[0012] Preferably, in step S1, the detection unit is connected in series in the return oil line of the hydraulic system; the local flow shear force generated by the return oil line increases the collision frequency between the dispersed water in the hydraulic medium and the hydrophilic groups on the surface of the sensing matrix.

[0013] Preferably, after steps S2 and S3, the method further includes: comparing the calculated moisture content and particulate matter cumulative concentration with preset system health thresholds; when the moisture content exceeds 500 ppm or the slope of the change in particulate matter cumulative concentration exceeds the preset threshold, outputting a warning signal to the external control terminal indicating that the hydraulic medium is in a state of accelerated contamination.

[0014] Preferably, after outputting the warning signal indicating that the hydraulic medium is in a state of accelerated contamination, the method further includes: calculating the pressure drop saturation point of the sensing matrix based on the real-time gradient of the cumulative particulate matter concentration, and calculating the remaining service life of the sensing matrix from the pressure drop saturation point, so as to generate maintenance cycle prediction data of the hydraulic system and synchronize it to the maintenance management module.

[0015] Compared with existing technologies, the online detection method for particulate matter and moisture content in hydraulic media of the present invention has the following advantages: 1. In the online detection of particulate matter and moisture content in hydraulic media, a three-dimensional network structure composite material formed by in-situ polymerization of pulp fiber and acrylic monomer captures and transforms free water and emulsified water in the hydraulic media into bound water within the polymer network. This eliminates the disordered interference of the gas-liquid multiphase flow interface on the polarization signal in the high-pressure circulation pipeline from the physical phase state level. It solves the technical contradiction that traditional sensors are prone to electrical signal distortion in emulsified oil environments, and enables the extracted equivalent distributed impedance signal to have high physical stability and measurement signal-to-noise ratio, thereby improving the objectivity of moisture content analysis.

[0016] 2. This invention utilizes the interception and accumulation mechanism of solid pollutants by a particle filtration module, transforming the complex particle counting process into the extraction of the slope of the change in static pressure difference across the two ends of a fixed porous medium. This avoids false positive alarms caused by light scattering in environments with high turbidity of oil or microbubbles due to optical detection methods. It achieves a linear mapping between the cumulative concentration of particulate matter and the pressure drop gradient of the flow field, making the detection process no longer dependent on fragile external sensor probes and enhancing the adaptability of the detection method under harsh working conditions.

[0017] 3. This invention utilizes the synergistic effect of pressure balancing, inert gas protection module, and composite purification device. By using inert gas bags to maintain a slight positive pressure in the oil tank, isolate atmospheric oxygen, and inhibit oil oxidation and deterioration, and by using highly absorbent materials to continuously lock in trace amounts of moisture, it maintains the chemical stability of the hydraulic medium measurement reference point. This avoids chemical interference and zero-point drift caused by acidic substances or sludge generated by oil aging on the electrode structure, ensuring the reference consistency of the online monitoring system during long-term operation. Attached Figure Description

[0018] Figure 1 This is a flowchart of the steps for detecting moisture and particulate matter in hydraulic media based on complex impedance and fluid pressure drop according to the present invention. Figure 2 This is a diagram of the logic architecture for multi-node data processing and status early warning control within the hydraulic system of this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] It should be noted that all directional and positional terms used in this invention, such as: up, down, left, right, front, back, vertical, horizontal, inner, outer, top, bottom, transverse, longitudinal, center, etc., are only used to explain the relative positional relationship and connection between components in a specific state (as shown in the accompanying drawings). They are only for the convenience of describing this invention and do not require that this invention be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the descriptions of "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0021] In the description of this invention, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to the internal connection of two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0022] In the description of this specification, references to the terms "an embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0023] A method for online detection of particulate matter and moisture content in hydraulic media includes the following steps: Step S1: Obtain the complex impedance response data of the hydraulic medium flowing through the detection unit and in a dynamic circulation state, as well as the fluid pressure drop data between the inlet and outlet of the detection unit. Step S2: Determine the moisture content based on the complex impedance response data; capture dispersed moisture in the hydraulic medium using the hydrophilic groups inside the sensing matrix, and induce the dispersed moisture to be converted into bound moisture locked inside the sensing matrix network structure through hydrogen bonding; use the central tube and the protective net surrounding the central tube in the detection unit as the inner and outer electrodes, respectively; extract the impedance reference level when the signal phase angle offset in the complex impedance response data reaches the preset stable range through the inner and outer electrodes, and calculate the moisture content based on the mapping relationship between the impedance reference level and the moisture content; Step S3: Determine the cumulative concentration of particulate matter based on fluid pressure drop data; utilize the physical interception effect of the sensing matrix on solid pollutants to obtain the flow rate of the hydraulic medium flowing through the sensing matrix and the dynamic viscosity at the current temperature, monitor the real-time gradient of the fluid pressure drop data over time, and calculate the cumulative concentration of particulate matter based on the real-time gradient, flow rate, and dynamic viscosity according to the preset interception pressure drop mapping rule.

[0024] Preferably, the process of extracting the impedance reference level in step S2 includes: monitoring the phase angle offset of the complex impedance response data within the excitation frequency range of 10Hz to 1MHz; calculating the fluctuation variance of the phase angle offset; and determining the value when the fluctuation variance is less than 0.01 for 500ms consecutively. When the physical phase transition from dispersed water to bound water is completed, the average real part of the complex impedance response data at this time is determined as the impedance reference level.

[0025] Preferably, the sensing matrix is ​​supported by pulp fibers, and the surface of the pulp fibers is grafted with a spatial cross-linked network containing hydrophilic groups; bound water is locked in the spatial cross-linked network through hydrogen bonds to maintain the dielectric environment at the interface between the hydraulic medium and the sensing matrix in a quasi-steady state and to suppress the macroscopic geometric deformation of the sensing matrix during the water absorption process.

[0026] Preferably, before step S1, the method further includes: filling the inner cavity of the hydraulic oil tank with inert gas to maintain the pressure at the top of the hydraulic oil tank in a slightly positive pressure range of 0.02 MPa to 0.05 MPa.

[0027] Preferably, the process of determining the moisture content in step S2 further includes: synchronously acquiring the real-time temperature of the hydraulic medium, and performing temperature compensation on the impedance reference level based on the real-time temperature; using a preset impedance temperature compensation curve, uniformly converting the impedance reference level at different working temperatures to the calibrated impedance value corresponding to 40°C, so as to eliminate the systematic error of the hydraulic medium viscosity-temperature characteristics on the complex impedance response data.

[0028] Preferably, the process of acquiring fluid pressure drop data in step S3 includes: synchronously acquiring the absolute pressure signals at the inlet and outlet of the detection unit; calculating the difference between the absolute pressure signals to generate the original sequence of fluid pressure drop data; and using a median filtering algorithm to filter out high-frequency pressure fluctuations caused by hydraulic pump source pulsation.

[0029] Preferably, in step S1, the detection unit is connected in series in the return oil line of the hydraulic system; the local flow shear force generated by the return oil line increases the collision frequency between the dispersed water in the hydraulic medium and the hydrophilic groups on the surface of the sensing matrix.

[0030] Preferably, after steps S2 and S3, the method further includes: comparing the calculated moisture content and particulate matter cumulative concentration with preset system health thresholds; when the moisture content exceeds 500 ppm or the slope of the change in particulate matter cumulative concentration exceeds the preset threshold, outputting a warning signal to the external control terminal indicating that the hydraulic medium is in a state of accelerated contamination.

[0031] Preferably, after outputting the warning signal indicating that the hydraulic medium is in a state of accelerated contamination, the method further includes: calculating the pressure drop saturation point of the sensing matrix based on the real-time gradient of the cumulative particulate matter concentration, and calculating the remaining service life of the sensing matrix from the pressure drop saturation point, so as to generate maintenance cycle prediction data of the hydraulic system and synchronize it to the maintenance management module.

[0032] Example 1: In the operating environment of a hydraulic power unit with pressure fluctuations and moisture intrusion, the hydraulic medium is subjected to shearing action by the oil pump, generating dispersed bubbles and water-in-oil emulsion droplets. When the multiphase fluid flows through the sensing interface, dielectric constant jumps and light scattering occur, causing pulse fluctuations in the detection signal and deviations from the preset benchmark. The hydraulic medium in a dynamic circulation state is acquired and guided to flow through a composite purification device. The particle filter module within the device intercepts solid pollutants. The static pressure difference data between the inlet and outlet of the module is collected, and the slope of this data change within a unit time window is calculated. Based on the preset mapping function from pressure difference to particulate matter concentration, the solid particulate matter in the medium is calculated. The hydraulic medium penetrates the superabsorbent composite filter module, which is composed of a three-dimensional network structure formed by in-situ polymerization of pulp fiber and acrylic monomer. The composite material adsorbs and locks free water and emulsified water in the oil. Through hydrogen bonding, the dispersed water is converted into bound water locked inside the sensing matrix. The transformation of the physical phase of water eliminates the polarization interference of the multiphase flow interface on the electrical signal. The central tube of the module and the protective net surrounding it are used as the inner and outer electrodes, respectively, to apply low-frequency AC excitation signals. The equivalent distributed impedance of the superabsorbent composite particle layer filled between the two electrodes is measured, and the signal phase angle offset in the complex impedance response data is extracted.

[0033] The water content of the medium is calculated based on the mapping relationship between impedance reference level and moisture content. According to the multiphase microfluidics mechanism, when the fluid crosses the fiber network, it forms local laminar shear stress, causing large-scale emulsion droplets to continuously break up, increasing the specific surface area of ​​the dispersed aqueous phase and the surface of the pulp fibers, and improving the mass transfer rate of water into the capillary channels inside the polymer network. This matches the short-cycle sensing time response specifications under dynamic fluid circulation conditions. Following the theory of polymer thermodynamics, the three-dimensional spatial network constructed by in-situ crosslinking of acrylic monomers generates an elastic contraction mechanism to counteract the osmotic pressure of free water molecules, offsetting the macroscopic geometric deformation trend caused by structural swelling during the material's water absorption process. This ensures that the physical spacing between the distributed electrodes and the distribution of the dielectric constant maintain spatial consistency. Specifically, the sensing matrix adopts a dual-scale confined structure in its physical construction, in which the rigid segments of ungrafted cellulose constitute a spatially interwoven rigid structure. A macroscopic support framework anchors the in-situ polymerized three-dimensional network structure between the framework's intersections. When the polymerized network absorbs moisture and swells, the strong mechanical resistance and physical restraint of the macroscopic framework force its swelling deformation to extend into the microscopic capillary pores between fibers. This ensures that the volume expansion is only manifested as a reduction in the effective cross-sectional area of ​​the fluid penetration channel at the microscopic scale, while maintaining the overall geometric dimensions of the sensing matrix and ensuring that the macroscopic interpolar spacing remains absolutely unchanged. This achieves a self-consistent unity between macroscopic dimensional stability and microscopic resistance abrupt changes in physical laws. The physical interception structure and the physicochemical analysis structure share the same physical carrier. By synchronously acquiring the flow field pressure drop and electric field impedance changes generated when the fluid passes through the interception interface, signal noise interference generated by the multiphase flow interface is eliminated. The hydraulic medium purification process and the online sensing process of the pollution state achieve spatial and logical isomorphism.

[0034] Example 2: The current test was conducted on a closed-loop hydraulic circulation test bench simulating industrial load. This test bench included an axial piston pump with a displacement of 40 mL / r and a load simulation valve assembly with a working pressure range of 0 to 31.5 MPa. The hydraulic oil tank was connected to an oil-resistant elastic bladder filled with high-purity nitrogen via a pressure regulating valve to maintain the stability of the hydraulic medium's physical properties. The detection unit was filled with a pre-prepared sensing matrix, specifically pulp fibers grafted with acrylic monomers and possessing a three-dimensional network structure. The test environment temperature was maintained at 40 ± 0.5℃, and the sampling period was... To balance signal resolution and data processing load, the sampling frequency was set to 10Hz. Set to 0.1s, and considering the inherent mechanical pulsations and high-frequency hydraulic noise generated by the 40mL / r axial piston pump during oil discharge, as well as the high-pressure load operation, the processor continuously acquires the original pressure sequence at this 10Hz frequency. Then, a one-dimensional sliding filter window of 11 discrete sampling points is set to execute a median filtering algorithm. This specific window span precisely encompasses the trough-to-peak cycle of a complete pulse transition per revolution under low-speed operation of the piston pump source. This allows for effective filtering of transient abnormal pressure spikes using a sorting and elimination mechanism. The experiment preserved the physical characteristics of the slow, gradual increase in long-period low-frequency pressure drop gradient caused by the slow interception and accumulation of solid particles on the filter element surface. The experimental group used a sensing matrix with an in-situ polymerized three-dimensional network structure, while the control group used a regular fiber filter element without monomer grafting treatment. Pure water was injected into the circulation pipeline in four gradient injections using a precision micro-pump, causing the water content in the hydraulic oil to reach 520 ppm, 1015 ppm, 1492 ppm, and 1985 ppm respectively. Complex impedance response data and phase angle offset were extracted from the experimental group at each water gradient. The signal converged to a stable range within 120 seconds after injection was completed. This value showed a gradient change with increasing moisture content. The phase angle shift at a moisture content of 1985 ppm was 2.43 times that at the baseline moisture content. Due to the strong hydrophilic groups generated by the acrylic monomer capturing dispersed moisture and converting it into bound moisture through hydrogen bonding, the random pulsation rate of the experimental signal amplitude remained within 0.8%. In contrast, the control group lacked a moisture locking mechanism, and the emulsion water droplets in the oil caused local dielectric constant jumps, resulting in a random fluctuation of 18.4% in the signal amplitude, making it impossible to establish a stable impedance reference level. During the determination of the convergence range, the system simultaneously monitored the phase angle shift of the complex impedance response data and calculated its fluctuation variance. The variance threshold of 0.01 rad² and the continuous 500 ms time window constraint are the boundary of the limiting physical criteria derived from multiple sets of flow field observation experiments. The 500 ms lower bound of the parameter completely covers the maximum relaxation time of the vortex shedding and breakup of the hydraulic medium with a viscosity of 46 cSt under the worst turbulent state. When the monitoring parameters meet the above conditions, it is confirmed at the fluid dynamics level that the emulsion water phase in the hydraulic medium no longer undergoes random splitting and recombination with local turbulent kinetic energy, but is completely and stably locked inside the polymer cross-linked network, and the dielectric distribution reaches an absolute steady state in macroscopic statistics.

[0035] During the particulate matter detection validation process, ISOMTD standard test dust is injected into the system. As particulate matter accumulates and is intercepted on the surface of the particulate filter module, the fluid pressure drop between the inlet and outlet of the module is collected. Data, calculated rate of change of pressure drop The pressure drop rate exhibits a linear mapping relationship with the particle injection concentration. When the particle concentration increases within the range of 0 to 25 mg / L, the rate of change of pressure drop increases linearly from 0.015 MPa / min to 0.218 MPa / min. In tests with injection concentrations exceeding 30 mg / L, the slope of the pressure drop increase shows a nonlinear jump due to the filter cake layer thickness reaching the critical saturation point. This performance inflection point provides experimental evidence for determining the effective range of solid particulate matter concentration. Based on the complex impedance reference level measured by the sensor and the slope of the fluid pressure drop change, combined with the preset mapping function, the content of contaminant components in the hydraulic medium is inverted. The measured data show that the moisture detection error is no higher than 4.2%, and the accuracy of solid particulate matter concentration inversion matches the laboratory microscopic counting method with a degree of 96.5%. This confirms that this method establishes a physical polarization environment through moisture phase transformation and, in conjunction with flow field pressure drop gradient sensing, obtains quantitative indicators of the physicochemical characteristics of the hydraulic medium under multiphase flow interference conditions.

[0036] Example 3: This example combines Figures 1 to 2 This document describes an online detection method for particulate matter and moisture content in hydraulic media. Figure 1 As shown, step S1 involves acquiring the complex impedance response data of the hydraulic medium in a dynamic circulation state and the fluid pressure drop data between the inlet and outlet of the detection unit. This step S1 is further divided into step S2 on the left, which uses the complex impedance response data to determine the moisture content, and step S3 on the right, which uses the fluid pressure drop data to determine the cumulative concentration of particulate matter. Step S2 points downwards to the physical phase transformation stage, which utilizes the hydrophilic groups inside the sensing matrix to capture dispersed moisture and induces it to transform into bound moisture locked inside the network structure through hydrogen bonding. This eliminates the random polarization interference of the gas-liquid multiphase flow interface on the electrical signal and establishes a stable physical polarization environment. It further points downwards to the electrical signal polarization extraction and calculation stage, which receives the underlying detection... The measurement unit electrode uses data from the inner electrode center tube and the outer electrode protection network. It extracts the impedance reference level when the signal phase angle offset in the complex impedance response data reaches the preset stable range through the inner and outer electrodes. The moisture content is calculated based on the mapping relationship between the impedance reference level and the moisture content. Step S3 on the right points downward to the physical interception parameter acquisition stage, which uses the physical interception effect of the sensing matrix on solid pollutants to obtain the flow rate of the hydraulic medium flowing through the sensing matrix and the dynamic viscosity at the current temperature. It further points downward to the flow field mapping inversion stage, which monitors the real-time gradient of the fluid pressure drop data over time and calculates the cumulative concentration of particulate matter based on the real-time gradient, flow rate, and dynamic viscosity according to the preset interception pressure drop mapping rule.

[0037] like Figure 2As shown, the hydraulic system, as the starting point, is associated with four data processing nodes: acquiring complex impedance response data, converting bound state moisture, extracting impedance reference level, and performing temperature compensation. The data from these four nodes all point to the node for determining moisture content. Simultaneously, the hydraulic system is also associated with two nodes: acquiring fluid pressure drop data and intercepting solid pollutants. The data from these two nodes jointly point to the node for determining cumulative particulate matter concentration. The nodes for determining moisture content and determining cumulative particulate matter concentration both point to the node for outputting early warning signals. This node for outputting early warning signals transmits information to the external control terminal outside the boundary. In addition, the node for determining cumulative particulate matter concentration also points separately to the node for generating maintenance cycle prediction data. This node for generating maintenance cycle prediction data points to the left to the node for calculating pressure drop saturation point and transmits information to the maintenance management module outside the boundary.

[0038] Example 4: When the hydraulic system changes the brand of the medium or the basic viscosity of the medium deviates due to long-term operation, the mapping coefficient between the static pressure difference and the particulate matter content preset in the detection unit and the real-time flow field characteristics will mismatch, causing the inversion value of solid particulate matter content to drift. At this time, it is necessary to reconstruct the sensing benchmark of the system through a determined process flow and calibration method. The preparation process of the sensing matrix is ​​as follows: 100 parts by weight of pulp fiber with a cellulose content of not less than 90% and an average fiber length of 2 to 3 mm are selected and soaked in deionized water at 25°C for 2 hours to swell. 20 parts by weight of acrylic monomer and 0.5 parts by weight of potassium persulfate are added as initiators. The mixture is placed in a reaction vessel with temperature control function, heated to 75°C at a stirring speed of 250 rpm and maintained at a constant temperature for 4 hours. A three-dimensional network structure with cross-linking density is generated by in-situ polymerization of monomer on the fiber surface. The reaction product is washed with anhydrous ethanol until neutral and vacuum dried at 60°C for 12 hours to obtain the composite material used to fill the detection unit.

[0039] The calibration process is initiated at the initial stage of system operation or during media replacement, and the initial static pressure difference of the clean hydraulic medium at the preset rated flow rate is collected. Using this as a reference for flow field resistance, ISOMTD standard dust was added to the circulation loop in five gradient increments using a precision powder injection device, with each increment set at 5 mg / L. The rate of change of fluid pressure drop during the stable operating cycle after each injection was recorded. Based on the least squares method for injection concentration By performing a linear fit with the rate of change of pressure drop, the inductance coefficient in the mapping function is determined. With intercept term The specific calculation formula is as follows: ,in, The content of solid particulate matter in the medium. The rate of change of pressure drop across the particle filter module. The inductive coefficient is used to characterize the interception properties of materials. To reflect the intercept term of the flow field background noise, based on the fluid dynamics principles of porous media, the absorption of free water by the polymer network is accompanied by the expansion of the micropore volume, leading to a nonlinear surge in fluid penetration resistance unrelated to the accumulation of solid particles. To isolate the flow field pressure drop interference caused by the swelling effect, the system performs a pre-decoupling calibration on the solid particle concentration inversion process. The processor introduces a dynamic compensation term for water concentration, correcting the theoretical model for calculating particle concentration. Based on the effective dielectric model of the composite system, the mean real part of the complex impedance response data within the polarization stability range is extracted as the impedance reference level. Simultaneous calculation of moisture content is then performed, and the calculation model is set as follows: , where variables The coefficient representing the swelling pressure drop coupling caused by an increase in unit water concentration is obtained by guiding clean standard oil samples at different water content levels through the sensing matrix and recording the corresponding static pressure drop gradient parameters; variables Represents the real-time moisture content of the hydraulic medium; variable Refers to the mean value parameter of the real part of the complex impedance extracted when the physical transformation of the phase state is completed; coefficient and The proportional gain and basic bias of the impedance real part parameter and the moisture concentration mapping function are respectively constructed. These are determined by data fitting after the detection unit is injected with a reference oil sample of known moisture concentration, calibrated by the Karl Fischer method in the laboratory. To eliminate the timing misalignment between the transient high-frequency extraction of pressure drop and the low-frequency steady-state determination of impedance polarization under dynamic changing conditions of multiphase flow, the processor establishes a timestamp queue in memory to execute a state alignment mechanism. During the transition period before the impedance reference level reaches the steady-state determination condition, the moisture concentration M in the theoretical model correction term directly calls and maintains the historical calculated value successfully locked in the previous continuous steady-state window as the feedforward compensation reference. This continues until the impedance phase fluctuation of the current period converges and the latest steady-state impedance reference level is extracted, and then the compensation formula is adjusted accordingly. The value is updated in a stepwise iterative manner to ensure that the real-time pressure drop gradient sampled at millisecond-level high speed can always be synchronously solved with the effective water state variables that are perfectly aligned with the time scale.

[0040] In the convergence determination of moisture detection, the system extracts the phase angle offset from the complex impedance response data. The first derivative is used, and the sliding observation window length is set to 30s. If the rate of change of phase angle offset of all sampling points within the window is less than 0.05%, the signal is judged to have reached physical stability. At this time, the extracted impedance reference level is used to calculate the moisture content. This method eliminates the influence of different physical and chemical properties of the medium on the sensing accuracy by quantitatively constraining the preparation process of the sensing matrix and calibrating the coefficients of the mapping function on site. This enables the sensing range of the detection system to cover the state evolution of the hydraulic medium throughout its entire life cycle, and realizes the dynamic approximation of the detection results to the laboratory analysis values.

[0041] Example 5: In an operating scenario where the hydraulic medium temperature frequently fluctuates between 20 and 70°C due to drastic load fluctuations, the complex impedance characteristics of the hydraulic medium exhibit a nonlinear shift with temperature. A temperature sensor installed at the inlet of the composite purification device is used to collect the medium temperature data in real time, based on the temperature compensation coefficient stored in the processor. For the original phase angle offset The correction is made, and the calculation formula is as follows. ,in, This is the corrected phase angle offset. This represents the phase angle offset of the original acquisition. The temperature of the medium is collected in real time. The preset calibration reference temperature is 40℃. This is the temperature compensation coefficient for the polarization intensity of the medium; When the system is in the initial commissioning stage, such as when replacing the high-absorbency composite filter module or changing the entire hydraulic medium, the characteristic deviation of the electrical signal caused by the physical bulk density of the sensing matrix and the initial wetting state of the fiber surface is eliminated by the pre-calibration procedure. This procedure guides the hydraulic medium, which is in a clean and dry state, to circulate for 15 minutes under conditions without moisture intrusion, and continuously collects the measured data of the complex impedance of the sensing matrix within 5 consecutive sampling windows. The calculated arithmetic mean is used as the impedance reference level. Write it into non-volatile memory as the initial zero point for the moisture content calculation formula.

[0042] Example 6: When the system is in the self-testing condition after initial inspection of oils with different base viscosities or after replacement of the detection unit, the physical filling consistency of the sensing matrix and the deviation of the electrode geometry are eliminated through a standardized initial calibration method. A coaxial structure with an inner electrode center tube outer diameter of 12mm and an outer electrode protective mesh inner diameter of 22mm is selected. The radial gap between the two electrodes is measured using a measuring tool, and four evenly distributed measuring points are taken in the circumferential direction to ensure that the inter-electrode filling thickness is maintained within the range of 5.0±0.1mm. Before the medium flows, the low-frequency AC signal source is turned on and the frequency is adjusted to 50Hz. The initial equivalent distributed impedance amplitude under no-load conditions is collected. The hydraulic medium to be tested is guided to fill the gap between the electrodes. Ten sets of distributed impedance data are continuously collected at a constant temperature of 40℃, and the variance is calculated. When the variance is less than 0.2%, the average value of the set of data is determined as the fundamental impedance modulus of the medium. ,in This represents the initial equivalent distributed impedance amplitude in air medium. This is the modulus of the basic impedance after being filled with hydraulic medium.

[0043] When the acrylic monomer cross-linked network inside the sensing matrix approaches the saturation point of moisture adsorption or the particle filter module becomes clogged, the system monitors the phase angle offset in real time. The purification efficiency is determined by the trajectory of the change over time, and the second derivative of the phase angle offset with respect to time is calculated. and with performance degradation slope Compare the results; if five consecutive sampling points meet the requirements... Greater than And the static pressure difference during flow If the initial pressure drop exceeds 2.5 times the rated flow rate, the purification and sensing unit is determined to have entered the nonlinear saturation region and will output a maintenance signal. This is the second derivative of the phase angle offset with respect to time. The preset threshold for determining the performance degradation slope. To detect the static pressure difference between the inlet and outlet of the detection unit, while outputting maintenance signals, the system extracts the current static pressure difference as the starting point for basic prediction and retrieves the pressure drop change rate sequence of the past 5 consecutive sampling cycles. A first-order linear extrapolation algorithm is used to fit the prediction equation of the pressure drop evolution trajectory. The preset filter element ultimate pressure threshold is substituted into the prediction equation to solve it in reverse and obtain the allowable flow volume of the hydraulic medium before reaching the destructive pressure difference saturation point. The real-time flow rate of the system detected at the current node is called, and the allowable flow volume is directly divided by the real-time flow rate to convert it into the remaining service life index with hours as the absolute unit of measurement. Finally, a clear prediction time node for the maintenance cycle is generated.

[0044] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A method for on-line detection of the particle content and moisture content of a hydraulic medium, characterized in that Includes the following steps: Step S1: Obtain the complex impedance response data of the hydraulic medium flowing through the detection unit and in a dynamic circulation state, as well as the fluid pressure drop data between the inlet and outlet of the detection unit; wherein, the detection unit is connected in series in the return oil line of the hydraulic system; the local flow shear force generated by the return oil line increases the collision frequency between the dispersed water in the hydraulic medium and the hydrophilic groups on the surface of the sensing matrix. Step S2: Determine the moisture content based on the complex impedance response data; capture the dispersed moisture in the hydraulic medium using the hydrophilic groups inside the sensing matrix, and induce the dispersed moisture to be converted into bound moisture locked inside the sensing matrix network structure through hydrogen bonding. The sensing matrix is ​​supported by pulp fibers, and the surface of the pulp fibers is grafted with a spatial cross-linked network containing hydrophilic groups. The bound water is locked in the spatial cross-linked network by hydrogen bonds to maintain the dielectric environment at the interface between the hydraulic medium and the sensing matrix in a quasi-steady state and to suppress the macroscopic geometric deformation of the sensing matrix during the water absorption process. The central tube and the protective net surrounding the central tube in the detection unit are used as the inner and outer electrodes, respectively. The impedance reference level when the signal phase angle offset in the complex impedance response data reaches the preset stable range is extracted by the inner and outer electrodes, and the moisture content is calculated according to the mapping relationship between the impedance reference level and the moisture content. The process of extracting the impedance reference level includes: monitoring the phase angle offset of the complex impedance response data within the excitation frequency range of 10Hz to 1MHz; calculating the variance of the phase angle offset fluctuation; and determining the value when the variance of the fluctuation is less than 0.01 for 500ms consecutively. When the physical phase transition from dispersed water to bound water is completed, the average real part of the complex impedance response data at this time is determined as the impedance reference level. Step S3: Determine the cumulative concentration of particulate matter based on fluid pressure drop data; Utilize the physical interception effect of the sensing matrix on solid pollutants, obtain the flow rate of the hydraulic medium flowing through the sensing matrix and the dynamic viscosity at the current temperature, monitor the real-time gradient of the fluid pressure drop data over time, and calculate the cumulative concentration of particulate matter based on the real-time gradient, flow rate, and dynamic viscosity according to the preset interception pressure drop mapping rule. Specifically, a pre-decoupling calibration is performed on the solid particulate matter concentration inversion process, and the processor introduces a dynamic compensation term for moisture concentration to correct the theoretical model for calculating particulate matter concentration. ;in, The content of solid particulate matter in the medium. The rate of change of pressure drop across the particle filter module. The inductive coefficient is used to characterize the interception properties of materials. The intercept term reflects the background noise of the flow field; the solution model is set as follows. , where variables The coefficient representing the swelling pressure drop coupling caused by an increase in unit water concentration is obtained by guiding clean standard oil samples at different water content levels through the sensing matrix and recording the corresponding static pressure drop gradient parameters; variables Represents the real-time moisture content of the hydraulic medium; variable Refers to the mean value parameter of the real part of the complex impedance extracted when the physical transformation of the phase state is completed; coefficient and These are respectively the proportional gain and the base bias of the impedance real part parameter mapping function to the moisture concentration; and the processor establishes a timestamp queue in memory to execute a state alignment mechanism. During the transition period when the impedance reference level has not reached the steady-state determination condition, the moisture concentration M in the theoretical model correction term directly calls and maintains the historical calculation value successfully locked in the previous continuous steady-state window as the feedforward compensation reference; until the impedance phase fluctuation of the current period converges and the latest steady-state impedance reference level is extracted, the compensation formula is then adjusted. The value is updated iteratively in a stepwise manner.

2. The method for online detection of particulate matter and moisture content in hydraulic media according to claim 1, characterized in that, Before step S1, the process also includes: filling the inner cavity of the hydraulic oil tank with inert gas to maintain the pressure at the top of the hydraulic oil tank in a slightly positive pressure range of 0.02MPa to 0.05MPa.

3. The method for online detection of particulate matter and moisture content in hydraulic media according to claim 1, characterized in that, The process of determining the moisture content in step S2 also includes: synchronously acquiring the real-time temperature of the hydraulic medium and performing temperature compensation on the impedance reference level based on the real-time temperature; using a preset impedance temperature compensation curve, uniformly converting the impedance reference level at different working temperatures to the calibrated impedance value corresponding to 40°C, so as to eliminate the systematic error of the hydraulic medium viscosity-temperature characteristics on the complex impedance response data.

4. The method for online detection of particulate matter and moisture content in hydraulic media according to claim 1, characterized in that, The process of acquiring fluid pressure drop data in step S3 includes: synchronously acquiring the absolute pressure signals at the inlet and outlet of the detection unit; calculating the difference between the absolute pressure signals to generate the original sequence of fluid pressure drop data; and using a median filtering algorithm to filter out high-frequency pressure fluctuations caused by hydraulic pump source pulsation.

5. The method for online detection of particulate matter and moisture content in hydraulic media according to claim 1, characterized in that, After steps S2 and S3, the method further includes: comparing the calculated moisture content and cumulative particulate matter concentration with preset system health thresholds; when the moisture content exceeds 500 ppm or the slope of change of cumulative particulate matter concentration exceeds the preset threshold, outputting a warning signal to the external control terminal indicating that the hydraulic medium is in a state of accelerated contamination.

6. The method for online detection of particulate matter and moisture content in hydraulic media according to claim 5, characterized in that, After outputting an early warning signal indicating that the hydraulic medium is in a state of accelerated contamination, the system also includes: calculating the pressure drop saturation point of the sensing matrix based on the real-time gradient of the cumulative particulate matter concentration, and calculating the remaining service life of the sensing matrix from the pressure drop saturation point, so as to generate maintenance cycle prediction data for the hydraulic system and synchronize it to the maintenance management module.

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