Guide rail rust-proof state monitoring system and method based on impedance detection

Abstract

The invention discloses a guide rail rust-proof state monitoring system and method based on impedance detection, and relates to the technical field of electrochemical guide rail rust-proof monitoring. Impedance detection electrodes are arranged on the surface of a guide rail, alternating current excitation signals are applied, and voltage response signals and current response signals of the guide rail are synchronously collected; calculating to form guide rail impedance spectrum data and constructing an impedance spectrum curve, analyzing and separating impedance responses of different frequency bands, extracting impedance spectrum characteristic parameters representing oil film isolation capability, interface charge transfer capability and diffusion impedance characteristics, and establishing an equivalent circuit model to execute parameter inversion calculation; and solving interface mechanism parameters reflecting the integrity degree of the antirust film on the surface of the guide rail and the interface corrosion activity, and meanwhile, generating a guide rail antirust state spatial distribution diagram through spatial consistency analysis and gradient change analysis, thereby realizing the mechanism monitoring and spatial positioning identification of the guide rail antirust state.

Description

Technical Field

[0001] This invention relates to the field of electrochemical guide rail rust prevention monitoring technology, specifically to a guide rail rust prevention status monitoring system and method based on impedance detection. Background Technology

[0002] In rail transit systems and various industrial conveying devices, guide rails, as critical load-bearing and guiding structures, are exposed to environmental conditions such as changes in air humidity, corrosive media, and mechanical vibrations over long periods. To reduce the risk of corrosion to the metal substrate, an anti-rust oil film is typically coated on the guide rail surface in engineering applications to form an isolation and protective interface, thereby blocking the electrochemical reaction between the corrosive media and the metal substrate.

[0003] However, in actual operation, the anti-rust oil film on the guide rail surface gradually degrades due to environmental changes, oil evaporation, and mechanical friction, leading to a reduction in oil film thickness or localized damage. This increases the electrochemical activity on the guide rail surface and creates a risk of corrosion. Current technologies rely heavily on manual inspection or visual inspection to detect the rust-proof status of guide rails. These methods are insufficient to reflect the electrochemical isolation capability within the oil film layer or to quantitatively characterize the interfacial corrosion reaction process. Furthermore, while some detection methods can detect guide rail corrosion through electrochemical parameters, they typically only provide localized results, making it difficult to identify the changes in rust-proof status along the guide rail's length or to spatially locate and analyze areas of film attenuation and corrosion activation. Therefore, current technologies struggle to systematically analyze the rust-proof status of guide rails from an electrochemical mechanism perspective and to achieve continuous spatial monitoring of the rust-proof status.

[0004] Therefore, it is necessary to provide a monitoring method and system based on the electrochemical impedance response characteristics of the guide rail to analyze the state of the anti-rust film and the interface corrosion mechanism of the guide rail, and to identify the spatial distribution of the anti-rust state of the guide rail, so as to improve the accuracy and visualization level of the anti-corrosion state assessment of the guide rail. Summary of the Invention

[0005] This invention addresses the shortcomings of existing technologies by proposing a guide rail rust prevention status monitoring system and method based on impedance detection.

[0006] The technical solution to achieve the objective of this invention is as follows:

[0007] On the one hand, the guide rail rust prevention status monitoring method based on impedance detection includes the following steps:

[0008] An AC excitation signal with a preset frequency range is applied to the guide rail metal substrate, and the voltage response signal and current response signal are collected simultaneously and the complex impedance value of the corresponding frequency is calculated to form the guide rail impedance spectrum data corresponding to each detection position.

[0009] Based on the impedance spectrum data of the rail, the surface impedance spectrum curve of the rail is constructed. Frequency band analysis is performed to separate the impedance response components of the anti-rust oil film layer, the electrochemical double layer and the metal substrate interface. The impedance spectrum feature parameters are extracted and the rail impedance feature set is formed according to the feature category and frequency band attribute.

[0010] An equivalent circuit model was established based on the electrochemical reaction mechanism of the guide rail surface. The guide rail impedance spectrum data was used as the fitting object and the guide rail impedance characteristic set was used as the constraint condition to perform parameter inversion calculation. The interface mechanism parameters characterizing the integrity of the anti-rust film and the level of interface corrosion activity were obtained by solving the interface mechanism parameters.

[0011] Consistency analysis and gradient change analysis were performed on the spatial distribution of interface mechanism parameters at each detection location along the length of the guide rail to generate a spatial distribution map of the guide rail's rust prevention status, which characterizes the stable region, film attenuation region, and corrosion activation region of the rust prevention film on the guide rail surface.

[0012] Furthermore, parameter inversion calculations are performed, including:

[0013] An equivalent circuit model was established based on the electrochemical reaction mechanism of the guide rail surface. The equivalent circuit model adopts a series structure, with the equivalent circuit unit of the anti-rust oil film layer, the equivalent circuit unit of the electrochemical double layer, and the equivalent circuit unit of the metal substrate interface connected in series in sequence. Each unit adopts a resistance-capacitor series model, a resistance-capacitor parallel model, and a resistance-diffusion impedance series model, respectively.

[0014] Based on the rail impedance feature set, three types of constraints are determined: parameter range constraint, feature matching constraint, and mechanism consistency constraint. The rail impedance spectrum data is used as the fitting object. A combined algorithm of least squares method and genetic algorithm is adopted. The objective function is to minimize the fitting error between the impedance response of the equivalent circuit model and the rail impedance spectrum data. The parameters of each component are iteratively optimized under the three types of constraints.

[0015] Based on the iterative optimization results, the fitting accuracy verification, constraint satisfaction verification, and repeatability verification are performed sequentially. After each verification is qualified, the inversion results of the equivalent circuit model element parameters corresponding to each detection position are organized to form the equivalent circuit model parameter set.

[0016] Furthermore, frequency band analysis and separation of impedance response components include:

[0017] The preset frequency range is divided into high-frequency, mid-frequency, and low-frequency bands. The high-frequency band corresponds to the impedance response of the anti-rust oil film layer, the mid-frequency band corresponds to the impedance response of the electrochemical double layer, and the low-frequency band corresponds to the impedance response of the metal matrix interface and diffusion process. The impedance spectrum curves in each frequency band are subjected to amplitude variation trend analysis and phase variation characteristic analysis to form a frequency band analysis dataset.

[0018] Based on the frequency band analysis dataset, the equivalent circuit decomposition method is used to separate the impedance response components of each interface in sequence: the impedance response component of the rust-preventive oil film layer is obtained by fitting the high-frequency impedance spectrum data with a resistance-capacitance series model; the impedance response component of the electrochemical double layer is obtained by fitting the mid-frequency net impedance data after deducting the impedance response component of the rust-preventive oil film layer with a resistance-capacitance parallel model; and the impedance response components of the metal matrix interface and diffusion are obtained by fitting the low-frequency net impedance data after deducting the above two impedance response components with a resistance-diffusion impedance series model.

[0019] The three impedance response components are superimposed and compared with the rail impedance spectrum data. If the relative error meets the preset threshold, the separation is deemed effective.

[0020] Furthermore, the interface mechanism parameters are solved, including:

[0021] The integrity and thickness deviation of the rust-preventive oil film are obtained from the parameters of the equivalent circuit unit of the rust-preventive oil film layer. The integrity of the rust-preventive oil film is based on the maximum resistance value corresponding to the preset intact rust-preventive oil film layer to reflect the oil film isolation and protection capability. The thickness deviation of the rust-preventive oil film is based on the preset standard rust-preventive oil film thickness to reflect the actual deviation of the oil film thickness.

[0022] The interfacial corrosion activity index is obtained from the parameters of the equivalent circuit unit of the electrochemical double layer, and the diffusion corrosion risk coefficient is obtained from the parameters of the equivalent circuit unit of the metal substrate interface. The interfacial corrosion activity index reflects the activity of the corrosion reaction at the rail interface, and the diffusion corrosion risk coefficient reflects the risk of the corrosive medium diffusing to the metal substrate interface.

[0023] The integrity of the rust-preventive oil film, the thickness deviation of the rust-preventive oil film, the interfacial corrosion activity index, and the diffusion corrosion risk coefficient are grouped according to the detection location and associated with coordinate parameters to form a set of interfacial mechanism parameters.

[0024] Further consistency analysis includes:

[0025] The interface mechanism parameters at each detection location are mapped to the actual spatial location according to the coordinate parameters, and a mapping relationship between the detection location number and the interface mechanism parameters is established to form an interface mechanism spatial sequence arranged in an orderly manner along the length of the guide rail.

[0026] Based on the spatial sequence of interface mechanisms, the parameters of each interface mechanism are standardized. The spatial continuity is achieved by using a sliding weighted smoothing method with the current detection position as the center and the closer the position, the higher the weight. This results in a continuous distribution sequence of each interface mechanism parameter.

[0027] Based on the continuous distribution sequence of each interface mechanism parameter, and taking into account the parameter deviation between adjacent detection positions and the parameter dispersion within a local window, when any interface mechanism parameter shows continuous deviation or multiple interface mechanism parameters show synchronous increase in dispersion, it is determined that there is an inconsistent rust prevention state in that local area, thus forming a consistent distribution result of the interface mechanism.

[0028] Further gradient change analysis includes:

[0029] Taking the continuous distribution sequence of each interface mechanism parameter as the object, the first-order difference method is used to calculate the spatial change rate of each detection position relative to the adjacent detection positions, and the change trend is accumulated within a preset spatial window to obtain the gradient value of each interface mechanism parameter.

[0030] Collaborative analysis is performed based on the gradient values ​​of various interface mechanism parameters: when the gradients of interface mechanism parameters characterizing the integrity of the anti-rust film are all negative and their absolute values ​​all reach the preset oil film attenuation threshold, the region is determined to have an oil film attenuation trend; when the gradients of interface mechanism parameters characterizing the level of interface corrosion activity are all positive and their absolute values ​​all reach the preset corrosion activation threshold, the region is determined to have a corrosion activation trend; when the absolute values ​​of the gradients of each interface mechanism parameter are all below the preset stability threshold, the region is determined to have a stable trend.

[0031] Based on the gradient trend determination results of each region, spatial gradient analysis results reflecting the direction, rate of change and duration of rust prevention status changes are generated.

[0032] Furthermore, a spatial distribution map of the rust prevention status of the guide rail is generated, including:

[0033] Based on the results of interface mechanism consistency distribution and spatial gradient analysis, the execution area along the length of the guide rail surface is identified according to the value range and gradient trend of each interface mechanism parameter: continuous sections where all interface mechanism parameters are in the low-risk range and have good consistency are identified as rust-preventive film stable areas; continuous sections where some interface mechanism parameters enter the medium-risk range and the gradient trend shows an oil film decay trend are identified as film decay areas; and continuous sections where all interface mechanism parameters enter the high-risk range and the gradient trend shows a corrosion activation trend are identified as corrosion activation areas.

[0034] Based on the region identification results, the region boundaries are corrected by the gradient change inflection points of each region, and the starting position, ending position and length range of each region are recorded. The spatial range of the rust-preventive film stable region, film attenuation region and corrosion activation region is mapped to the guide rail coordinate position to generate a spatial distribution map of the guide rail rust prevention status.

[0035] Furthermore, the guide rail impedance spectrum data corresponding to each detection position is generated, including:

[0036] Inert electrodes are selected as impedance detection electrodes and arranged at equal intervals along the length of the guide rail. Each impedance detection electrode is numbered sequentially along the length of the guide rail and its coordinate parameters are recorded.

[0037] Connect the working electrode interface of the impedance measuring device to the guide rail metal substrate, connect the reference electrode interface to the reference electrode, and connect the auxiliary electrode interface to each impedance detection electrode respectively. Use shielded wires for the connecting wires and apply AC excitation signals at each frequency point in ascending order of frequency.

[0038] Based on the application of AC excitation signals at each frequency point, voltage response signals and current response signals are synchronously acquired. The acquired signals are filtered and the average amplitude and average phase of each frequency point are calculated. The complex impedance values ​​corresponding to each frequency point are sequentially arranged and combined in ascending order of frequency to form the rail impedance spectrum data.

[0039] Furthermore, impedance spectrum characteristic parameters are extracted from each impedance response component and aggregated to form a rail impedance characteristic set, including:

[0040] Based on the impedance response components of the rust-preventive oil film, the oil film resistance, oil film capacitance, and oil film insulation coefficient are extracted, classified into the oil film isolation characteristic category, and associated with the high-frequency band; based on the electrochemical double-layer impedance response components, the interface charge transfer resistance, double-layer capacitance, and charge transfer rate constant are extracted, classified into the charge transfer characteristic category, and associated with the mid-frequency band; based on the interface and diffusion impedance response components of the metal substrate, the diffusion impedance coefficient, the bulk resistance of the metal substrate, and the diffusion rate are extracted, classified into the diffusion impedance characteristic category, and associated with the low-frequency band.

[0041] Outliers were removed and standardized according to the three sigma criterion for the extracted feature parameters. The coordinate parameters of each detection position were associated and the oil film isolation feature, charge transfer feature and diffusion impedance feature parameters were integrated to form the rail impedance feature set.

[0042] Secondly, the guide rail rust prevention condition monitoring system based on impedance detection includes a data acquisition module, a feature analysis module, a mechanism inversion module, and a condition identification module:

[0043] The data acquisition module applies an AC excitation signal within a preset frequency range to the guide rail metal substrate, simultaneously acquires voltage response signals and current response signals, and calculates the complex impedance value at the corresponding frequency to form guide rail impedance spectrum data corresponding to each detection position.

[0044] The feature analysis module constructs the impedance spectrum curve of the rail surface based on the rail impedance spectrum data, performs frequency band analysis, separates the impedance response components of the anti-rust oil film layer, electrochemical double layer and metal substrate interface, extracts impedance spectrum feature parameters, and collects them into a rail impedance feature set according to feature category and frequency band attribute.

[0045] The mechanism inversion module establishes an equivalent circuit model based on the electrochemical reaction mechanism of the guide rail surface, uses the guide rail impedance spectrum data as the fitting object and the guide rail impedance characteristic set as the constraint condition to perform parameter inversion calculation, and solves the interface mechanism parameters characterizing the integrity of the anti-rust film and the level of interface corrosion activity.

[0046] The state identification module performs consistency analysis and gradient change analysis on the spatial distribution of interface mechanism parameters at each detection location along the length of the guide rail, generating a spatial distribution map of the guide rail's rust prevention state to characterize the stable region, attenuation region, and corrosion activation region of the rust prevention film on the guide rail surface.

[0047] Compared with the prior art, the advantages of this invention are as follows:

[0048] 1. By performing frequency band analysis on the impedance spectrum curve of the guide rail and separating the impedance response components of the oil film layer, electrochemical double layer and metal substrate interface, the characteristic parameters of the impedance spectrum are extracted and an equivalent circuit model is established. The interface mechanism parameters are solved by parameter inversion calculation, so that the rust prevention status of the guide rail can be quantitatively characterized from the electrochemical mechanism level, thereby achieving accurate identification of the integrity of the rust prevention film and the interface corrosion activity.

[0049] 2. By performing consistency analysis and gradient change analysis on the interface mechanism parameters along the length of the guide rail, the stable region of the anti-rust film, the film attenuation region and the corrosion activation region are identified, and a spatial distribution map of the anti-rust status of the guide rail is generated. This enables continuous spatial positioning and regional identification of the anti-rust status of the guide rail, providing a visual basis and decision support for the anti-corrosion maintenance of the guide rail. Attached Figure Description

[0050] Figure 1 The flowchart shows a method for monitoring the rust prevention status of guide rails based on impedance detection.

[0051] Figure 2 This is a schematic diagram illustrating the frequency band analysis of the guide rail impedance spectrum in this invention;

[0052] Figure 3 This is a flowchart illustrating the impedance response component separation process in this invention.

[0053] Figure 4 This is a schematic diagram of the spatial distribution and area identification of the rust prevention status of the guide rail in this invention. Detailed Implementation

[0054] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0055] Example 1

[0056] This invention discloses a method for monitoring the rust prevention status of guide rails based on impedance detection, comprising the following steps:

[0057] S1: Impedance detection electrodes are set at intervals along the length of the guide rail surface. An AC excitation signal covering a preset frequency range is applied to the guide rail metal substrate through an impedance measurement device. The guide rail voltage response signal and current response signal are collected synchronously at each excitation frequency. The complex impedance value of the corresponding frequency is calculated based on the amplitude and phase relationship between voltage and current. The guide rail impedance spectrum data corresponding to each detection position is formed by combining them in ascending order of frequency.

[0058] S2: Construct the impedance spectrum curve of the rail surface based on the rail impedance spectrum data, perform frequency band analysis on the amplitude change trend and phase change characteristics of the impedance spectrum curve in different frequency ranges, separate the impedance response components generated by the interface of the anti-rust oil film layer, the electrochemical double layer and the metal substrate, extract the impedance spectrum characteristic parameters that characterize the isolation ability of the film layer, the interface charge transfer ability and the diffusion impedance characteristics, and collect them according to the characteristic category and frequency band attribute to form the rail impedance characteristic set.

[0059] S3: Based on the electrochemical reaction mechanism of the guide rail surface, an equivalent circuit model is established. The guide rail impedance spectrum data is used as the fitting object, and the guide rail impedance characteristic set is used as the constraint condition to perform parameter inversion calculation. This ensures that the impedance response calculated by the equivalent circuit model is consistent with the guide rail impedance spectrum data, thereby solving for the interface mechanism parameters that characterize the integrity of the anti-rust film and the level of interface corrosion activity.

[0060] S4: Based on the interface mechanism parameters at each detection location, perform consistency analysis and gradient change analysis on the spatial distribution of the interface mechanism parameters along the guide rail length direction to generate a spatial distribution map of the guide rail rust prevention status, so as to characterize the stable area, film attenuation area and corrosion activation area of ​​the rust prevention film on the guide rail surface.

[0061] refer to Figure 1 , Figure 1 This is a flowchart of a guide rail rust prevention status monitoring method based on impedance detection.

[0062] In step S1, impedance detection electrodes are arranged on the surface of the guide rail and an AC excitation signal is applied. Voltage and current response signals are simultaneously acquired to calculate the complex impedance value, forming guide rail impedance spectrum data, including:

[0063] S101: Layout of impedance detection electrodes.

[0064] An inert electrode that does not interfere with the electrochemical reaction of the guide rail's metal substrate material is selected as the impedance detection electrode, with platinum or graphite electrodes being preferred. The effective detection area of ​​the electrode is controlled to be [missing information]. The electrode thickness is .

[0065] Along the length of the guide rail, according to The impedance detection electrodes are evenly spaced, with the arrangement path parallel to the length direction of the guide rail, and the distance between the center of the electrode and the edge of the guide rail is not less than [missing information]. To avoid edge effects affecting detection accuracy, the corresponding placement positions on the guide rail are wiped with anhydrous ethanol before deployment to remove surface dust, oil, and loose oxide layers. After the anhydrous ethanol has completely evaporated, the impedance detection electrodes are fixed to the guide rail surface with an insulating adhesive to ensure tight contact and no gaps between the electrodes and the guide rail metal substrate, and that the electrode surface is free of damage and contaminants.

[0066] Each impedance sensing electrode corresponds to a unique sensing position number, which are sequentially numbered from one end to the other along the length of the guide rail. ,in For numbering symbols, The total number of impedance detection electrodes is a positive integer. The coordinate parameters of each detection position are recorded. The origin of the coordinate system is selected as the endpoint of one end of the guide rail. The horizontal axis is the distance of the detection position along the length of the guide rail, and the vertical axis is the center position of the guide rail in the width direction.

[0067] S102: Application of AC excitation signal.

[0068] A high-precision electrochemical workstation was selected as the impedance measurement device, and the voltage measurement accuracy of this device is no less than [a certain value]. The accuracy of current measurement is not lower than Frequency output accuracy is not lower than The working electrode interface of the impedance measuring device is connected to the metal substrate of the guide rail via wires, the reference electrode interface is connected to the preset saturated calomel reference electrode, and the auxiliary electrode interface is connected to each impedance detection electrode respectively. Shielded wires are used for connection to avoid external electromagnetic interference.

[0069] Set the preset frequency range as This range covers the low-frequency band ( ), mid-frequency band ( ) and high frequency band ( This allows for the comprehensive capture of impedance response signals from the anti-rust oil film, electrochemical double layer, and metal substrate interface. The AC excitation signal is set to constant voltage excitation mode, and the excitation voltage amplitude is set to... This amplitude prevents the guide rail metal substrate from polarizing due to an excessively large excitation signal, while ensuring a sufficient signal-to-noise ratio in the response signal. Start the impedance measurement device and apply AC excitation signals at each frequency point in ascending order. The duration of the excitation signal at each frequency point is set to [value missing]. To ensure that the electrochemical reaction on the guide rail surface reaches a stable state, response signal acquisition is performed.

[0070] S103: Synchronous acquisition of voltage and current response signals.

[0071] At each excitation frequency The voltage response signal between the guide rail metal substrate and the impedance detection electrode, and the current response signal between the guide rail metal substrate and the auxiliary electrode are simultaneously acquired through the built-in data acquisition module of the impedance measurement device.

[0072] During the acquisition process, the sampling frequency is set to the excitation frequency. The sampling frequency is more than 100 times higher than the excitation frequency, ensuring that the number of sampling points in each excitation cycle is no less than 100 to accurately capture the amplitude and phase characteristics of the signal. The acquired voltage and current response signals are filtered in real time using a second-order low-pass filter, with the cutoff frequency set to the excitation frequency. Ten times stronger, filtering out external electromagnetic interference and contact noise. Synchronous acquisition at each frequency point. Group response signals, the acquisition time for each group of signals is... The average amplitude and average phase of each signal group are calculated to reduce the impact of random errors on the detection results.

[0073] Record each detection location and each excitation frequency. Average voltage amplitude Voltage phase value Average current amplitude Current phase value To ensure the integrity and accuracy of data records, each data point retains 6 significant digits.

[0074] S104: Calculation of complex impedance value.

[0075] Based on each excitation frequency The amplitude and phase relationship of the acquired voltage and current response signals are used to calculate the complex impedance value at the corresponding frequency. The calculation formula is as follows:

[0076] ,

[0077] in, This is the complex impedance value. It is the complex form of the voltage response signal, containing voltage amplitude and phase information. This is the complex form of the current response signal, containing current amplitude and phase information; the specific derivation is as follows: based on the average voltage amplitude... Voltage phase value The complex expression for voltage is:

[0078] ,

[0079] in, is a natural constant, with a value of approximately 2.718. The imaginary unit satisfies Based on the average current amplitude Current phase value The complex expression for current is:

[0080] ,

[0081] Will and Substituting into the formula for calculating complex impedance, we obtain the final expression for complex impedance:

[0082] ,

[0083] in, Let the complex impedance phase angle be denoted as . .

[0084] During the calculation, a high-precision complex number arithmetic algorithm is used to ensure that the calculation error does not exceed [the specified value]. Meanwhile, the calculated complex impedance amplitude... and complex impedance phase angle A rationality check was performed, including the complex impedance amplitude. The average voltage amplitude at each frequency point collected in S103 is divided by the average current amplitude; if the complex impedance amplitude at a certain frequency point... The value is outside the normal range. The normal range is preset based on the material of the guide rail's metal substrate and the characteristics of the anti-rust oil film. For example, the normal range for the complex impedance amplitude of a steel guide rail without an anti-rust film is as follows: If the data point is not found, the response signal at that frequency point is re-acquired and recalculated to ensure the validity of the complex impedance value.

[0085] S105: The combination of guide rail impedance spectrum data.

[0086] For each detection location, according to the excitation frequency By sequentially organizing and combining the complex impedance values ​​corresponding to each frequency point in an increasing order, the rail impedance spectrum data corresponding to that detection position is formed.

[0087] The rail impedance spectrum data is stored in the form of two-dimensional data sets, each containing three core parameters: excitation frequency. Complex impedance amplitude Complex impedance phase angle excitation frequency The impedance spectrum data is distributed using logarithmic intervals to ensure sufficient frequency points in both low and high frequency bands, thereby improving the resolution of the impedance spectrum curves. The impedance spectrum data for each detection location is numbered, matching the location number, and the coordinate parameters, electrode placement information, and detection time of that location are also recorded, forming a complete impedance spectrum dataset. This dataset provides fundamental data support for subsequent steps such as impedance spectrum curve construction and feature parameter extraction.

[0088] In step S2, by constructing impedance spectrum curves and performing frequency band analysis, the impedance response components of different interfaces are separated, and then key feature parameters are extracted. Finally, these parameters are aggregated to form a rail impedance feature set, including:

[0089] S201: Construction of the surface impedance spectrum curve of the guide rail.

[0090] Using the complete rail impedance spectrum dataset obtained in step S1, construct the complex impedance amplitude-frequency curve and the complex impedance phase angle-frequency curve for each detection location, which together form the rail surface impedance spectrum curve for that detection location.

[0091] During curve construction, a high-precision data fitting algorithm was used to smooth the guide rail impedance spectrum data. A Gaussian filtering algorithm was selected, with the filtering window size set to 5 frequency points to filter out random noise remaining during data acquisition, ensuring the continuity and smoothness of the curve. The fitting error was controlled within a certain range. Within. The curve is plotted using a dual vertical axis coordinate system, with the horizontal axis representing the excitation frequency. The logarithmic scale is used to clearly present the curve variation characteristics in different frequency ranges; the left vertical axis represents the complex impedance amplitude. It uses logarithmic coordinates and has an appropriate range of values. The impedance amplitude variation range is covered under the rust-proof condition of the guide rail; the right vertical axis represents the complex impedance phase angle. Linear coordinates are used, and the range of values ​​is [value range missing]. ,correspond .

[0092] After the construction is completed, the validity of each impedance spectrum curve is verified. If the curve has obvious discontinuities, abrupt changes, or fitting errors exceeding the preset range, it is determined to be an invalid curve. The original impedance spectrum data of the rail at the detection location is retrieved again. The preset range is the threshold range set during the construction of the rail surface impedance spectrum curve, which is no more than 1% of the fitting error. The above smoothing and plotting steps are repeated until the curve meets the validity requirements. If there are abnormalities in the original data, the process returns to step S1 to re-acquire the impedance spectrum data of the detection location to ensure that the rail surface impedance spectrum curve can truly reflect the impedance response characteristics of the rail surface.

[0093] S202: Frequency band analysis of impedance spectrum curve.

[0094] Based on the preset frequency range set in step S1, under AC excitation conditions, the electrochemical processes at different interfaces have different response time constants, and their dominant frequency ranges in the impedance spectrum show significant differences. Among them, the anti-rust oil film layer is a dielectric isolation structure, and its polarization process has the shortest response time, corresponding to the high-frequency fast response range, exhibiting impedance characteristics dominated by capacitance behavior in the high-frequency range; the electrochemical double layer is an interface charge accumulation structure, and its charging and discharging process has a medium time scale, corresponding to the mid-frequency response range, exhibiting charge transfer and capacitive coupling behavior; the metal matrix interface and diffusion process are controlled by mass migration, with the longest response time, corresponding to the low-frequency range, exhibiting diffusion-controlled impedance characteristics.

[0095] Based on the aforementioned mechanistic differences, the impedance spectrum curves are divided into three characteristic frequency bands, each corresponding to the impedance response of a different interface. The criteria for dividing each frequency band are as follows: the high-frequency band is... This mainly corresponds to the impedance response of the anti-rust oil film layer; the mid-frequency band is... This mainly corresponds to the impedance response of the electrochemical double layer; the low-frequency range is... This mainly corresponds to the impedance response of the metal matrix interface and diffusion process.

[0096] For the impedance spectrum curves within each characteristic frequency band, amplitude variation trend analysis and phase variation characteristic analysis were performed respectively. Among them, the amplitude variation trend analysis adopted a combination of linear fitting and slope calculation to calculate the complex impedance amplitude within each frequency band. With excitation frequency The slope of change The rate of change of amplitude can be judged by the magnitude of the absolute value of the slope. The larger the absolute value of the slope, the more sensitive the impedance amplitude is to frequency changes in that frequency band.

[0097] Phase change characteristic analysis combines phase angle abrupt change point identification with phase interval statistics to identify the complex impedance phase angle in each frequency band. The abrupt change point is specifically determined as follows: First, extract the complex impedance phase angle data corresponding to all frequency points within each characteristic frequency band, and arrange them into a phase angle sequence in ascending order of excitation frequency; calculate the phase angle difference between adjacent frequency points point by point, and compare this difference with the preset phase abrupt change threshold. Comparison, if the difference is ≥ If the next frequency point in the current frequency band is determined to be the phase angle abrupt change point, then after completing the calculation of all adjacent points and the preliminary identification of abrupt change points within a single frequency band, the range of phase angle values ​​and the average phase angle within each frequency band are statistically analyzed. To clarify the laws and characteristics of phase changes.

[0098] During frequency band analysis, the slope of amplitude change at each detection location and in each characteristic frequency band is recorded. Phase angle range, average phase angle The number of phase abrupt change points is used to form a frequency band analysis dataset, which provides data support for the subsequent separation of impedance response components. At the same time, the consistency of the analysis results is checked. If the analysis results of a certain frequency band do not match the interface impedance response mechanism corresponding to that frequency band, such as the slope of the amplitude change in the high-frequency band being abnormally negative, the frequency band division boundary is readjusted and the analysis steps are repeated to ensure the accuracy of the analysis results.

[0099] refer to Figure 2 , Figure 2 This is a schematic diagram illustrating the frequency band analysis of the guide rail impedance spectrum.

[0100] Figure 2 The diagram illustrates the response characteristics of the guide rail impedance spectrum in different excitation frequency ranges and the meaning of the interface mechanism corresponding to each frequency band. The impedance spectrum is divided into low-frequency, mid-frequency, and high-frequency bands along the frequency axis. The low-frequency band corresponds to the metal matrix interface process and diffusion impedance component, the mid-frequency band corresponds to the electrochemical double layer process and charge transfer component, and the high-frequency band corresponds to the rust-preventive oil film process and film isolation component. At the same time, each frequency band corresponds to different equivalent branch forms, which are used to support the subsequent fitting and solution of interface mechanism parameters and the identification of rust prevention status.

[0101] It should be noted that, Figure 2 This is for illustrative purposes only, intended to help understand the process structure and data organization, and does not represent the precise working state in actual operation.

[0102] S203: Separation of impedance response components.

[0103] Based on the frequency band analysis dataset obtained in step S202, the equivalent circuit decomposition method is used. Combined with the difference in response time scale of the electrochemical reaction on the guide rail surface, the impedance spectrum data is segmented and separated step by step to obtain the impedance response components of the anti-rust oil film layer, the impedance response components of the electrochemical double layer, and the impedance response components of the metal substrate interface and diffusion.

[0104] Since the polarization establishment process of the rust-preventive oil film is the fastest, followed by the charging and discharging process of the electrochemical double layer, and the diffusion control process is the slowest, they correspond to the dominant response ranges in the impedance spectrum at high, mid, and low frequencies, respectively. Based on this mechanism, establishing equivalent circuit models matching the dominant processes for different frequency bands can reduce parameter coupling caused by simultaneous fitting of multiple interface processes, and improve the stability and physical interpretability of the impedance component separation results.

[0105] Impedance response components of the rust-preventive oil film were separated from the high-frequency impedance spectrum data. Under high-frequency excitation, the AC signal preferentially acts on the surface dielectric isolation structure of the rust-preventive oil film. The current mainly exhibits capacitive coupling behavior through the oil film, and the deep diffusion process in the metal matrix has not yet been fully established. Therefore, the high-frequency impedance response is mainly dominated by the rust-preventive oil film. Based on this, high-frequency impedance spectrum data was selected, and a resistance-capacitance series model was used to fit the data to separate the impedance response components of the rust-preventive oil film. The expression is:

[0106] ,

[0107] in, To determine the resistance of the rust-preventive oil film layer, For capacitors with rust-preventive oil film layer, Angular frequency, The unit is the imaginary unit. During fitting, the measured complex impedance value in the high-frequency band is used as the target data, first determined based on the impedance amplitude level in the high-frequency band. The initial value is then determined based on the capacitive characteristics corresponding to the average phase angle in the high-frequency band. The initial values ​​are then used, followed by the sum of squared complex impedance deviations between the model impedance response and the measured impedance response as the objective function. The slope of the high-frequency amplitude change and the average phase angle obtained in step S202 are used as constraints, and the least squares method is employed for iterative updating. and Continue until the fitting error meets the preset threshold. After fitting is complete, by... and The determined model impedance is used as the impedance response component of the anti-rust oil film layer.

[0108] Electrochemical double-layer impedance response component separation was performed on the mid-frequency impedance spectrum data. Within the mid-frequency range, the rapid polarization response of the rust-preventive oil film is essentially stable, while the charging and discharging behavior of the double layer at the interface gradually becomes the main contributor. Therefore, this frequency band is more suitable for characterizing the electrochemical double-layer process. To avoid superimposed interference from the oil film response on the mid-frequency fitting results, the separated rust-preventive oil film impedance response component was first subtracted from the original mid-frequency impedance spectrum data point by point to form the net mid-frequency impedance data. Then, a resistance-capacitance parallel model was used to fit this net impedance data, and its electrochemical double-layer impedance response component was determined. The expression is:

[0109] ,

[0110] in, The resistance for interfacial charge transfer. This refers to the electrochemical double-layer capacitance. During fitting, the amplitude level of the net impedance data in the mid-frequency band is first determined. The initial value is then determined based on the phase angle distribution range and the capacitive characteristics of the mid-frequency band. The initial values ​​were then used, followed by the sum of squared complex impedance deviations between the model impedance and net impedance data as the objective function. Constraints were imposed by the slope of amplitude variation in the mid-frequency band, the average phase angle, and the phase variation range. The least squares method was then used for parameter optimization. After iterative convergence, the obtained... and This characterizes the resistance to interfacial charge transfer and the energy storage capacity of the double layer; the corresponding model response is the electrochemical double layer impedance response component.

[0111] The low-frequency impedance spectroscopy data were subjected to separation of the metal-matrix interface and diffusion impedance response components. Under low-frequency excitation, the electrochemical system exhibits a relatively long response time, and the migration and diffusion of the corrosive medium near the interface, as well as the hysteresis response of the metal-matrix interface, gradually become fully apparent. Therefore, the low-frequency impedance characteristics are mainly controlled by the metal-matrix interface process and the diffusion process. To ensure that the low-frequency fitting object reflects the intrinsic characteristics of this slow process as much as possible, the obtained rust-preventive oil film impedance response component and the electrochemical double-layer impedance response component were first subtracted from the original low-frequency impedance spectroscopy data at each frequency point to form the low-frequency net impedance data. Then, a resistance-diffusion impedance series model was used for fitting, and the metal-matrix interface and diffusion impedance response components were separated. Represented as:

[0112] ,

[0113] in, The bulk resistance of the metal substrate. This is the diffusion impedance component. Further, the diffusion impedance component is expressed as:

[0114] ,

[0115] in, This is the diffusion impedance coefficient, used to characterize the degree of restriction on diffusion in corrosive media. During fitting, it is determined based on the low-frequency amplitude level of the net impedance data in the low-frequency range. The initial value is determined based on the low-frequency phase lag characteristics and the trend of impedance continuously increasing as the frequency decreases. The initial values ​​are then determined, and a complex impedance deviation sum of squares objective function is constructed between the model impedance and the net impedance in the low-frequency band. Under the constraints of the slope of the amplitude change in the low-frequency band, the average phase angle, and the diffusion-dominant trend, the least squares method is used for iterative updates. and This continues until the fitting result converges. After fitting is complete, by... and The combined model response consists of the metal matrix interface and diffusion impedance response components.

[0116] After separation, the three impedance response components are superimposed for verification. , and The combined impedance response is obtained by superposition. The complex impedance value corresponding to the original impedance spectrum data By comparison, if the relative error between the two does not exceed If the error exceeds the acceptable range, the separation is deemed valid; if the error exceeds the acceptable range, the fitting model parameters for each frequency band are adjusted, and the separation steps are repeated until the verification requirements are met.

[0117] refer to Figure 3 , Figure 3 This is a flowchart for separating impedance response components.

[0118] S204: Extraction of impedance spectrum characteristic parameters.

[0119] Based on the three impedance response components obtained by separation, impedance spectrum characteristic parameters characterizing the film isolation capability, interfacial charge transfer capability, and diffusion impedance characteristics were extracted. All parameters were extracted in combination with frequency band attributes and response mechanisms to ensure their relevance and effectiveness. The specific extraction content is as follows:

[0120] Characteristic parameters that characterize the isolation ability of rust-preventive oil film: impedance response components of rust-preventive oil film. Extracted from, including the resistance of the rust-preventive oil film layer. 1. Anti-rust oil film layer capacitor and the impedance amplitude of the rust-preventive oil film .

[0121] in, For the center frequency of the high-frequency band amplitude, and The fitting results are directly taken from step S203; the insulation coefficient of the oil film layer is calculated simultaneously. ,in, The thickness of the rust-preventive oil film can be derived from the impedance fitting results. , To improve the resistivity of the rust-preventive oil film, The effective detection area of ​​the electrode. It directly characterizes the insulation and isolation capability of the oil film layer; the higher the value, the better the isolation performance.

[0122] Characteristic parameters representing the charge transfer capability of an interface: from the electrochemical double-layer impedance response components Extracted from, including interfacial charge transfer resistance Electrochemical double-layer capacitance and charge transfer rate constant .

[0123] in, and The fitting results are taken directly from step S203; The calculation formula is:

[0124] ,

[0125] in, The gas constant is 8.314 J / (mol·K). The ambient thermodynamic temperature is assumed to be 298K. The number of electrons transferred in the electrode reaction is the value for the corrosion reaction of steel rails. , It is Faraday's constant; The bigger, The smaller the value, the greater the resistance to interfacial charge transfer, and the more difficult the corrosion reaction is to occur.

[0126] Characteristic parameters representing diffusion impedance properties: from the metal matrix interface and diffusion impedance response components Extracted from, including diffusion impedance coefficient Metal matrix bulk resistance and diffusion impedance amplitude .

[0127] in, and The fitting results are taken directly from step S203; For the center frequency of the low frequency band The amplitude; and the diffusion rate are calculated simultaneously. ,in, The thickness of the diffusion layer, The diffusion coefficient of the corrosive medium can be derived from... The derivation formula is as follows:

[0128] ,

[0129] It characterizes the diffusion rate of corrosive media to the metal matrix interface. The smaller the value, the greater the diffusion resistance and the lower the corrosion risk.

[0130] During the extraction process, outlier removal is performed on each feature parameter, using 3... The principle is that if a feature parameter at a certain detection location is determined to be an outlier, the average value of the parameter at adjacent detection locations is used to replace it to ensure the validity and continuity of the feature parameter; at the same time, the extraction method, calculation process and corresponding frequency band of each feature parameter are recorded to provide a basis for subsequent aggregation to form a feature set.

[0131] S205: The collection and formation of the rail impedance characteristic set.

[0132] Based on the frequency band attributes of the feature parameters, all extracted impedance spectrum feature parameters are classified and grouped to form a rail impedance feature set. The specific grouping principles and steps are as follows:

[0133] All characteristic parameters are divided into three categories: oil film isolation characteristics, charge transfer characteristics, and diffusion resistance characteristics. The oil film isolation characteristics category includes... , , , The charge transfer feature class includes , , The diffusion impedance characteristic class includes , , , .

[0134] For each type of feature parameter, a corresponding frequency band information is associated. Specifically, the oil film isolation feature is associated with the high frequency band, the charge transfer feature with the mid frequency band, and the diffusion impedance feature with the low frequency band. At the same time, the detection position coordinates, extraction time, and fitting error corresponding to each feature parameter are also associated to ensure the traceability of the feature parameters.

[0135] The collected feature parameters are grouped according to detection location, with each detection location corresponding to a complete set of three types of feature parameters. These are stored in a two-dimensional data table, and the parameter values ​​in the table are standardized. The normalized parameter value range is as follows: This facilitates the subsequent inversion calculation of equivalent circuit model parameters.

[0136] The collected rail impedance feature set undergoes completeness and consistency verification. Completeness verification ensures that each detection location and each feature category contains all corresponding feature parameters without any omissions or gaps. Consistency verification ensures that the extraction methods and calculation standards for the same feature parameters are consistent across different detection locations, that parameter units are uniform, and that frequency band correlations are accurate. If the verification fails, the corresponding steps are returned to supplement and improve the set until the verification requirements are met, ultimately forming a complete and standardized rail impedance feature set.

[0137] In step S3, an equivalent circuit model is established, and parameter inversion is performed using the rail impedance spectrum data as the fitting target and the rail impedance characteristic set as the constraint. Finally, the interface mechanism parameters reflecting the state of the anti-rust film and corrosion activity are solved, including:

[0138] S301: Establishment of an equivalent circuit model based on electrochemical reaction mechanism.

[0139] Based on the electrochemical reaction mechanism of the guide rail surface and referring to the impedance response component characteristics of the anti-rust oil film layer, electrochemical double layer and metal substrate interface obtained by step S203, an equivalent circuit model that completely matches the actual electrochemical reaction process of the guide rail surface is established. The model adopts a series structure, with the equivalent circuit unit of the anti-rust oil film layer, the equivalent circuit unit of the electrochemical double layer, the metal substrate interface and the diffusion equivalent circuit unit connected in series in sequence, comprehensively covering the impedance response mechanism of each interface of the guide rail surface.

[0140] The structural and component parameters of each unit in the equivalent circuit model are defined as follows:

[0141] The equivalent circuit unit of the rust-preventive oil film layer adopts a resistor-capacitor series model, which is consistent with the fitting model of the impedance response component of the rust-preventive oil film layer in step S203.

[0142] The equivalent circuit unit of the electrochemical double layer is a resistor-capacitor parallel model, which is consistent with the fitting model of the electrochemical double layer impedance response component in step S203.

[0143] Metal substrate interface and diffusion equivalent circuit unit: The resistance-diffusion impedance series model is adopted, which is consistent with the fitting model of the metal substrate interface and diffusion impedance response components in step S203.

[0144] Total impedance expression of equivalent circuit model The sum of the series impedances of all units is given by: After the model is established, the initial value range of each component parameter is determined. The initial value range is determined by taking the corresponding impedance spectrum characteristic parameter in step S204 as the central reference value and combining the fluctuation range of the same parameter at adjacent detection positions to expand the interval, so that the initial parameter value not only fits the actual impedance response characteristics, but also satisfies the constraints of spatial continuity and electrochemical mechanism.

[0145] S302: Determination of constraints for parameter inversion calculation.

[0146] Using the rail impedance characteristic set obtained in step S2 as the core constraint, and combining it with the physical laws of electrochemical reactions on the rail surface, the constraint system for parameter inversion calculation is determined. This system includes three types of constraints: parameter range constraints, characteristic matching constraints, and mechanism consistency constraints. This ensures that the parameters of the equivalent circuit model obtained by inversion not only conform to the characteristic data but also fit the actual electrochemical reaction mechanism. The specific constraints are as follows:

[0147] Parameter range constraints: Based on the distribution range and statistical range of impedance spectrum characteristic parameters extracted in step S204, the lower limit is taken as the minimum effective value of the characteristic parameter corresponding to each detection position or the lower quantile value after outlier removal, and the upper limit is taken as the maximum effective value or the upper quantile value. The physical reasonable range of parameters is constrained by combining the conductivity characteristics of the guide rail material and the electrochemical reaction mechanism. The physical reasonable range is determined according to the electrochemical mechanism corresponding to each parameter. Resistance parameters should be greater than zero and match the conductivity characteristics of the guide rail metal substrate. Capacitance parameters should be in the typical range of double layer or dielectric polarization. Diffusion impedance parameters should meet the diffusion control characteristic of increasing impedance amplitude with decreasing frequency, thereby ensuring that the parameter values ​​are derived from actual detection data and meet the requirements of consistency between physical meaning and mechanism.

[0148] Feature matching constraint: The impedance characteristic parameters calculated from the equivalent circuit model must be consistent with the corresponding parameters in the rail impedance characteristic set extracted in step S204, with the error controlled within a certain range. Within. Specifically includes: model calculations , Matching with oil film isolation feature parameters in the feature set; model calculation , Matching with charge transfer characteristic parameters in the feature set; model calculation , The model parameters are matched with the diffusion impedance characteristic parameters in the feature set to ensure that the model parameters are consistent with the actual detection characteristics.

[0149] Mechanism consistency constraints: Based on the electrochemical reaction mechanism of the guide rail surface, constraints are set on the correlation between parameters. For example, Insulation coefficient with oil film layer satisfy ,and Follow It increases with the increase of; With charge transfer rate constant satisfy ,and and Negative correlation; With diffusion rate The parameters are positively correlated to ensure that the inversion parameters conform to the inherent laws of electrochemical reactions and to avoid parameter combinations that contradict the mechanism.

[0150] The above constraints are organized into a set of constraint equations, and the constraint weights are defined. Among them, the mechanism consistency constraint has the highest weight (0.4), followed by the feature matching constraint (0.35), and the parameter range constraint has the lowest weight (0.25). The constraint weights are set based on the degree of influence of each constraint on the physical reliability and data consistency of the parameter inversion results. The mechanism consistency constraint directly reflects the inherent law of electrochemical reaction and plays a leading role in avoiding physical distortion of parameter combinations, so it is given the highest weight. The feature matching constraint is used to ensure the consistency between the model response and the measured impedance characteristics, and has a key impact on the fitting accuracy, so it is given the second highest weight. The parameter range constraint is mainly used to limit the search space and prevent parameters from going out of bounds. Its direct impact on the results is relatively weak, so it is given a lower weight. This achieves a balance between the physical rationality and the data fitting accuracy of the inversion results, providing a clear constraint basis for subsequent parameter inversion calculations.

[0151] S303: Execution of parameter inversion calculation.

[0152] The guide rail impedance spectrum data obtained in step S1 includes data for each detection position and each excitation frequency. Corresponding complex impedance amplitude Complex impedance phase angle For the fitting object, using the constraints determined in step S302 as constraints, a combined algorithm of "least squares method + genetic algorithm" is used to perform parameter inversion calculation, balancing inversion accuracy and convergence speed. The specific execution steps are as follows:

[0153] The rail impedance characteristic parameters extracted in step S204 are used as the initial values ​​of the parameters of each component in the equivalent circuit model. At the same time, according to the parameter range constraints in step S302, the search interval of each parameter is set to ensure that the initial values ​​and search intervals meet the constraint requirements. The relevant parameters of the genetic algorithm are initialized, including the population size, which is set to 50, the number of iterations, which is set to 100, the crossover probability, which is set to 0.8, the mutation probability, which is set to 0.05, and the fitting error threshold of the least squares method is initialized to 3%.

[0154] Substitute the parameters of the current equivalent circuit model into the total impedance expression to calculate each excitation frequency. The impedance response of the model, including the magnitude and phase angle of the complex impedance, is calculated using a high-precision complex number arithmetic algorithm to ensure that the calculation error does not exceed 0.5%.

[0155] The least squares method is used to calculate the fitting error between the model impedance response and the rail impedance spectrum data. A genetic algorithm is then used to iteratively optimize the component parameters of the equivalent circuit model, with minimizing the fitting error as the objective function. The parameters are adjusted in accordance with the constraints of step S302. After each iteration, the model impedance response and fitting error are recalculated, and it is determined whether the error is less than a preset threshold of 3%. If the threshold is not reached, iterative optimization continues until the upper limit of the number of iterations is reached or the fitting error meets the requirements.

[0156] For each detection position, the above parameter inversion steps are performed to obtain the equivalent circuit model parameters corresponding to each detection position. At the same time, a consistency analysis is performed on the inversion parameters of all detection positions. If the difference of the same parameter between adjacent detection positions exceeds 10%, the parameter is collaboratively corrected to ensure that the parameter changes continuously and reasonably along the guide rail length direction and to avoid abnormal sudden changes.

[0157] During the inversion calculation process, the parameter values, fitting errors, and constraint conditions are recorded in real time for each iteration, forming a parameter inversion log, which provides a traceable basis for subsequent result verification and parameter adjustment.

[0158] S304: Verification and correction of inversion results.

[0159] After the parameter inversion calculation is completed, the inversion results are verified in multiple dimensions to ensure the accuracy and reliability of the equivalent circuit model. If the verification fails, the parameters are corrected. The specific verification and correction steps are as follows:

[0160] Calculate the average relative error between the model impedance response and the rail impedance spectrum data at each detection position. If the average relative error does not exceed 3% and the maximum relative error at a single frequency point does not exceed 5%, the fitting accuracy is considered acceptable. If the error exceeds the range, adjust the number of iterations and the parameter search range of the genetic algorithm, and re-execute the inversion calculation in step S303 until the fitting accuracy meets the requirements.

[0161] Check whether the parameters of the equivalent circuit model obtained by inversion fully meet the three types of constraints set in step S302. If there are parameters that are out of range, do not match the characteristic parameters, or contradict the electrochemical reaction mechanism, use the constraint weighted correction method to adjust the parameters. The adjustment range shall not exceed 10% of the original parameter value. After adjustment, recalculate the fitting error to ensure that the corrected parameters meet the constraints without significantly reducing the fitting accuracy.

[0162] The rationality of the inversion parameters is verified by considering the electrochemical reaction mechanism on the guide rail surface. For example, if the inversion results in... Too small, such as less than This indicates that the anti-rust oil film layer is damaged or insufficient in thickness, and needs to be verified in conjunction with the oil film isolation characteristic parameters in step S204; if If the value is too small, it indicates high interfacial corrosion activity, requiring further investigation. The values ​​of the parameters are cross-validated to ensure that the inversion parameters are consistent with the actual rust prevention status and corrosion mechanism.

[0163] For the same detection location, repeat the parameter inversion calculation three times. If the relative deviation of the same parameter value obtained from the three inversions does not exceed 2%, the inversion result is judged to have good repeatability. If the deviation exceeds the range, optimize the parameter settings of the inversion algorithm, such as increasing the population size and increasing the number of iterations, and re-execute the inversion calculation until the repeatability meets the requirements.

[0164] After all verifications are passed, the final equivalent circuit model parameters are determined, forming the equivalent circuit model parameter set corresponding to each detection position. If the verification requirements still cannot be met after multiple corrections, return to step S2 to reconstruct the impedance spectrum curve, analyze the frequency band, and extract the characteristic parameters to ensure the accuracy of the basic data.

[0165] S305: Solving and organizing the interface mechanism parameters.

[0166] Based on the validated equivalent circuit model parameter set and combined with the electrochemical reaction mechanism of the guide rail surface, the interface mechanism parameters characterizing the integrity of the anti-rust film and the level of interface corrosion activity are obtained. The specific solution and processing steps are as follows:

[0167] Determination of interfacial mechanism parameters characterizing the integrity of the anti-rust film:

[0168] Rust-preventive oil film integrity Based on the equivalent circuit model Insulation coefficient of the oil film layer in step S204 Solve using the following formula: ,in, The maximum resistance value corresponding to a pre-set, intact anti-rust oil film layer is set according to the standard parameters of the anti-rust oil film on the guide rail. For example, the maximum resistance value of an intact oil film on a steel guide rail is set as follows: . The range of values ​​is The higher the value, the more complete the anti-rust oil film layer and the better the isolation performance; when When the oil film layer is intact, it is determined that the oil film layer is intact; when At that time, it was determined that the oil film layer had minor damage; when At that time, it was determined that the oil film layer was severely damaged and had lost its isolation and protective function.

[0169] Rust-preventive oil film thickness deviation :based on The derived oil film thickness With preset standard oil film thickness The difference, where the preset standard oil film thickness is determined based on the actual rust prevention process specifications and material protection requirements of the guide rail, is calculated using the following formula: . When the oil film thickness meets the standard or is too thick; When the deviation is too large, it indicates that the oil film thickness is insufficient. The larger the absolute value of the deviation, the thinner the oil film thickness, and the higher the risk of damage.

[0170] Determination of interface mechanism parameters characterizing the level of interfacial corrosion activity:

[0171] Surface corrosion activity index Based on the equivalent circuit model and charge transfer rate constant Solve using the following formula: ,in, The charge transfer rate constant corresponding to the preset maximum corrosion activity is set according to the guide rail metal material, such as steel guide rails. Set as . The range of values ​​is The higher the value, the higher the interfacial corrosion activity and the greater the corrosion risk; when When the interface corrosion activity is low, it is determined that the interface corrosion activity is low; when At that time, the interface corrosion activity was determined to be moderate; when If the interface is deemed to have high corrosion activity, anti-corrosion measures should be taken promptly.

[0172] Diffusion corrosion risk factor Based on the equivalent circuit model and diffusion rate Solve using the following formula: ,in The diffusion impedance coefficient corresponding to the preset maximum diffusion risk is determined based on the upper bound distribution of diffusion impedance characteristic parameters in the low-frequency band and calibrated in combination with historical detection data of the corrosion activation region. The diffusion impedance coefficient corresponding to the significant enhancement of diffusion-dominant behavior is used as the benchmark value. The range of values ​​is The higher the value, the faster the corrosive medium diffuses to the metal matrix interface, and the higher the risk of diffusion corrosion; when When the risk of diffusion corrosion is low, it is determined that the risk is low. At that time, the risk of diffusion corrosion was determined to be moderate; when At that time, the risk of diffusion corrosion was determined to be high.

[0173] The threshold values ​​and preset characteristic parameters for various rust prevention status determinations mentioned above are all set based on the intrinsic material properties of the metal substrate and rust-preventive oil film of rail transit / industrial guide rails, the industry-standard testing rules in the field of electrochemical corrosion, and the engineering corrosion risk classification standards. At the same time, combined with the detection system parameters such as electrode layout, frequency excitation, and equivalent circuit model of this monitoring method, and through statistical analysis and working condition adaptation verification of a large amount of measured data on the rust prevention status of guide rails, it is ensured that the threshold division is highly matched with the actual rust-preventive film status and interface corrosion risk, and meets the early warning and treatment requirements of engineering corrosion prevention maintenance.

[0174] All interface mechanism parameters obtained from the solution , , , The parameters are grouped according to the detection location. Each detection location corresponds to a complete set of interface mechanism parameters. The coordinate parameters, equivalent circuit model parameters and inversion error of the detection location are recorded and stored in the form of a two-dimensional data table to form a complete set of interface mechanism parameters.

[0175] In step S4, consistency analysis and gradient change analysis are performed on the spatial distribution of interface mechanism parameters along the length of the guide rail to generate a spatial distribution map of the guide rail's rust prevention state, which characterizes the stable region, film attenuation region, and corrosion activation region of the rust-preventive film on the guide rail surface, including:

[0176] S401: Preprocessing of interface mechanism parameters.

[0177] By calling the interface mechanism parameter set generated in step S305, the integrity of the anti-rust oil film corresponding to each detection location is extracted. 1. Deviation in the thickness of the anti-rust oil film Surface corrosion activity index Diffusion corrosion risk coefficient At the same time, it calls up the coordinate parameters, detection position number, equivalent circuit model parameters and inversion error of each detection position.

[0178] Using the length of the guide rail as the main analysis axis, the interface mechanism parameters of each detection position are mapped to the actual coordinate position according to the detection position number, and a one-to-one mapping relationship between the detection position number, coordinate parameters and interface mechanism parameters is established, forming an orderly spatial sequence of interface mechanisms along the length of the guide rail.

[0179] During the data processing, integrity checks are performed on all interface mechanism parameters at the same detection location to ensure that each detection location contains [the necessary parameters]. , , and Four interface mechanism parameters; if there are missing values, the same interface mechanism parameters at adjacent detection locations are called to perform interpolation to fill in the missing values, and the source of the filling is marked to ensure the data continuity of the subsequent spatial analysis process.

[0180] Simultaneously, the inversion error at each detection location is screened for validity. When the inversion error at a certain detection location exceeds the allowable range determined in step S304, the detection location is marked as a low-confidence location, and its weight is reduced in subsequent consistency analysis to avoid interference from local abnormal data on the overall spatial distribution judgment. Finally, a sequence of guide rail interface mechanism parameters that can be used for spatial analysis is formed.

[0181] S402: Standardization and spatial continuity processing of interface mechanism parameters.

[0182] Based on the guide rail interface mechanism parameter sequence formed by step S401, standard unified processing is performed on interface mechanism parameters with different dimensions and different value ranges to eliminate the influence of parameter dimension differences on spatial distribution analysis results.

[0183] Among them, the integrity of the anti-rust oil film Surface corrosion activity index and diffusion corrosion risk coefficient Interval normalization is used to map the rust-preventive oil film thickness to a preset standard range; deviations in rust-preventive oil film thickness are addressed. The centering process is performed based on the direction and magnitude of the deviation from the standard oil film thickness, so that it can simultaneously characterize two types of state deviations: insufficient oil film thickness and redundant oil film thickness.

[0184] Based on standardized processing, spatial continuity processing is performed on various interface mechanism parameters along the guide rail length. A sliding weighted smoothing method based on adjacent detection positions is used to eliminate the disturbance of isolated fluctuation points on the overall spatial distribution trend. During the weighting process, with the current detection position as the center, interface mechanism parameters of adjacent detection positions are selected to participate in the weighting calculation. Parameters closer to the current detection position are assigned higher weights to ensure that the smoothing result still maintains the local authenticity of the guide rail interface state.

[0185] After the continuous processing is completed, continuous distribution sequences of rust-preventive oil film integrity, rust-preventive oil film thickness deviation, interface corrosion activity, and diffusion corrosion risk are formed, providing a unified and stable spatial parameter basis for subsequent consistency analysis and gradient change analysis.

[0186] S403: Consistency analysis of interface mechanism parameters along the guide rail length direction.

[0187] Based on the continuous distribution sequence of interface mechanism parameters formed in step S402, a consistency analysis is performed on the parameter distribution along the length of the guide rail at each detection location to determine the spatial uniformity and stability of the rust prevention state on the guide rail surface.

[0188] Consistency analysis is based on the parameter deviation between adjacent detection locations and the degree of parameter dispersion within a local window, and calculates the integrity of the anti-rust oil film accordingly. 1. Deviation in the thickness of the anti-rust oil film Surface corrosion activity index and diffusion corrosion risk coefficient The difference between adjacent detection positions, and the discrete statistics of each parameter within a preset local spatial window, wherein the preset local spatial window is set to 3 to 5 consecutive detection positions to match the evenly distributed spacing of the guide rail electrodes.

[0189] Within the local space window The change was small. Fluctuation range is under control. and When the fluctuation is low, such as the change in the integrity of the rust-preventive oil film within the window being ≤5%, the fluctuation range of the rust-preventive oil film thickness deviation being ≤±2μm, and the low fluctuation of the interface corrosion activity index and the diffusion corrosion risk coefficient being ≤3%, it is determined that the interface mechanism parameters within this local space range have good consistency, indicating that the rust-preventive film state on the corresponding guide rail surface is stable and the interface corrosion activity is evenly distributed.

[0190] When any interface mechanism parameter within a local space window deviates continuously, or when multiple interface mechanism parameters simultaneously show an increase in dispersion within the same space, it is determined that there is an inconsistency in the rust prevention status within that local space, indicating that the corresponding area has experienced local film degradation, enhanced interfacial activity, or changes in diffusion channels.

[0191] During the analysis, the consistency analysis results of each detection location are linked with their inversion error weights. Single-point anomalies at low-confidence locations are not directly identified as spatial anomalies, but are jointly judged in conjunction with the parameter change trends of adjacent detection locations to improve the reliability of spatial consistency analysis results. Ultimately, a consistent distribution result of the interface mechanism along the guide rail length is formed.

[0192] S404: Gradient variation analysis of interface mechanism parameters along the guide rail length direction.

[0193] Based on the consistency analysis, gradient change analysis is performed on the interface mechanism parameters along the guide rail length to identify the spatial transition characteristics of the guide rail surface rust prevention state from stable to decay and from decay to corrosion activation.

[0194] Taking the continuous distribution sequences of rust-preventive oil film integrity, rust-preventive oil film thickness deviation, interfacial corrosion activity index, and diffusion corrosion risk coefficient as the objects, the first-order difference method is used to calculate the difference between the corresponding parameter values ​​of each detection position and the previous and next detection positions, and divide it by the corresponding detection position spacing to obtain the local spatial change rate of the detection position. Then, with the current detection position as the center, the spatial change rates of each adjacent position are accumulated by distance weighting within a preset spatial window to form a cumulative change trend that characterizes the continuous change direction and intensity of the parameter at that position, and this cumulative change trend is determined as the single-parameter gradient value of the corresponding interfacial mechanism parameter.

[0195] in, When the gradient value is negative, it indicates that the integrity of the rust-preventive oil film decreases along the length of the guide rail, meaning that the oil film layer is gradually damaged; when the gradient value is positive, it indicates that the integrity of the oil film is increasing, meaning that the oil film layer is gradually intact; the larger the absolute value of the gradient, the faster the oil film integrity changes. When the absolute value of the gradient is ≥0.2, it is determined that the oil film state is changing rapidly. When the gradient value is negative, it indicates that the oil film thickness deviation decreases along the length of the guide rail, meaning the oil film thickness gradually becomes thinner; when the gradient value is positive, it indicates that the oil film thickness deviation increases, meaning the oil film thickness gradually approaches or exceeds the standard thickness; when the absolute value of the gradient is ≥0.15, it is determined that the oil film thickness changes rapidly. When the gradient value is positive, it indicates that the interfacial corrosion activity increases along the length of the guide rail, and the corrosion risk gradually increases; when the gradient value is negative, it indicates that the corrosion activity decreases, and the corrosion risk gradually decreases; when the absolute value of the gradient is ≥0.2, it is determined that the corrosion activity changes rapidly. When the gradient value is positive, it indicates that the risk of diffusion corrosion increases along the length of the guide rail and the diffusion rate of the corrosive medium accelerates; when the gradient value is negative, it indicates that the risk of diffusion corrosion decreases and the diffusion rate slows down; when the absolute value of the gradient is ≥0.18, it is determined that the risk of diffusion corrosion changes rapidly.

[0196] By combining the gradient change trends of the four parameters, a synergistic analysis is performed to identify abnormal gradient combinations related to the rust prevention status of the guide rail:

[0197] Oil film decay trend: The gradient is negative. The gradient is negative, and the absolute values ​​of both gradients are ≥0.15, indicating that the oil film layer in this region is showing a rapid decay trend, with decreased oil film integrity and thinning thickness.

[0198] Corrosion activation trend: Gradient is positive The gradient is positive, and the absolute values ​​of both gradients are ≥0.18, indicating that the interfacial corrosion activity in this region is rapidly increasing and the risk of diffusion corrosion is rapidly increasing, showing a trend of corrosion activation.

[0199] Stable trend: The absolute values ​​of the gradients of the four parameters are all ≤0.08, and the trend of change is stable, without continuous increase or decrease, indicating that the oil film state and corrosion activity in this area are stable and there are no obvious abnormalities.

[0200] The above-mentioned threshold for judging gradient changes and criteria for judging parameter change rates are all based on the uniform spacing of electrodes on the guide rail, the physical characteristics and actual change laws of interface mechanism parameters, combined with the statistical analysis of engineering measured data on the decay of anti-rust film and corrosion activation of rail transit / industrial guide rails. At the same time, they match the practical needs of early warning and risk classification in engineering corrosion prevention, ensuring that the thresholds can accurately distinguish the three trends of stable anti-rust status, oil film decay, and corrosion activation of guide rails, and that the judgment results are highly compatible with the actual anti-corrosion maintenance treatment nodes of guide rails.

[0201] Through the above gradient change analysis, spatial gradient analysis results are generated that reflect the direction, rate, and duration of change of the rust prevention status of the guide rail surface, providing a basis for subsequent area division.

[0202] S405: Zoning determination of the rust prevention status of guide rails.

[0203] Based on the results of consistency analysis and spatial gradient analysis, the rust prevention status along the length of the guide rail surface is identified and the boundaries are defined to obtain the stable rust film region, the film attenuation region, and the corrosion activation region.

[0204] When a certain continuous spatial segment , , , This indicates that the oil film layer is intact, the oil film thickness meets the standard, the interfacial corrosion activity is low, and the risk of diffusion corrosion is low. At the same time, when the consistency analysis results of this section are good, the continuous spatial section is determined to be a stable area of ​​the anti-rust film, indicating that the anti-rust film on the corresponding guide rail surface is intact, the interfacial corrosion activity is low, and the risk of diffusion corrosion is under control.

[0205] When 50% ≤ <80%, 20% ≤60%, 30%< ≤70% indicates that the oil film layer is slightly damaged, the oil film thickness is insufficient, the interfacial corrosion activity is moderate, and the risk of diffusion corrosion is moderate. However, when the overall consistency has not been completely unstable, the continuous spatial section is judged to be the film layer attenuation area, indicating that the anti-rust film on the corresponding guide rail surface has undergone local deterioration, the isolation ability has decreased, and the interfacial corrosion process has begun to intensify.

[0206] When a certain continuous spatial segment , , and If the absolute value is not less than 50% of the preset standard rust-preventive oil film thickness, that is, the actual thickness of the oil film is half or more less than the standard thickness, it indicates that the oil film layer is severely damaged, the oil film thickness is seriously insufficient, the interface corrosion activity is high, and the risk of diffusion corrosion is high. At the same time, when the consistency analysis shows that there is a significant parameter change between this section and the adjacent area, the continuous spatial section is determined to be a corrosion activation area, indicating that the rust-preventive film protection ability of the corresponding guide rail surface has obviously failed, the interface electrochemical reaction is active, and the risk of corrosion medium diffusion is high.

[0207] During the region division process, the starting position, ending position, length range, and corresponding detection position number of each region are recorded. For sections with parameter transitions at the region boundaries, the gradient change inflection point is used as the boundary correction basis to match the region division results with the actual rust prevention status change process of the guide rail surface, ultimately forming a continuous and clear guide rail rust prevention status region division result along the guide rail length direction.

[0208] refer to Figure 4 , Figure 4 This is a schematic diagram showing the spatial distribution and area identification of the rust prevention status of the guide rail.

[0209] Figure 4 The diagram illustrates the variation trend of interface mechanism parameters along the length of the guide rail and the region identification results based on this trend. The upper part shows the relative variation relationship of mechanism parameters such as the integrity of the anti-rust oil film, the interface corrosion activity, and the diffusion risk level along the length of the guide rail, while the lower part shows the corresponding linear partitioning results of the guide rail. Based on the consistent variation characteristics and gradient variation boundaries of the interface mechanism parameters, the guide rail can be divided into an anti-rust film stable zone, a film attenuation zone, and a corrosion activation zone along its length, thereby realizing the visual expression and spatial positioning identification of the guide rail's anti-rust status.

[0210] It should be noted that, Figure 4 This is for illustrative purposes only, intended to help understand the process structure and data organization, and does not represent the precise working state in actual operation.

[0211] S406: Generation of spatial distribution map of rust prevention status of guide rail.

[0212] Based on the region division results obtained in step S405, the coordinate parameters, interface mechanism parameters, and region category information of each detection location are called to generate a spatial distribution map of the guide rail rust prevention status.

[0213] The spatial distribution map of the rust prevention status of the guide rail uses the length direction of the guide rail as the horizontal spatial coordinate and the actual coordinate parameters corresponding to the detection position as the positioning basis. It maps the interface mechanism parameters of each detection position to the corresponding spatial position and distinguishes and displays the stable area of ​​the rust prevention film, the film attenuation area and the corrosion activation area through continuous area coloring.

[0214] The spatial distribution map is simultaneously overlaid with information on the changing trends of the integrity of the anti-rust oil film, the interfacial corrosion activity index, and the diffusion corrosion risk coefficient. This allows the spatial distribution map to not only reflect the regional categories but also characterize the changing features and evolution direction of the interfacial mechanism parameters within each region.

[0215] During the generation process, the boundary locations, lengths, dominant interface mechanism parameters, and criteria for region determination of each region are marked to ensure that the spatial distribution map has clear readability and traceability. At the same time, the detection time, total number of detection locations, source of interface mechanism parameters, and region division rules corresponding to the spatial distribution map are associated and recorded to form a complete result data file.

[0216] The final output spatial distribution map of the rust prevention status of the guide rail is used to characterize the spatial distribution of the stable area, film attenuation area and corrosion activation area of ​​the rust prevention film on the guide rail surface, providing a visual basis for the assessment of the rust prevention status of the guide rail surface and subsequent maintenance decisions.

[0217] This embodiment collects voltage and current response signals by deploying impedance detection electrodes on the guide rail surface and calculates the guide rail impedance spectrum data. It constructs the guide rail impedance spectrum curve and analyzes the impedance response at different frequency bands to extract impedance spectrum characteristic parameters characterizing oil film isolation, charge transfer, and diffusion impedance. An equivalent circuit model is established to perform parameter inversion calculations, obtaining interface mechanism parameters characterizing the integrity of the anti-rust film, oil film thickness variation, and interface corrosion activity. Furthermore, spatial consistency analysis and gradient change analysis identify stable regions, film attenuation regions, and corrosion activation regions of the anti-rust film on the guide rail surface, generating a spatial distribution map of the guide rail's anti-rust status. This enables mechanistic identification, continuous spatial monitoring, and regional assessment of the guide rail's anti-rust status, providing reliable data and visualized decision support for guide rail corrosion protection maintenance and operation management.

[0218] Example 2

[0219] This invention discloses a guide rail rust prevention condition monitoring system based on impedance detection, including a data acquisition module, a feature analysis module, a mechanism inversion module, and a condition identification module:

[0220] The data acquisition module synchronously acquires the guide rail voltage response signal and current response signal at each excitation frequency. Based on the amplitude and phase relationship between voltage and current, it calculates the complex impedance value at the corresponding frequency and combines them in ascending order of frequency to form the guide rail impedance spectrum data corresponding to each detection position.

[0221] The feature analysis module constructs the impedance spectrum curve of the rail surface based on the rail impedance spectrum data. It performs frequency band analysis on the amplitude variation trend and phase variation characteristics of the impedance spectrum curve in different frequency ranges, separates the impedance response components generated by the interface of the anti-rust oil film, the electrochemical double layer and the metal substrate, extracts the impedance spectrum feature parameters that characterize the isolation capability of the film layer, the interface charge transfer capability and the diffusion impedance characteristics, and collects them according to the feature category and frequency band attribute to form the rail impedance feature set.

[0222] The mechanism inversion module establishes an equivalent circuit model based on the electrochemical reaction mechanism of the guide rail surface. It uses the guide rail impedance spectrum data as the fitting object and the guide rail impedance characteristic set as the constraint condition to perform parameter inversion calculation, so that the impedance response calculated by the equivalent circuit model is consistent with the guide rail impedance spectrum data. In this way, the interface mechanism parameters characterizing the integrity of the anti-rust film and the level of interface corrosion activity can be obtained.

[0223] Based on the interface mechanism parameters at each detection location, the state identification module performs consistency analysis and gradient change analysis on the spatial distribution of the interface mechanism parameters along the length of the guide rail, generating a spatial distribution map of the guide rail's rust prevention state to characterize the stable region, attenuation region, and corrosion activation region of the rust prevention film on the guide rail surface.

[0224] The specific functions of each module described above are as described in the relevant content of the guide rail rust prevention status monitoring method based on impedance detection in Example 1, and will not be repeated here.

[0225] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for monitoring the rust prevention status of guide rails based on impedance detection, characterized in that, Includes the following steps: An AC excitation signal with a preset frequency range is applied to the guide rail metal substrate, and the voltage response signal and current response signal are collected simultaneously and the complex impedance value of the corresponding frequency is calculated to form the guide rail impedance spectrum data corresponding to each detection position. Based on the impedance spectrum data of the rail, the surface impedance spectrum curve of the rail is constructed. Frequency band analysis is performed to separate the impedance response components of the anti-rust oil film layer, the electrochemical double layer and the metal substrate interface. The impedance spectrum feature parameters are extracted and the rail impedance feature set is formed according to the feature category and frequency band attribute. An equivalent circuit model was established based on the electrochemical reaction mechanism of the guide rail surface. The guide rail impedance spectrum data was used as the fitting object and the guide rail impedance characteristic set was used as the constraint condition to perform parameter inversion calculation. The interface mechanism parameters characterizing the integrity of the anti-rust film and the level of interface corrosion activity were obtained by solving the interface mechanism parameters. Consistency analysis and gradient change analysis were performed on the spatial distribution of interface mechanism parameters at each detection location along the length of the guide rail to generate a spatial distribution map of the guide rail's rust prevention status, which characterizes the stable region, film attenuation region, and corrosion activation region of the rust prevention film on the guide rail surface.

2. The method for monitoring the rust prevention status of guide rails based on impedance detection as described in claim 1, characterized in that, Perform parameter inversion calculations, including: An equivalent circuit model was established based on the electrochemical reaction mechanism of the guide rail surface. The equivalent circuit model adopts a series structure, with the equivalent circuit unit of the anti-rust oil film layer, the equivalent circuit unit of the electrochemical double layer, and the equivalent circuit unit of the metal substrate interface connected in series in sequence. Each unit adopts a resistance-capacitor series model, a resistance-capacitor parallel model, and a resistance-diffusion impedance series model, respectively. Based on the rail impedance feature set, three types of constraints are determined: parameter range constraint, feature matching constraint, and mechanism consistency constraint. The rail impedance spectrum data is used as the fitting object. A combined algorithm of least squares method and genetic algorithm is adopted. The objective function is to minimize the fitting error between the impedance response of the equivalent circuit model and the rail impedance spectrum data. The parameters of each component are iteratively optimized under the three types of constraints. Based on the iterative optimization results, the fitting accuracy verification, constraint satisfaction verification, and repeatability verification are performed sequentially. After each verification is qualified, the inversion results of the equivalent circuit model element parameters corresponding to each detection position are organized to form the equivalent circuit model parameter set.

3. The method for monitoring the rust prevention status of guide rails based on impedance detection as described in claim 1, characterized in that, Frequency band analysis and separation of impedance response components, including: The preset frequency range is divided into high-frequency, mid-frequency, and low-frequency bands. The high-frequency band corresponds to the impedance response of the anti-rust oil film layer, the mid-frequency band corresponds to the impedance response of the electrochemical double layer, and the low-frequency band corresponds to the impedance response of the metal matrix interface and diffusion process. The amplitude variation trend analysis and phase variation characteristic analysis are performed on the impedance spectrum curves in each frequency band to form a frequency band analysis dataset. Based on the frequency band analysis dataset, the equivalent circuit decomposition method is used to separate the impedance response components of each interface in sequence: the impedance response component of the rust-preventive oil film layer is obtained by fitting the high-frequency impedance spectrum data with a resistance-capacitance series model; the impedance response component of the electrochemical double layer is obtained by fitting the mid-frequency net impedance data after deducting the impedance response component of the rust-preventive oil film layer with a resistance-capacitance parallel model; and the impedance response components of the metal matrix interface and diffusion are obtained by fitting the low-frequency net impedance data after deducting the two impedance response components with a resistance-diffusion impedance series model. The three impedance response components are superimposed and compared with the rail impedance spectrum data. If the relative error meets the preset threshold, the separation is deemed effective.

4. The method for monitoring the rust prevention status of guide rails based on impedance detection as described in claim 2, characterized in that, Solving for interface mechanism parameters, including: The integrity and thickness deviation of the rust-preventive oil film are solved from the parameters of the equivalent circuit unit of the rust-preventive oil film layer. The integrity of the rust-preventive oil film is referenced to the maximum resistance value corresponding to the preset intact rust-preventive oil film layer, which characterizes the oil film isolation and protection capability. The thickness deviation of the rust-preventive oil film is referenced to the preset standard rust-preventive oil film thickness, which characterizes the degree of deviation of the actual state of the oil film layer thickness. The interfacial corrosion activity index is solved from the parameters of the electrochemical double-layer equivalent circuit unit, and the diffusion corrosion risk coefficient is solved from the parameters of the metal matrix interfacial equivalent circuit unit. The integrity of the rust-preventive oil film, the thickness deviation of the rust-preventive oil film, the interfacial corrosion activity index, and the diffusion corrosion risk coefficient are grouped according to the detection location and associated with coordinate parameters to form a set of interfacial mechanism parameters.

5. The method for monitoring the rust prevention status of guide rails based on impedance detection as described in claim 1, characterized in that, Consistency analysis includes: The interface mechanism parameters at each detection location are mapped to the actual spatial location according to the coordinate parameters, and a mapping relationship between the detection location number and the interface mechanism parameters is established to form an interface mechanism spatial sequence arranged in an orderly manner along the length of the guide rail. Based on the spatial sequence of interface mechanisms, the parameters of each interface mechanism are standardized. The spatial continuity is achieved by using a sliding weighted smoothing method with the current detection position as the center and the closer the position, the higher the weight. This results in a continuous distribution sequence of each interface mechanism parameter. Based on the continuous distribution sequence of each interface mechanism parameter, and taking into account the parameter deviation between adjacent detection positions and the parameter dispersion within a local window, when any interface mechanism parameter shows continuous deviation or multiple interface mechanism parameters show synchronous increase in dispersion, it is determined that there is an inconsistent rust prevention state in that local area, thus forming a consistent distribution result of the interface mechanism.

6. The method for monitoring the rust prevention status of guide rails based on impedance detection as described in claim 5, characterized in that, Gradient change analysis includes: Taking the continuous distribution sequence of each interface mechanism parameter as the object, the first-order difference method is used to calculate the spatial change rate of each detection position relative to the adjacent detection positions, and the change trend is accumulated within a preset spatial window to obtain the gradient value of each interface mechanism parameter. Collaborative analysis is performed based on the gradient values ​​of various interface mechanism parameters: when the gradients of interface mechanism parameters characterizing the integrity of the anti-rust film are all negative and their absolute values ​​all reach the preset oil film attenuation threshold, the region is determined to have an oil film attenuation trend; when the gradients of interface mechanism parameters characterizing the level of interface corrosion activity are all positive and their absolute values ​​all reach the preset corrosion activation threshold, the region is determined to have a corrosion activation trend; when the absolute values ​​of the gradients of each interface mechanism parameter are all below the preset stability threshold, the region is determined to have a stable trend. Based on the gradient trend determination results of each region, spatial gradient analysis results reflecting the direction, rate, and duration of rust prevention status changes are generated.

7. The method for monitoring the rust prevention status of guide rails based on impedance detection as described in claim 6, characterized in that, Generate a spatial distribution map of the rust prevention status of the guide rail, including: Based on the results of interface mechanism consistency distribution and spatial gradient analysis, the execution area along the length of the guide rail surface is identified according to the value range and gradient trend of each interface mechanism parameter: continuous sections where all interface mechanism parameters are in the low-risk range and have good consistency are identified as rust-preventive film stable areas; continuous sections where some interface mechanism parameters enter the medium-risk range and the gradient trend shows an oil film decay trend are identified as film decay areas; and continuous sections where all interface mechanism parameters enter the high-risk range and the gradient trend shows a corrosion activation trend are identified as corrosion activation areas. Based on the region identification results, the region boundaries are corrected by the gradient change inflection points of each region, and the starting position, ending position and length range of each region are recorded. The spatial range of the rust-preventive film stable region, film attenuation region and corrosion activation region is mapped to the guide rail coordinate position to generate a spatial distribution map of the guide rail rust prevention status.

8. The method for monitoring the rust prevention status of guide rails based on impedance detection as described in claim 1, characterized in that, The guide rail impedance spectrum data corresponding to each detection position is generated, including: Inert electrodes are selected as impedance detection electrodes and arranged at equal intervals along the length of the guide rail. Each impedance detection electrode is numbered sequentially along the length of the guide rail and its coordinate parameters are recorded. Connect the working electrode interface of the impedance measuring device to the guide rail metal substrate, connect the reference electrode interface to the reference electrode, and connect the auxiliary electrode interface to each impedance detection electrode respectively. Use shielded wires for the connecting wires, and apply AC excitation signals at each frequency point in ascending order of frequency. Based on the application of AC excitation signals at each frequency point, voltage response signals and current response signals are synchronously acquired. The acquired signals are filtered and the average amplitude and average phase of each frequency point are calculated. The complex impedance values ​​corresponding to each frequency point are sequentially arranged and combined in ascending order of frequency to form the rail impedance spectrum data.

9. The method for monitoring the rust prevention status of guide rails based on impedance detection as described in claim 3, characterized in that, Impedance spectrum characteristic parameters are extracted from each impedance response component and aggregated to form a rail impedance characteristic set, including: Based on the impedance response components of the rust-preventive oil film, the oil film resistance, oil film capacitance, and oil film insulation coefficient are extracted, classified into the oil film isolation characteristic category, and associated with the high-frequency band; based on the electrochemical double-layer impedance response components, the interface charge transfer resistance, double-layer capacitance, and charge transfer rate constant are extracted, classified into the charge transfer characteristic category, and associated with the mid-frequency band; based on the interface and diffusion impedance response components of the metal substrate, the diffusion impedance coefficient, the bulk resistance of the metal substrate, and the diffusion rate are extracted, classified into the diffusion impedance characteristic category, and associated with the low-frequency band. Outliers were removed and standardized according to the three sigma criterion for the extracted feature parameters. The coordinate parameters of each detection position were associated and the oil film isolation feature, charge transfer feature and diffusion impedance feature parameters were integrated to form the rail impedance feature set.

10. A guide rail rust prevention status monitoring system based on impedance detection, used to implement the guide rail rust prevention status monitoring method based on impedance detection as described in any one of claims 1-9, characterized in that, It includes a data acquisition module, a feature analysis module, a mechanism inversion module, and a state identification module: The data acquisition module applies an AC excitation signal within a preset frequency range to the guide rail metal substrate, simultaneously acquires voltage response signals and current response signals, and calculates the complex impedance value at the corresponding frequency to form guide rail impedance spectrum data corresponding to each detection position. The feature analysis module constructs the impedance spectrum curve of the rail surface based on the rail impedance spectrum data, performs frequency band analysis, separates the impedance response components of the anti-rust oil film layer, electrochemical double layer and metal substrate interface, extracts impedance spectrum feature parameters, and collects them into a rail impedance feature set according to feature category and frequency band attribute. The mechanism inversion module establishes an equivalent circuit model based on the electrochemical reaction mechanism of the guide rail surface, uses the guide rail impedance spectrum data as the fitting object and the guide rail impedance characteristic set as the constraint condition to perform parameter inversion calculation, and solves the interface mechanism parameters characterizing the integrity of the anti-rust film and the level of interface corrosion activity. The state identification module performs consistency analysis and gradient change analysis on the spatial distribution of interface mechanism parameters at each detection location along the length of the guide rail, generating a spatial distribution map of the guide rail's rust prevention state to characterize the stable region, attenuation region, and corrosion activation region of the rust prevention film on the guide rail surface.

Images