A method and system for assessing the wear of bridge bearing sliding plates

Abstract

This invention discloses a method and system for assessing the wear of bridge bearing sliding plates. The assessment method includes the following steps: collecting multi-source heterogeneous data reflecting the service status of bridge bearings; processing the vertical spacing data to remove measurement interference caused by the deformation of the main beam and extracting the effective vertical spacing data within the structural quiet window; performing structural thermal expansion compensation on the effective vertical spacing data in conjunction with real-time temperature data, and introducing a lower envelope algorithm based on monotonically decreasing physical constraints for data denoising to obtain the true exposed thickness monitoring value of the sliding plate after removing environmental and wear debris interference; assessing the current wear state and uneven pressure condition of the bridge bearing sliding plate based on the true exposed thickness monitoring value; constructing a nonlinear degradation mapping model based on the cumulative sliding stroke data and the true exposed thickness monitoring value, and assessing the remaining service life of the sliding plate. This assessment method provides a decision-making reference for the refined maintenance of bridge bearings.

Description

Technical Field

[0001] This invention belongs to the field of bridge engineering monitoring technology, and more specifically, relates to a method and system for assessing the wear of bridge bearing sliding plates. Background Technology

[0002] Long-span suspension bridges and cable-stayed bridges, due to their superior spanning capacity, have become the mainstream bridge types for crossing rivers, lakes, seas, and complex terrains. However, these bridge structures have relatively flexible overall stiffness, and under the combined effects of increasing traffic flow, wind loads, and temperature effects, the main girder exhibits significant high-frequency, low-amplitude motion characteristics. Bridge bearings, as key force-transmitting components connecting the superstructure and substructure, not only bear the heavy responsibility of transmitting the vertical load of the main girder but also need to adapt to the complex displacement and rotational deformation generated during operation. During the long-term service of long-span cable-stayed bridges, the high-frequency, low-amplitude reciprocating motion at the beam ends results in a huge daily cumulative displacement within the bearings, causing the vulnerable components within the bearings, especially the friction pair sliding plates, to endure severe frictional wear. Furthermore, bridge bearings are often exposed to the elements, facing complex conditions such as corrosive media erosion, uneven stress distribution, and dust and impurity intrusion, further accelerating the aging and wear process of the sliding plate materials. This non-linear rapid wear not only shortens the actual service life of the bearings, but may also cause bearing failure, thereby threatening the overall structural safety and operational stability of the bridge and posing a severe challenge to subsequent maintenance and repair.

[0003] Currently, the industry primarily relies on a combination of regular manual inspections and real-time monitoring using external sensors to assess and maintain the service condition of bridge bearings. For routine manual inspections, long-span cable-stayed bridges typically undergo an annual inspection. Inspectors visually examine the bearing's appearance and use simple measuring tools such as feeler gauges to measure the thickness of exposed sliding plates, thus inferring the degree of wear. In the field of automated monitoring, existing technologies mainly involve installing external displacement and rotation sensors around the bearings or at key locations on the bridge structure to collect real-time data on the bearing's displacement and rotational deformation, thereby indirectly reflecting the bearing's motion status. These existing inspection and monitoring systems, to a certain extent, capture the macroscopic motion trajectory of the bearings and perform preliminary screening of external defects, forming the basic data support for current bridge maintenance.

[0004] However, the aforementioned existing technologies have significant limitations and defects in practical applications, making it difficult to effectively solve the problem of accurate assessment of wear on bearing slide plates in long-span bridges. For example, the time span of manual periodic inspections is large, usually annual or semi-annually. This discrete inspection frequency cannot continuously capture the dynamic evolution of slide plate wear, resulting in serious lag in the inspection results. It cannot accurately reveal the true wear pattern of the slide plate under high-frequency, low-amplitude motion, and is very easy to miss the optimal maintenance window. Secondly, although existing external sensor monitoring schemes can achieve real-time data acquisition, their monitoring objects are mainly the overall displacement and rotation angle of the bearing, which are indirect parameters and cannot directly quantify the thickness loss of the slide plate material. More importantly, the exposed thickness of bridge bearing slide plates is usually very small, generally less than 3 mm, and the internal space of the bearing is small and the structure is compact. Conventional contact or non-contact monitoring equipment is limited by installation space and line of sight obstruction, making it difficult to penetrate deep into the bearing to directly and accurately monitor the minute changes in slide plate thickness in real time. Summary of the Invention

[0005] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a method and system for assessing the wear of bridge bearing sliding plates. By constructing a multi-source heterogeneous data fusion and deep mining mechanism, it adaptively fits and dynamically obtains the wear coefficient reflecting the current complex working conditions. Combined with a preset sliding plate safety failure threshold, it achieves accurate prediction of the remaining service life of the sliding plate. At the same time, it overcomes spatial limitations and environmental interference, reveals the nonlinear evolution law of sliding plate wear, and realizes a technological leap from passive inspection to proactive predictive maintenance, significantly improving the accuracy and foresight of bridge bearing operation and maintenance.

[0006] To achieve the above objectives, the present invention provides a method for assessing the wear of bridge bearing sliding plates, comprising the following steps: S1: Through a sensor network deployed on and around the bridge bearings, multi-source heterogeneous data reflecting the service status of the bridge bearings are collected synchronously. S2: Perform dynamic variance filtering on the vertical spacing data based on dynamic load characteristics to eliminate measurement interference caused by the deformation of the main beam and extract the effective vertical spacing data within the structural quiet window period; S3: Combine real-time temperature data to perform structural thermal expansion compensation on the effective vertical spacing data, and introduce a lower envelope algorithm based on monotonically decreasing physical constraints to denoise the data, and obtain the true external thickness monitoring value of the slide plate after removing environmental and wear debris interference. S4: Based on the actual external thickness monitoring value, evaluate the current wear status and uneven pressure condition of the bridge bearing slide plate; S5: Based on the cumulative sliding stroke data and the actual external leakage thickness monitoring value, a nonlinear degradation mapping model with the cumulative sliding stroke as the driving variable is constructed. The dynamic wear coefficient reflecting the current service conditions is obtained through adaptive fitting, and the remaining service life of the slide is predicted by combining the preset slide safety failure threshold.

[0007] Further, step S1 includes: S11: Four high-precision vertical displacement sensors are evenly distributed in a rectangle and vertically fixed to the upper spherical cap liner of the bridge bearing; the detection end of the vertical displacement sensor is vertically oriented towards the bottom surface of the corresponding upper bearing plate assembly, so as to read and record the vertical distance between each vertical displacement sensor and the corresponding upper bearing plate assembly in real time. S12: Surface temperature sensors are attached to the exposed surfaces of the upper bearing plate assembly and the lower base plate assembly to obtain real-time temperature data of the bridge bearing. S13: Horizontal displacement sensors are externally installed between the upper bearing plate assembly and the lower load-bearing structure in the longitudinal and transverse directions of the bridge bearing to monitor the instantaneous horizontal relative displacement of the bridge bearing and record the cumulative sliding stroke between the upper and lower friction pairs of the bridge bearing. S14: Using the time axis with the highest sampling frequency as a reference, assign a global timestamp to the vertical spacing data, the temperature data, and the cumulative sliding stroke data through the Global Positioning System or Network Time Protocol.

[0008] Furthermore, the cumulative sliding stroke is calculated as follows: ; In the formula, To accumulate sliding travel; This is the latest discrete data sampling point sequence number; , For the support in the first Absolute displacement coordinates in the longitudinal and transverse directions at each sampling time; , These are the absolute displacement coordinates in the longitudinal and transverse directions at the previous sampling time.

[0009] Further, step S2 includes: S21: Based on the continuously acquired vertical spacing data, a sliding time window with a fixed time length and sliding step size is set along the time axis, and within each sliding time window, a set of vertical spacing data containing multiple discrete sampling points is extracted; S22: For the set of vertical spacing data within the sliding time window, calculate the arithmetic mean of the data corresponding to each vertical displacement sensor measuring point, and calculate the short-time data variance within the sliding time window based on the arithmetic mean. The calculation formula is as follows: ; In the formula, For the first The vertical displacement sensor in the first Short-term data variance within a time window; This represents the total number of discrete sampling points contained within the sliding window; This is the internal time index for discrete sampling points within the window; For the first The vertical displacement sensor in the first The first window Measured values ​​of instantaneous vertical spacing; For the first The vertical displacement sensor in the first The arithmetic average of the vertical spacing within each window; S23: Based on the pre-stored dynamic load fluctuation threshold, the short-time data variance corresponding to each vertical displacement sensor measurement point is compared with the dynamic load fluctuation threshold one by one; when any short-time data variance is greater than the dynamic load fluctuation threshold, it is determined that the bridge is currently bearing vehicle dynamic load that causes structural elastic deformation within the current sliding time window, and the entire set of vertical spacing data within the sliding time window is marked as contaminated interference data and removed. S24: When the variance of the short-time data corresponding to all sensor measurement points is less than or equal to the dynamic load fluctuation threshold, it is determined that the bridge is in a quiet window period under constant self-weight within the current sliding time window. At this time, the arithmetic mean of all measured vertical spacing values ​​within the sliding time window is extracted and output as the effective vertical spacing data representing the quiet period.

[0010] Further, step S3 includes: S31: Based on the reference calibration data, including the initial reference temperature, the initial vertical spacing readings of each vertical displacement sensor, and the initial exposed thickness of the slide plate measured manually, calculate and solidify the inherent vertical distance constant between each vertical displacement sensor and the upper surface of the slide plate; at the same time, synchronously acquire the effective vertical spacing data and real-time temperature data at the current moment; S32: Based on the linear expansion coefficient of the steel components affected by the thermal expansion of the bridge bearing, calculate the absolute structural deformation caused by the real-time temperature difference; subtract the absolute structural deformation from the effective vertical spacing data to obtain the theoretical vertical spacing after temperature compensation; further subtract the inherent vertical distance constant from the theoretical vertical spacing to obtain the preliminary exposed thickness of the sliding plate after eliminating dynamic load deformation and temperature interference. S33: Save the measured external thickness of the skateboard to the skateboard's historical minimum thickness record stack. Introduce a lower envelope constraint based on the physical property that the wear thickness of the skateboard decreases in a strictly monotonically manner. Compare the initial external thickness of the skateboard with the historical minimum thickness record value from the previous moment: If the initial external thickness of the skateboard is greater than or equal to the historical minimum thickness record value, it is determined that there is a false interference caused by the wear debris padding on the current sliding interface. The historical minimum thickness record value is forcibly output as the actual external thickness monitoring value at the current moment, and the historical minimum thickness record value remains unchanged. If the initial external thickness of the skateboard is less than the historical minimum thickness record value, it is determined that the skateboard has undergone substantial physical wear. The initial external thickness of the skateboard is output as the actual external thickness monitoring value at the current moment, and the actual external thickness monitoring value is overwritten and updated in the historical minimum thickness record stack.

[0011] Further, step S4 includes: S41: Simultaneously acquire the actual external leakage thickness monitoring value of the measuring points where the four vertical displacement sensors are located at the current moment, and read the fixed horizontal distance between adjacent vertical displacement sensors; S42: Calculate the wear of the skateboard's external thickness by taking the minimum value of the actual external thickness monitoring values ​​of the four measuring points; compare this minimum value with the preset health status threshold range to evaluate the overall wear condition of the skateboard; S43: Calculate the uneven pressure rotation of the slide plate based on the actual external thickness monitoring values ​​and horizontal spacing of different measuring points, calculate the vertical rotation angle of the slide plate at adjacent vertical displacement sensors, and take the maximum value among the vertical rotation angles of the slide plate at the four measuring points as the characteristic rotation angle for evaluating the uneven pressure condition of the slide plate.

[0012] Further, step S5 includes: S51: Based on the historical sequence of the cumulative sliding travel data of the bridge bearing in the transverse and longitudinal directions, which is continuously calculated and recorded, and the historical sequence of the minimum actual exposed thickness of the sliding plate that is strictly aligned with the timestamp of the cumulative sliding travel data, a nonlinear degradation mapping model conforming to the contact mechanical wear law is fitted using the nonlinear least squares method; the nonlinear degradation mapping model is as follows: ; In the formula, This refers to the cumulative sliding stroke between the upper and lower friction pairs of the bridge bearing. For a certain cumulative sliding stroke At that time, the theoretical predicted value of the exposed thickness of the skateboard; The dynamic wear coefficient; It is a non-linear degradation index; S52: Based on the aforementioned nonlinear degradation mapping model, with the minimum exposed thickness of the skateboard as the safety boundary, calculate the remaining total sliding stroke that the skateboard can withstand before reaching the failure standard; then, combined with the bridge's average daily cumulative sliding stroke rate within the recent sliding window, convert the calculated remaining total sliding stroke into the remaining service life days of the skateboard, and update it in real time.

[0013] Furthermore, the formulas for calculating the remaining total sliding stroke and the remaining service life in days are as follows: ; ; In the formula, The remaining physical travel distance that the skateboard can slide from its current state to its safe limit; Minimum exposed thickness of the preset skateboard; This represents the total actual sliding distance recorded by the system at the current moment. The predicted remaining lifespan of the skateboard in days; This refers to the average daily cumulative sliding travel rate of the bridge within the most recent statistical period.

[0014] A second aspect of the present invention provides a bridge bearing slide plate wear assessment system, applied to the assessment method described above, comprising: Multi-source heterogeneous data synchronous acquisition module: used to synchronously acquire multi-source heterogeneous data reflecting the service status of bridge bearings through a sensor network deployed in and around the bridge bearings; Dynamic variance filtering module: used to perform dynamic variance filtering on vertical spacing data based on dynamic load characteristics, eliminate measurement interference caused by main beam deformation, and extract effective vertical spacing data within the structural quiet window period; Structural thermal expansion compensation and physical constraint denoising module: used to perform structural thermal expansion compensation on the effective vertical spacing data by combining real-time temperature data, and introduce a lower envelope algorithm based on monotonically decreasing physical constraints to denoise the data, and obtain the true external thickness monitoring value of the slide plate after removing environmental and wear debris interference. The slide plate wear condition and uneven pressure condition assessment module is used to assess the current wear condition and uneven pressure condition of the bridge bearing slide plate based on the actual external thickness monitoring value. The remaining service life prediction module for the skateboard is used to construct a nonlinear degradation mapping model with the cumulative sliding stroke as the driving variable based on the cumulative sliding stroke data and the actual external leakage thickness monitoring value. It obtains the dynamic wear coefficient reflecting the current service conditions through adaptive fitting and predicts the remaining service life of the skateboard by combining it with the preset skateboard safety failure threshold.

[0015] A third aspect of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein the computer program, when executed by a processor, controls the device in which the storage medium is located to perform the evaluation method as described above.

[0016] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: 1. The evaluation method of this invention constructs a multi-source heterogeneous data fusion and deep mining mechanism, adaptively fits and dynamically obtains the wear coefficient reflecting the current complex working conditions, and combines it with a preset sliding plate safety failure threshold to achieve accurate prediction of the remaining service life of the sliding plate. At the same time, it overcomes spatial limitations and environmental interference, reveals the nonlinear evolution law of sliding plate wear, realizes the technological leap from passive inspection to proactive predictive maintenance, and significantly improves the accuracy and foresight of bridge bearing operation and maintenance.

[0017] 2. The evaluation method of this invention, by simultaneously introducing surface temperature data and horizontal cumulative sliding stroke data, expands the originally isolated vertical spacing monitoring into a three-dimensional data matrix that includes temperature field state, high-frequency vertical fluctuations, and absolute frictional displacement. This provides the necessary basic compensation benchmark for subsequent algorithms to accurately eliminate thermal expansion interference and dynamic load deformation. Furthermore, it corrects the core driving parameter for wear life prediction from static time to dynamic relative sliding distance, thereby ensuring that the evaluation model conforms to the first principles of tribology at the data source level, laying a data foundation for achieving high-precision, interference-resistant sliding plate condition monitoring.

[0018] 3. The evaluation method of this invention, by introducing a time sliding window and short-time data variance calculation, obtains the ability to autonomously identify the instantaneous stress conditions of bridges, enabling precise screening and extraction of quiet window periods when the bridge is under constant self-weight without heavy vehicle traffic. The effective vertical spacing data extracted thereby eliminates measurement artifacts caused by high-frequency dynamic loads from a physical source, thus providing a set of high-fidelity and pure high-quality benchmark data sources for subsequent temperature compensation, envelope denoising, and high-confidence slide plate life prediction.

[0019] 4. The evaluation method of this invention transforms the physical nature of irreversible material wear and strictly monotonically decreasing thickness in tribology into a mathematical lower envelope algorithm, forcibly suppressing and eliminating any abnormal data rebound caused by wear debris padding, thereby finally outputting a set of smooth and completely consistent real external leakage thickness monitoring values, providing absolutely reliable underlying data support for subsequent high-precision condition assessment and scientific life prediction.

[0020] 5. The evaluation method of the present invention, by continuously absorbing the latest high-fidelity thickness minimum data extracted from cleaning, can adaptively solve and update the dynamic wear coefficient in reverse, thereby achieving high-precision and adaptive prediction of the remaining physical life of the skateboard under the scientific framework of replacing time with travel, and completely solving the prediction error of the traditional method caused by linear fitting. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the steps of the evaluation method according to an embodiment of the present invention; Figure 2 This is a first installation diagram of the sensor according to an embodiment of the present invention; Figure 3 This is a second installation diagram of the sensor according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of the evaluation system according to an embodiment of the present invention.

[0022] In all the accompanying drawings, the same reference numerals denote the same technical features, specifically: 1-upper seat plate assembly, 2-upper guide rail assembly, 3-slide plate, 4-upper spherical crown liner, 5-upper spherical slide plate, 6-intermediate liner, 7-tensile shaft, 8-lower spherical crown liner, 9-lower spherical slide plate, 10-lower flat slide plate, 11-lower guide rail assembly, 12-lower base plate assembly, 13-vertical displacement sensor, 14-temperature sensor, 15-horizontal displacement sensor. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0024] Example 1 Please refer to Figures 1 to 3 This invention provides a method for assessing the wear of bridge bearing sliding plates, comprising the following steps: S1: Through a sensor network deployed on and around the bridge bearings, multi-source heterogeneous data reflecting the service status of the bridge bearings are collected synchronously. S2: Perform dynamic variance filtering on the vertical spacing data based on dynamic load characteristics to eliminate measurement interference caused by the deformation of the main beam and extract the effective vertical spacing data within the structural quiet window period; S3: Combine real-time temperature data to perform structural thermal expansion compensation on the effective vertical spacing data, and introduce a lower envelope algorithm based on monotonically decreasing physical constraints to denoise the data, and obtain the true external thickness monitoring value of the slide plate 3 after removing environmental and wear debris interference. S4: Based on the actual external thickness monitoring value, evaluate the current wear state and uneven pressure condition of the bridge bearing sliding plate 3; S5: Based on the cumulative sliding stroke data and the actual external leakage thickness monitoring value, a nonlinear degradation mapping model with the cumulative sliding stroke as the driving variable is constructed. The dynamic wear coefficient reflecting the current service conditions is obtained through adaptive fitting, and the remaining service life of the slide plate 3 is predicted in combination with the preset safety failure threshold of the slide plate 3.

[0025] Specifically, existing bearing slide plate wear monitoring mainly relies on single vertical displacement measurement or assessment based solely on time span, leading to severe data coupling interference problems under complex operating conditions. In actual bridge service environments, the vertical spacing changes measured by sensors not only include the actual wear of slide plate 3, but also deeply superimposed the thermal expansion and contraction deformation of steel components caused by environmental temperature differences, as well as the structural elastic bending deformation caused by dynamic loads such as heavy traffic. Furthermore, traditional assessments rely solely on the natural passage of time to fit the lifespan, failing to truly reflect the essential physical causes of slide plate 3 wear. This one-dimensional information acquisition makes it impossible for the system to effectively remove the aforementioned physical noise, easily leading to distorted assessments of slide plate 3 thickness and lifespan predictions. Therefore, this embodiment uses the multi-source heterogeneous data from step S1 to construct a complete and comprehensive multivariate independent variable data foundation for subsequent physical noise removal and scientific lifespan prediction. Wherein: Step S1 includes: S11: Four high-precision vertical displacement sensors 13 are evenly distributed in a rectangle and vertically fixed on the upper spherical crown liner 4 of the bridge bearing; the detection end of the vertical displacement sensor 13 is vertically oriented towards the bottom surface of the corresponding upper bearing plate assembly 1, so as to read and record the vertical distance between each vertical displacement sensor 13 and the corresponding upper bearing plate assembly 1 in real time. S12: Surface temperature sensors 14 are attached to the exposed surfaces of the upper bearing plate assembly 1 and the lower base plate assembly 12 respectively to obtain real-time temperature data of the bridge bearing. S13: Horizontal displacement sensors 15 are externally installed between the upper bearing plate assembly 1 in the longitudinal and transverse directions and the lower bearing structure, respectively, to monitor the instantaneous horizontal relative displacement of the bridge bearing and record the cumulative sliding stroke between the upper and lower friction pairs of the bridge bearing. S14: Using the time axis with the highest sampling frequency as a reference, assign a global timestamp to the vertical spacing data, the temperature data, and the cumulative sliding stroke data through the Global Positioning System or Network Time Protocol.

[0026] It should be noted that in this embodiment, the vertical displacement sensor 13, the temperature sensor 14, and the horizontal displacement sensor 15 are all common sensors in the prior art, such as laser displacement sensors, platinum resistance temperature sensors, and laser displacement sensors. In other embodiments, other types of sensors may also be used, and no specific limitation is made here. In an optional embodiment, when the horizontal displacement sensor 15 is a laser displacement sensor, there are at least two of them; the laser displacement sensor is rigidly anchored to the side of the lower base plate assembly 12 or the upper end of the platform by a mounting bracket, and its reflective target is rigidly fixed to the side of the upper base plate assembly 1.

[0027] In an optional embodiment, the cumulative sliding stroke is calculated as follows: ; In the formula, To accumulate sliding travel; This is the latest discrete data sampling point sequence number; , For the support in the first Absolute displacement coordinates in the longitudinal and transverse directions at each sampling time; , These are the absolute displacement coordinates in the longitudinal and transverse directions at the previous sampling time.

[0028] Specifically, in actual bridge service environments, bridge bearings are subjected to dynamic loads such as heavy vehicle impacts and wind loads under high traffic volumes for extended periods. These dynamic loads cause high-frequency elastic bending and transient deformation at the microscopic level in steel structural components such as the bearing upper plate. Existing monitoring systems typically equate continuously acquired real-time vertical spacing readings directly to the exposed thickness of the sliding plate 3. This results in the measurement data being heavily superimposed with elastic deformation noise of this magnitude, which may even mask the actual minute wear of the sliding plate 3. This physical spatial measurement coupling and contamination causes the distance changes captured by the system under complex working conditions to exhibit drastic and irregular fluctuations, easily creating the physical illusion of the sliding plate 3's thickness rapidly thinning and then instantly recovering. This constitutes the core technical obstacle to achieving high-precision wear condition assessment. Therefore, step S2 of this embodiment aims to completely remove the elastic deformation interference caused by the dynamic loads through dynamic filtering at the algorithm level, restoring the true physical gap of the bearing under stable stress. Wherein: Step S2 includes: S21: Based on the continuously acquired vertical spacing data, a sliding time window with a fixed time length and sliding step size is set along the time axis, and within each sliding time window, a set of vertical spacing data containing multiple discrete sampling points is extracted; S22: For the set of vertical spacing data within the sliding time window, calculate the arithmetic mean of the data corresponding to each measuring point of the vertical displacement sensor 13, and calculate the short-time data variance within the sliding time window based on the arithmetic mean. The calculation formula is as follows: ; In the formula, For the first The vertical displacement sensor 13 in the first Short-term data variance within a time window; This represents the total number of discrete sampling points contained within the sliding window; This is the internal time index for discrete sampling points within the window; For the first The vertical displacement sensor 13 in the first The first window Measured values ​​of instantaneous vertical spacing; For the first The vertical displacement sensor 13 in the first The arithmetic average of the vertical spacing within each window; S23: Based on the pre-stored dynamic load fluctuation threshold, the short-time data variance corresponding to each vertical displacement sensor 13 measuring point is compared with the dynamic load fluctuation threshold one by one; when any short-time data variance is greater than the dynamic load fluctuation threshold, it is determined that the bridge is bearing vehicle dynamic load that causes structural elastic deformation within the current sliding time window, and the entire set of vertical spacing data within the sliding time window is marked as polluted and interfered data and removed. S24: When the variance of the short-time data corresponding to all sensor measurement points is less than or equal to the dynamic load fluctuation threshold, it is determined that the bridge is in a quiet window period under constant self-weight within the current sliding time window. At this time, the arithmetic mean of all measured vertical spacing values ​​within the sliding time window is extracted and output as the effective vertical spacing data representing the quiet period.

[0029] It should be noted that the dynamic load fluctuation threshold is the upper limit of the background noise variance obtained by sampling and calculating the vertical spacing during a historical reference period when the bridge is stationary and has no vehicle traffic.

[0030] Specifically, in the actual service environment of bridge bearings, indirect measurement methods are highly susceptible to both environmental and physical interference: on the one hand, the natural environment of long-span bridges experiences drastic temperature differences, and the microscopic deformation of the bearing steel components due to thermal expansion and contraction is often on the same order of magnitude as the minute wear of the sliding plate 3 itself, causing the measurement data to deviate significantly from the true thickness; on the other hand, the wear debris generated by the sliding plate 3 during long-term friction, as well as externally intruded dust, easily accumulates and compacts at the sliding interface. This third-body elevation effect causes the displacement sensor to detect a false thickening or high-frequency oscillation rebound of the sliding plate 3, which violates physical common sense. The deep coupling of these multi-source physical noises constitutes the fundamental technical bottleneck preventing traditional monitoring methods from obtaining the true wear evolution law. Therefore, the core objective of step S3 in this embodiment is to accurately extract the pure true physical wear value of the sliding plate 3 from the contaminated measurement data through multi-physics field compensation and rigid boundary constraints. Wherein: Step S3 includes: S31: Based on the reference calibration data including the initial reference temperature, the initial vertical spacing readings of each vertical displacement sensor 13, and the initial exposed thickness of the slide plate measured manually, calculate and solidify the inherent vertical distance constant between each vertical displacement sensor 13 and the upper surface of the slide plate 3; at the same time, synchronously acquire the effective vertical spacing data and real-time temperature data at the current moment. S32: Based on the linear expansion coefficient of the steel components affected by the thermal expansion of the bridge bearing, calculate the absolute structural deformation caused by the real-time temperature difference; subtract the absolute structural deformation from the effective vertical spacing data to obtain the theoretical vertical spacing after temperature compensation; further subtract the inherent vertical distance constant from the theoretical vertical spacing to obtain the preliminary exposed thickness of the sliding plate after eliminating dynamic load deformation and temperature interference. S33: Save the measured external thickness of the skateboard to the historical minimum thickness record stack of skateboard 3. Introduce a lower envelope constraint based on the physical property that the wear thickness of skateboard 3 decreases in a strictly monotonically manner. Compare the initial external thickness of the skateboard with the historical minimum thickness record value at the previous moment: If the initial external thickness of the skateboard is greater than or equal to the historical minimum thickness record value, it is determined that there is a false interference caused by the wear debris padding on the current sliding interface. The historical minimum thickness record value is forcibly output as the actual external thickness monitoring value at the current moment, and the historical minimum thickness record value remains unchanged. If the initial external thickness of the skateboard is less than the historical minimum thickness record value, it is determined that the skateboard 3 has undergone substantial physical wear. The initial external thickness of the skateboard is output as the actual external thickness monitoring value at the current moment, and the actual external thickness monitoring value is overwritten and updated in the historical minimum thickness record stack.

[0031] In an optional embodiment, the formula for calculating the initial exposed thickness of the sliding plate is: ; ; In the formula, The first after thermal expansion error correction The theoretical vertical spacing between the locations of the vertical displacement sensors 13; The measured data of effective vertical spacing during the quiet window period; The coefficient of linear expansion of steel components in bridge bearings affected by thermal expansion; This refers to the real-time temperature data of the support at the current moment. To initialize the reference temperature; The effective reference thickness of the steel component is affected by temperature changes and is within the measurement path of the vertical displacement sensor 13; This is the initial calculated value for the exposed thickness of the sliding plate; It is the inherent vertical distance constant between the vertical displacement sensor 13 and the upper surface of the slide plate 3.

[0032] It should be noted that the aforementioned The factory properties of the bridge bearing materials are preset and entered; Known physical constants extracted from bridge bearing design drawings.

[0033] In an optional embodiment, boundary constraints are performed in step S33 using the following formula: ; In the formula, This is the actual external leakage thickness monitoring value at the current moment after removing all interference; This represents the initial exposed thickness of the slide plate at the current moment. The minimum historical thickness recorded from the start of monitoring up to the previous moment.

[0034] Understandably, during use, after processing by the algorithm, a smooth, step-like, monotonically decreasing data sequence that fully conforms to the physical laws of wear can be output, completely solving the technical problem of data bounce distortion caused by the intrusion of external impurities in indirect measurement.

[0035] Specifically, during the long-term service of a bridge, the bridge bearings not only bear the enormous vertical static load transmitted by the main girder, but also experience complex spatial rotation and eccentric compression due to the high-frequency, low-amplitude movement of the main girder, lateral wind loads, and temperature gradient effects. This uneven stress distribution greatly exacerbates the abnormal wear of the sliding plate 3 in certain areas. Relying solely on the thickness data of a single measuring point can easily lead to misjudgments of overall safety while indicating that certain areas have already been worn through. Therefore, step S4 of this embodiment aims to utilize high-fidelity thickness data from multiple measuring points that have undergone deep cleaning and physical constraint processing to construct a two-dimensional evaluation system covering both the overall absolute wear depth and the degree of spatial relative eccentric loading. Furthermore, by capturing the ultimate thickness of the weakest point and the spatial rotational attitude between each measuring point, the system can comprehensively and objectively quantify the actual service health of the sliding plate 3. Among these: Step S4 includes: S41: Simultaneously acquire the actual external leakage thickness monitoring value of the measuring points where the four vertical displacement sensors 13 are located at the current moment, and read the fixed horizontal distance between adjacent vertical displacement sensors 13; S42: Calculate the wear of the outer leakage thickness of the skateboard by taking the minimum value of the actual outer leakage thickness monitoring values ​​of the four measuring points; compare the minimum value with the preset health status threshold range to evaluate the overall wear condition of the skateboard 3; S43: Calculate the uneven pressure rotation of the slide plate 3 based on the actual external leakage thickness monitoring value and horizontal spacing at different measuring points, calculate the vertical rotation angle of the slide plate at adjacent vertical displacement sensors 13, and take the maximum value among the vertical rotation angles of the slide plate at the four measuring points as the characteristic rotation angle for evaluating the uneven pressure condition of the slide plate 3.

[0036] In an optional embodiment, when the minimum value is between two and three millimeters, the wear condition of the slide plate 3 is determined to be good; when the minimum value is between one and two millimeters, the wear condition of the slide plate 3 is determined to be average; when the minimum value is between zero and one millimeter, the wear condition of the slide plate 3 is determined to be poor, requiring inspection or replacement. It should be noted that during use, the initial exposed thickness of the bridge bearing slide plate is generally three millimeters.

[0037] In an optional embodiment, the initial uniform pressure rotation angle of the bridge bearing sliding plate 3 is zero. When the characteristic rotation angle is between zero and the first eccentric load threshold, the uneven pressure condition is determined to be good; when the characteristic rotation angle is between the first eccentric load threshold and the second eccentric load threshold, the uneven pressure condition is determined to be average; when the characteristic rotation angle is between the second eccentric load threshold and the third eccentric load threshold, the uneven pressure condition is determined to be poor. Wherein, the first eccentric load threshold is one divided by the horizontal distance between the corresponding two measuring points, the second eccentric load threshold is two divided by the horizontal distance between the corresponding two measuring points, and the third eccentric load threshold is four divided by the horizontal distance between the corresponding two measuring points.

[0038] In an optional embodiment, the formulas for calculating the vertical rotation angle of the skateboard and the characteristic rotation angle are as follows: ; ; ; ; ; In the formula, Characteristic corner; , , , To be respectively the current time Below, the vertical rotation angle of the slide plate between any two vertical displacement sensors 13; , , , These are the actual external leakage thickness monitoring values ​​at four different measuring points; This represents the horizontal distance between the two measuring points.

[0039] Specifically, most existing bridge bearing sliding plate 3 life prediction methods employ simple linear data fitting methods based on natural time. However, material wear is essentially driven by the work done by relative sliding friction. If the bridge experiences a surge in traffic volume or frequent large displacements of the main girder in a particular month, the wear of the sliding plate 3 will accelerate dramatically; conversely, if the bridge is closed to traffic, the sliding plate 3 will not experience actual wear even over time. Traditional time-thickness linear evaluation models completely disregard this physical essence, leading to severely distorted prediction results under complex and variable actual working conditions, failing to provide reliable early warnings. Therefore, the core objective of step S5 in this embodiment is to reconstruct the driving dimension and mathematical model for life prediction. Wherein: Step S5 includes: S51: Based on the historical sequence of the cumulative sliding stroke data of the bridge bearing in the transverse and longitudinal directions that are continuously calculated and recorded, and the historical sequence of the minimum actual exposed thickness of the sliding plate that is calculated and strictly aligned with the timestamp of the cumulative sliding stroke data, a nonlinear degradation mapping model that conforms to the law of contact mechanical wear is fitted by the nonlinear least squares method. S52: Based on the aforementioned nonlinear degradation mapping model, with the minimum exposed thickness of the slide plate as the safety boundary, calculate the remaining total sliding stroke that the slide plate 3 can withstand before reaching the failure standard; then, combined with the bridge's average daily cumulative sliding stroke rate within the recent sliding window, convert the calculated remaining total sliding stroke into the remaining service life days of the slide plate 3, and update it in real time.

[0040] In an optional embodiment, the nonlinear degradation mapping model is: ; In the formula, This refers to the cumulative sliding stroke between the upper and lower friction pairs of the bridge bearing. For a certain cumulative sliding stroke At that time, the theoretical predicted value of the exposed thickness of the skateboard; The dynamic wear coefficient; It is a non-linear degradation index.

[0041] It should be noted that the dynamic wear coefficient represents the absolute wear rate under the current bridge load, dust and roughness combined environment; the nonlinear degradation index represents the wear acceleration effect caused by the thinning of the slide plate 3 and the change of contact stress, and its value is not less than 1.

[0042] In an optional embodiment, the formulas for calculating the remaining total sliding stroke and the remaining service life in days are as follows: ; ; In the formula, The remaining physical travel distance that skateboard 3 can sustain from its current wear down to the safety limit; Minimum exposed thickness of the preset skateboard; This represents the total actual sliding distance recorded by the system at the current moment. The predicted remaining lifespan of the skateboard in days; This refers to the average daily cumulative sliding travel rate of the bridge within the most recent statistical period.

[0043] It should be noted that the above The average daily cumulative sliding travel rate of the bridge is calculated within the most recent statistical period, such as the past 30 days. This value changes dynamically with the seasons and traffic flow, thus enabling the calculated remaining service life to have a high degree of environmental awareness and working condition self-adaptation capability.

[0044] Example 2 Please refer to Figure 4 This invention provides a bridge bearing sliding plate wear assessment system, comprising: Multi-source heterogeneous data synchronous acquisition module: used to synchronously acquire multi-source heterogeneous data reflecting the service status of bridge bearings through a sensor network deployed in and around the bridge bearings; Dynamic variance filtering module: used to perform dynamic variance filtering on vertical spacing data based on dynamic load characteristics, eliminate measurement interference caused by main beam deformation, and extract effective vertical spacing data within the structural quiet window period; Structural thermal expansion compensation and physical constraint denoising module: used to perform structural thermal expansion compensation on the effective vertical spacing data by combining real-time temperature data, and introduce the lower envelope algorithm based on monotonically decreasing physical constraints to denoise the data, and obtain the real external thickness monitoring value of the slide plate 3 after removing environmental and wear debris interference. The slide plate wear condition and uneven pressure condition assessment module is used to assess the current wear condition and uneven pressure condition of the bridge bearing slide plate 3 based on the actual external thickness monitoring value. The remaining service life prediction module for the sliding plate is used to construct a nonlinear degradation mapping model with the cumulative sliding stroke as the driving variable based on the cumulative sliding stroke data and the actual external leakage thickness monitoring value. It obtains the dynamic wear coefficient reflecting the current service conditions through adaptive fitting and predicts the remaining service life of the sliding plate 3 in combination with the preset safety failure threshold of the sliding plate 3.

[0045] Other technical features are the same as in Embodiment 1 and can achieve the same technical effects, so they will not be described in detail here.

[0046] Example 3 This invention also provides a computer-readable storage medium storing a computer program that is executed by a processor to implement the various steps in the evaluation method described above. For details, please refer to the implementation methods provided for the various steps described above, which will not be repeated here.

[0047] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0048] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0049] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0050] In this invention, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit it; those skilled in the art will readily understand that the above descriptions are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for assessing the wear of bridge bearing sliding plates, characterized in that, Includes the following steps: S1: Through a sensor network deployed on and around the bridge bearings, multi-source heterogeneous data reflecting the service status of the bridge bearings are collected synchronously. S2: Perform dynamic variance filtering on the vertical spacing data based on dynamic load characteristics to eliminate measurement interference caused by the deformation of the main beam and extract the effective vertical spacing data within the structural quiet window period; S3: Combine real-time temperature data to perform structural thermal expansion compensation on the effective vertical spacing data, and introduce a lower envelope algorithm based on monotonically decreasing physical constraints to denoise the data and obtain the true external thickness monitoring value of the slide plate (3) after removing environmental and wear debris interference. S4: Based on the actual external thickness monitoring value, evaluate the current wear state and uneven pressure condition of the bridge bearing slide plate (3); S5: Based on the cumulative sliding stroke data and the actual external leakage thickness monitoring value, a nonlinear degradation mapping model with the cumulative sliding stroke as the driving variable is constructed. The dynamic wear coefficient reflecting the current service conditions is obtained through adaptive fitting, and the remaining service life of the slide plate (3) is predicted by combining the preset safety failure threshold of the slide plate (3).

2. The evaluation method according to claim 1, characterized in that, Step S1 includes: S11: Four high-precision vertical displacement sensors (13) are evenly distributed in a rectangle and vertically fixed on the upper spherical crown liner (4) of the bridge bearing; the detection end of the vertical displacement sensor (13) is vertically oriented towards the bottom surface of the corresponding upper bearing plate assembly (1) so as to read and record the vertical distance between each vertical displacement sensor (13) and the corresponding upper bearing plate assembly (1) in real time. S12: Surface temperature sensors (14) are attached to the exposed surfaces of the upper bearing plate assembly (1) and the lower base plate assembly (12) respectively to obtain real-time temperature data of the bridge bearing. S13: Horizontal displacement sensors (15) are externally installed between the upper bearing plate assembly (1) in the longitudinal and transverse directions and the lower bearing structure, respectively, to monitor the instantaneous horizontal relative displacement of the bridge bearing and record the cumulative sliding stroke between the upper and lower friction pairs of the bridge bearing. S14: Using the time axis with the highest sampling frequency as a reference, assign a global timestamp to the vertical spacing data, the temperature data, and the cumulative sliding stroke data through the Global Positioning System or Network Time Protocol.

3. The evaluation method according to claim 2, characterized in that, The cumulative sliding stroke is calculated as follows: ； In the formula, To accumulate sliding travel; This is the latest discrete data sampling point sequence number; , For the support in the first Absolute displacement coordinates in the longitudinal and transverse directions at each sampling time; , These are the absolute displacement coordinates in the longitudinal and transverse directions at the previous sampling time.

4. The evaluation method according to claim 3, characterized in that, Step S2 includes: S21: Based on the continuously acquired vertical spacing data, a sliding time window with a fixed time length and sliding step size is set along the time axis, and within each sliding time window, a set of vertical spacing data containing multiple discrete sampling points is extracted; S22: For the set of vertical spacing data within the sliding time window, calculate the arithmetic mean of the data corresponding to each vertical displacement sensor (13) measuring point, and calculate the short-time data variance within the sliding time window based on the arithmetic mean. The calculation formula is as follows: ； In the formula, For the first The vertical displacement sensor (13) in the first Short-term data variance within a time window; This represents the total number of discrete sampling points contained within the sliding window; This is the internal time index for discrete sampling points within the window; For the first The vertical displacement sensor (13) in the first The first window Measured values ​​of instantaneous vertical spacing; For the first The vertical displacement sensor (13) in the first The arithmetic average of the vertical spacing within each window; S23: Based on the pre-stored dynamic load fluctuation threshold, the short-time data variance corresponding to each vertical displacement sensor (13) measuring point is compared with the dynamic load fluctuation threshold one by one; when any short-time data variance is greater than the dynamic load fluctuation threshold, it is determined that the bridge is bearing vehicle dynamic load that causes structural elastic deformation within the current sliding time window, and the entire set of vertical spacing data within the sliding time window is marked as polluted interference data and removed. S24: When the variance of the short-time data corresponding to all sensor measurement points is less than or equal to the dynamic load fluctuation threshold, it is determined that the bridge is in a quiet window period under constant self-weight within the current sliding time window. At this time, the arithmetic mean of all measured vertical spacing values ​​within the sliding time window is extracted and output as the effective vertical spacing data representing the quiet period.

5. The evaluation method according to claim 4, characterized in that, Step S3 includes: S31: Based on the reference calibration data including the initial reference temperature, the initial vertical spacing readings of each vertical displacement sensor (13) and the initial exposed thickness of the slide plate measured manually, calculate and solidify the inherent vertical distance constant between each vertical displacement sensor (13) and the upper surface of the slide plate (3); at the same time, synchronously acquire the effective vertical spacing data and real-time temperature data at the current moment; S32: Based on the linear expansion coefficient of the steel components affected by the thermal expansion of the bridge bearing, calculate the absolute structural deformation caused by the real-time temperature difference; subtract the absolute structural deformation from the effective vertical spacing data to obtain the theoretical vertical spacing after temperature compensation; further subtract the inherent vertical distance constant from the theoretical vertical spacing to obtain the preliminary exposed thickness of the sliding plate after eliminating dynamic load deformation and temperature interference. S33: Save the measured external thickness of the skateboard to the historical minimum thickness record stack of the skateboard (3). Introduce the lower envelope constraint based on the physical property that the wear thickness of the skateboard (3) decreases in a strictly monotonically manner. Compare the initial external thickness of the skateboard with the historical minimum thickness record value of the previous moment. If the initial external thickness of the skateboard is greater than or equal to the historical minimum thickness record value, it is determined that there is a false interference caused by the wear debris padding on the current sliding interface. The historical minimum thickness record value is forcibly output as the real external thickness monitoring value at the current moment, and the historical minimum thickness record value remains unchanged. If the initial external thickness of the skateboard is less than the historical minimum thickness record value, it is determined that the skateboard (3) has undergone substantial physical wear. The initial external thickness of the skateboard is output as the real external thickness monitoring value at the current moment, and the real external thickness monitoring value is overwritten and updated to the historical minimum thickness record stack.

6. The evaluation method according to claim 5, characterized in that, Step S4 includes: S41: Simultaneously acquire the actual external leakage thickness monitoring value of the measuring points where the four vertical displacement sensors (13) are located at the current moment, and read the fixed horizontal distance between adjacent vertical displacement sensors (13); S42: Take the minimum value of the actual external leakage thickness monitoring value of the four measuring points to calculate the external leakage thickness wear of the skateboard; compare the minimum value with the preset health status threshold range to evaluate the overall wear status of the skateboard (3); S43: Calculate the uneven pressure rotation of the slide plate (3) based on the actual external leakage thickness monitoring value and horizontal spacing of different measuring points, calculate the vertical rotation angle of the slide plate at adjacent vertical displacement sensors (13), and take the maximum value of the vertical rotation angle of the slide plate at the four measuring points as the characteristic rotation angle for evaluating the uneven pressure condition of the slide plate (3).

7. The evaluation method according to claim 6, characterized in that, Step S5 includes: S51: Based on the historical sequence of the cumulative sliding travel data of the bridge bearing in the transverse and longitudinal directions, which is continuously calculated and recorded, and the historical sequence of the minimum actual exposed thickness of the sliding plate that is strictly aligned with the timestamp of the cumulative sliding travel data, a nonlinear degradation mapping model conforming to the contact mechanical wear law is fitted using the nonlinear least squares method; the nonlinear degradation mapping model is as follows: ； In the formula, This refers to the cumulative sliding stroke between the upper and lower friction pairs of the bridge bearing. For a certain cumulative sliding stroke At that time, the theoretical predicted value of the exposed thickness of the skateboard; The dynamic wear coefficient; It is a non-linear degradation index; S52: Based on the nonlinear degradation mapping model, with the minimum exposed thickness of the skateboard as the safety boundary, calculate the remaining total sliding stroke that the skateboard (3) can withstand before reaching the failure standard; then, combined with the average daily cumulative sliding stroke rate of the bridge in the recent sliding window, convert the calculated remaining total sliding stroke into the remaining service life days of the skateboard (3) and update it in real time.

8. The evaluation method according to claim 7, characterized in that, The formulas for calculating the remaining total sliding stroke and the remaining service life in days are as follows: ； ； In the formula, The remaining physical travel of the skateboard (3) from its current state to the safety limit; Minimum exposed thickness of the preset skateboard; This represents the total actual sliding distance recorded by the system at the current moment. The predicted remaining lifespan of the skateboard in days; This refers to the average daily cumulative sliding travel rate of the bridge within the most recent statistical period.

9. A bridge bearing sliding plate wear assessment system, applied to the assessment method according to any one of claims 1-8, characterized in that, include: Multi-source heterogeneous data synchronous acquisition module: used to synchronously acquire multi-source heterogeneous data reflecting the service status of bridge bearings through a sensor network deployed in and around the bridge bearings; Dynamic variance filtering module: used to perform dynamic variance filtering on vertical spacing data based on dynamic load characteristics, eliminate measurement interference caused by main beam deformation, and extract effective vertical spacing data within the structural quiet window period; Structural thermal expansion compensation and physical constraint denoising module: used to combine real-time temperature data to perform structural thermal expansion compensation on the effective vertical spacing data, and introduce the lower envelope algorithm based on monotonically decreasing physical constraints to denoise the data, and obtain the true external thickness monitoring value of the slide plate (3) after removing environmental and wear debris interference; The slide plate wear condition and uneven pressure condition assessment module is used to assess the current wear condition and uneven pressure condition of the bridge bearing slide plate (3) based on the actual external thickness monitoring value. The remaining service life prediction module of the slide plate is used to construct a nonlinear degradation mapping model with the cumulative sliding stroke as the driving variable based on the cumulative sliding stroke data and the actual external leakage thickness monitoring value. It obtains the dynamic wear coefficient reflecting the current service conditions through adaptive fitting and predicts the remaining service life of the slide plate (3) in combination with the preset safety failure threshold of the slide plate (3).

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program that, when executed by a processor, controls the device containing the storage medium to perform the evaluation method as described in any one of claims 1-8.

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