A method for grading intelligent reinforcement optimization of an electric dust collector

Abstract

The present application relates to a kind of electric dust collector hierarchical intelligent reinforcement optimization method, belong to electric dust collector technical field, comprising: the construction of full structure multi-source heterogeneous data standardization database;Establish load-stress-corrosion-negative pressure four-dimensional coupling feature matrix;Through multiscale grey correlation analysis, the health attenuation slope of component is solved and hierarchical threshold is self-adaptively corrected;According to defect grade and attenuation slope, hierarchical reinforcement scheme is generated and construction is implemented;In three limit checking conditions, measured stress margin coefficient is closed-loop iterative optimized;And based on long-term monitoring, preventive reinforcement plan is generated.The present application realizes the change from experience extensive reinforcement to data quantification accurate reinforcement, can reduce steel consumption by more than 27%, shorten construction period, eliminate structure collapse hidden danger, reduce operation and maintenance cost, suitable for all kinds of thermal power unit electric dust collector full structure reinforcement reconstruction.

Description

Technical Field

[0001] This invention belongs to the field of electrostatic precipitator technology, specifically relating to a graded intelligent reinforcement and optimization method for electrostatic precipitators. Background Technology

[0002] Currently, the structural reinforcement of electrostatic precipitators in the thermal power industry generally adopts the traditional modification model of "manual visual inspection + fixed drawing reference + experience-based selection and reinforcement". Taking the DC2.3-216 electrostatic precipitator of Boiler No. 1 in a certain plant as an example, the actual appraisal conclusion shows that the equipment reliability level is level three, the stress between the columns of the support spans reaches 1.05~1.136, which exceeds the standard limit, the bottom beam has a large area of ​​missing welds and missing ribs, the multi-layer wall panels and internal and external stiffening ribs of the ash hopper are seriously insufficient in strength under the combined action of 1.5 times the full load ash load and 4.903kPa negative pressure, many wall panels of the shell are rusted through, and the steel beam of the ash unloading platform is damaged due to the opening. At the same time, due to the normalization of high ash content and low quality coal blending, the actual ash load fluctuation range is far greater than the original design conditions, further aggravating the structural safety risks of the equipment.

[0003] Analysis reveals the following inherent flaws in existing traditional reinforcement technologies:

[0004] Data sources are fragmented and there is no standardized full life cycle modeling: the original reinforcement relies only on static data from a single test, which breaks the correlation between design, operation and maintenance, coal quality and real-time working conditions. It cannot quantify the additional stress increment brought about by dynamic ash accumulation, corrosion and negative pressure coupling. The selection of reinforcement sections relies on the experience of designers, and there are generally two extremes: over-reinforcement with redundant materials or insufficient reinforcement that still does not meet the 1.2 / 1.5 times load specification.

[0005] Single-parameter judgment of defects lacks a four-dimensional coupled quantitative judgment mechanism: Conventional solutions only determine the defect level by visual deformation and local wall thickness measurement, which cannot distinguish whether "stress exceedance is caused by a single or coupled factor such as ash accumulation overload / flue gas negative pressure / section corrosion". The accuracy of graded reinforcement is poor and the reinforcement solutions for similar defects are seriously homogenized.

[0006] Static assessment of deterioration trends, lack of multi-scale time-series prediction: Current reinforcement is post-fault repair, which cannot fit the long-term deterioration and decay pattern of components based on historical operating data, and cannot predict the risk of structural failure in the next 3-5 years in advance, resulting in frequent problems of short-term re-deterioration and secondary reinforcement after modification.

[0007] Static spot checks during reinforcement acceptance lack closed-loop verification and iteration under extreme working conditions: Traditional completion acceptance only visually inspects welds and dimensions, without simulating the actual stress of three harsh working conditions: 1.2 / 1.5 times full load and extreme negative pressure. Hidden reinforcement defects (incomplete welds in internal weld legs, eccentric stress of stiffening) cannot be detected, leaving potential safety hazards of collapse.

[0008] Fixed and rigid thresholds cannot adapt to dynamic load changes caused by coal quality fluctuations: fixed grading thresholds cannot be adaptively adjusted with annual coal ash content fluctuations and equipment corrosion and aging; low ash conditions have large reinforcement redundancy; and high ash blending conditions have insufficient reinforcement margin. Summary of the Invention

[0009] The purpose of this invention is to overcome the problems existing in the prior art and to provide a graded intelligent reinforcement and optimization method for electrostatic precipitators.

[0010] The technical solution of the present invention is as follows:

[0011] A graded intelligent reinforcement and optimization method for electrostatic precipitators includes the following steps:

[0012] S1: Obtain the static inherent parameters, historical time-series data and real-time dynamic data of the entire structure of the electrostatic precipitator, establish a time-series index according to the unique component code, and build a standardized database for the entire life cycle.

[0013] S2: Based on the aforementioned standardized database for the entire life cycle, four types of characteristic quantities are extracted: ash accumulation load, equivalent stress, cross-sectional corrosion thinning rate, and negative pressure inside the shell. Multi-field coupling correction coefficients are introduced to calculate the state deviation coefficient, and a four-dimensional coupling characteristic matrix of load-stress-corrosion-negative pressure is constructed.

[0014] S3: The four-dimensional coupling feature matrix is ​​split into multiple time windows. An improved grey relational algorithm is used to calculate the correlation between the state deviation coefficient under each time window and the degradation feature curve of the historical failure sample in the full life cycle standardized database. After weighted fusion, the health decay slope of each component is fitted and updated.

[0015] S4: Based on the health decay slope and dynamic grading threshold, determine the component defect level, generate and output a graded reinforcement scheme;

[0016] S5: Implement reinforcement construction on the components according to the graded reinforcement scheme;

[0017] S6: Simulate the extreme verification conditions and conduct on-site measurements to calculate the stress surplus coefficient at each measuring point. Iterate and optimize the reinforcement parameters based on the stress surplus coefficient until all measuring points meet the preset specification requirements.

[0018] Furthermore, in step S2, the multi-field coupling correction coefficient includes a load additional coefficient, a corrosion reduction coefficient, and a negative pressure additional coefficient. The multi-field coupling correction coefficient is combined with structural mechanics theory to construct a coupling calculation formula, generate comprehensive characteristic values ​​for each measuring point, collect the reference stress values ​​for each measuring point under no-load, clean, and dust-free working conditions, and compare the comprehensive characteristic values ​​with the reference stress values ​​to obtain the state deviation coefficient. The four-dimensional coupling characteristic matrix is ​​constructed based on the state deviation coefficient.

[0019] Furthermore, the four-dimensional coupling feature matrix is ​​filled with data row by row, with each effective sampling sequence of the day as the row and the state deviation coefficients corresponding to the four types of feature quantities, namely, ash load, equivalent stress, cross-sectional corrosion thinning rate and negative pressure inside the shell, as the columns, and finally forms an Mn×4 four-dimensional coupling feature matrix, where n is the number of effective samplings on the day.

[0020] Furthermore, in step S3, the multi-level time window includes a daily window, a monthly window, and a quarterly window; the weighted fusion uses fixed weights for fusion, and fits a univariate linear degradation equation with the cumulative number of days of operation as the independent variable and the comprehensive correlation degree as the dependent variable to solve for the health decay slope.

[0021] Furthermore, in step S4, the method for adjusting the dynamic grading threshold is as follows: retrieve the historical stress sample datasets of normal, exceeding standard, and failure from the full life cycle standardized database, construct the three types of stress probability distribution models respectively, and dynamically correct the stress grading thresholds corresponding to each level of defect with preset false alarm rate and detection rate as constraints.

[0022] Furthermore, the defect levels include Level 1 mild defects, Level 2 moderate defects, and Level 3 severe defects; each level of defect corresponds to a different stress grading threshold range and is matched with different foundation reinforcement schemes; the health attenuation slope is used to correct the reinforcement safety margin of the foundation reinforcement scheme.

[0023] Furthermore, in step S6, the extreme verification conditions include the support full ash load condition, the ash hopper full ash load condition, and the extreme negative pressure condition; the stress surplus coefficient is calculated by dividing the difference between the allowable stress of the component and the measured stress under the extreme conditions by the allowable stress.

[0024] Furthermore, in step S6, the iterative optimization specifically involves: when the stress surplus coefficient is less than the preset lower limit, enlarging the reinforcing section or adding auxiliary stiffening components and re-verifying; when the stress surplus coefficient is greater than the preset upper limit, reducing the amount of material used in subsequent reinforcement of similar components until the stress surplus coefficient of all measuring points falls within the preset reasonable range.

[0025] Furthermore, the method also includes step S7: based on the continuously updated four-dimensional coupling feature matrix and the health decay slope, predict the remaining safe service life of each component and generate a periodic preventive reinforcement list.

[0026] Furthermore, in step S3, the degradation characteristic curve of the historical failure sample is obtained by fitting historical monitoring data of the same type of component retrieved from the full life cycle standardized database.

[0027] Compared with the prior art, the beneficial effects of the present invention are:

[0028] This invention achieves accurate calculation of reinforcement margins through standardized modeling of multi-source heterogeneous data and quantitative analysis of four-dimensional coupled feature matrices, avoiding over-reinforcement caused by traditional experience-based selection. Actual engineering verification shows that the overall steel consumption of the project is reduced by approximately 27.3% compared to traditional experience-based reinforcement methods, saving nearly 32 tons of main materials and directly reducing material costs by approximately 216,000 yuan.

[0029] This invention utilizes a four-dimensional coupling characteristic matrix of load, stress, corrosion, and negative pressure to comprehensively quantify the additional stress increment under multi-field coupling effects. Combined with closed-loop stress verification under three extreme working conditions, it ensures that all components simultaneously meet the mandatory design specifications of 1.2 times the support ash load, 1.5 times the ash hopper load, and 4.903 kPa ultimate negative pressure. Engineering verification has improved the structural reliability level of the electrostatic precipitator from level three to level one, fundamentally eliminating the safety hazard of collapse.

[0030] This invention employs an intelligent hierarchical reinforcement scheme based on the synergy of defect level and attenuation slope, precisely locating defect points and corresponding reinforcement measures within each structural zone, effectively reducing unnecessary maintenance work. Engineering verification has shown that unnecessary maintenance work is reduced by approximately 42%, and the overall reinforcement construction cycle is shortened from the traditional estimated 45 days to 35 days.

[0031] This invention utilizes a multi-scale grey relational degradation analysis model to continuously calculate the health degradation slope of each component and predict the remaining safe service life. It generates a preventative reinforcement list before defects even emerge, shifting from "post-failure emergency repair" to "pre-emptive minor reinforcement." Calculations indicate that this can effectively avoid a large-scale overall overhaul within the next five years, saving at least 1.6 million yuan in major repair costs.

[0032] The algorithm framework of this invention is not dependent on a specific unit model and can be applied to the complete structural transformation project of dry or horizontal electrostatic precipitators for units with different capacity levels from 300MW to 1000MW. It can be implemented simply by replacing the corresponding design benchmark parameters. It has strong industrial replicability and broad prospects for promotion and application. Detailed Implementation

[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] This invention provides a graded intelligent reinforcement and optimization method for electrostatic precipitators, comprising the following steps:

[0035] Step S1: Standardization and import of multi-source heterogeneous data into the database and construction of the full-component model topology.

[0036] S1-1: Data source classification and collection.

[0037] This step involves collecting data from three types of data sources:

[0038] ① Static inherent parameters: original factory design drawings, component material parameters, original cross-sectional dimensions, and factory load design values;

[0039] ②Historical time series data: maintenance records over the years, reliability assessment reports, monthly coal ash analysis logs, and past partial renovation records;

[0040] ③ Real-time dynamic data: Strain gauge stress sensors, negative pressure transmitters, and continuous level gauges for ash hoppers are installed in key supports of the support frame, internal and external reinforcement of ash hoppers (layers 1-9), bottom beam segments, and pressure-bearing areas of the shell. Real-time data is collected synchronously at a sampling cycle of 15 minutes.

[0041] S1-2: Data preprocessing and standardized data entry.

[0042] The 3σ criterion is used to remove abnormal sensor jump values, and missing time series data is filled in by linear interpolation. The physical dimensions are unified, and the unique component code is used as the primary key to divide the data into four major topological partitions: support subdomain, ash hopper subdomain, bottom beam subdomain, and shell subdomain, thus establishing a standardized database for the entire life cycle.

[0043] The database archives maintenance data, failure samples, and historical stress data from previous years. It can directly retrieve standard degradation characteristic curves and stress samples of three categories (normal, exceeding standard, and failure) for similar components, serving as a benchmark for grey relational analysis and probability distribution modeling. By identifying the defect level and health decay slope of the corresponding measuring point through component coding, the database can retrieve the component's historical defects and past reinforcement records, avoiding redundant reinforcement and blind strengthening.

[0044] S1-3: Reference stress calibration.

[0045] Under no-load, clean, and dust-free operating conditions, the reference stress values ​​at each measuring point are collected as the benchmark parameters for subsequent deviation calculations. The measuring points are determined based on the structural stress characteristics of the four major load-bearing systems of the electrostatic precipitator: steel support, bottom beam, ash hopper, and shell. Sensors are deployed at key load-bearing, pressure-bearing, and easily failed locations in each span, segment, and layer. The measuring points are located by combining component numbers and topological partitioning, and the reference stress is collected accordingly.

[0046] Step S2: Construct a four-dimensional coupling feature matrix and calculate the real-time state deviation coefficient.

[0047] S2-1: Normalize four types of characteristic quantities: ash load, measured equivalent stress, corrosion section thinning rate, and real-time shell internal negative pressure, and map them to the [0,1] interval using the min-max normalization algorithm.

[0048] S2-2: Introducing a multi-field coupling correction coefficient , , (corresponding to load additional coefficient, corrosion reduction coefficient, and negative pressure additional coefficient respectively), and constructing a coupled calculation formula based on structural mechanics theory to generate comprehensive characteristic values ​​for each measuring point.

[0049] S2-3: Compare the comprehensive characteristic value with the benchmark stress value obtained in S1-3, and calculate the state deviation coefficient to characterize the degree of operational deviation.

[0050] S2-4: Arranged according to the sampling time sequence of the day, construct a four-dimensional coupled feature matrix of load-stress-corrosion-negative pressure based on the state deviation coefficients corresponding to the four types of features of each measuring point.

[0051] Mn×4, where n is the total number of valid sampled time series for the day; the matrix construction logic is as follows:

[0052] 1) Define the matrix rows and columns: The matrix has n rows, with each group of synchronous sampling times on the same day as a row; the four columns are fixed with the state deviation coefficients calculated for each of the four characteristics: ash accumulation load, equivalent stress, cross-sectional corrosion thinning rate, and negative pressure inside the shell.

[0053] 2) Filling matrix elements time by time: For each set of real-time data collected synchronously over 15 minutes, retrieve the four state deviation coefficients obtained after normalization, multi-field coupling correction, and benchmark stress comparison of the four types of features, and fill them into the matrix row corresponding to the current time series in column order;

[0054] 3) Time sequence sorting and shaping: All valid sampling time sequences of the day are entered line by line according to the order of equipment operation time, and finally a time sequenced four-dimensional coupled feature moment Mn×4 is formed with the state deviation coefficient as the element and carrying the equipment time sequence deterioration information.

[0055] The specific data sources for the four types of characteristic quantities are as follows: the ash load is calculated from parameters such as ash hopper level, ash density, and cavity volume in the database, and the real-time ash level data comes from the on-site ash level gauge; the equivalent stress and negative pressure inside the shell are taken from the real-time monitoring data collected by the sensor at a 15-minute cycle and stored in the database; the corrosion section thinning rate is calculated by combining the original cross-sectional dimensions of the component and the records of previous wall thickness tests archived in the database.

[0056] Step S3: Multi-scale grey relational analysis to solve for the self-tuning of the component health degradation slope and dynamic grading threshold.

[0057] S3-1: Divide the analysis into three windows: short period (7-day window), medium period (30-day window), and long period (90-day window), and decompose the four-dimensional coupled feature matrix by scale.

[0058] S3-2: Retrieve the standard degradation characteristic curves of historical failure samples from the same unit / model of dust collector, and calculate the correlation between the state deviation coefficient and the failure sample under three analysis windows using an improved Deng's grey relational algorithm. , , .

[0059] Historical degradation characteristic curves are obtained by taking monitoring, maintenance and failure case data of the same type of equipment from the database over the years, and then fitting them according to daily, monthly and quarterly scales.

[0060] S3-3: Multi-scale correlation degree weighted fusion and fitting of health deterioration slope: Based on the daily updated four-dimensional coupling feature matrix, the correlation degree of the three types of operating states of the day in the short term, medium term and long term is calculated; the daily comprehensive health correlation degree Y is obtained by weighted fusion using fixed weights (0.4 for short term, 0.35 for medium term and 0.25 for long term).

[0061] Throughout the process, multiple time units were discarded, and the daily time series T was used as the sole independent variable, where T represents the cumulative number of days of operation. The comprehensive correlation degree Y corresponding to each day was used as the dependent variable. (T,Y) sample data were collected over several consecutive days, and a univariate linear degradation equation Y=kT+b was fitted.

[0062] In the formula: b is the baseline correlation degree of the initial state of the component; the slope k is the health decay slope, which characterizes the rate of decay of the overall health state of the component with the number of days. It has uniform dimensions and can be used to compare the deterioration rate of components at all measuring points.

[0063] S3-4: Retrieve historical stress sample datasets of three types: normal, exceeding standard, and failure, and construct three stress probability distribution models respectively. With the preset false alarm rate α≤5% and detection rate β≥90% as constraints, adaptively and dynamically correct the stress classification thresholds of level I / II / III defects to avoid defects with poor adaptability to fixed threshold working conditions.

[0064] Among them, the three types of stress sample datasets, namely normal, exceeding the standard, and failure, were all extracted from the standardized database of the entire life cycle of electrostatic precipitators established in the early stage. They were classified and screened according to component partitions and measuring point numbers. Stress data corresponding to the time periods of stable equipment operation, stress exceeding the limit without failure, and structural failure were extracted respectively. After removing abnormal data, they were sorted into three independent sample sets.

[0065] Statistical analysis was conducted on the three types of samples to obtain their respective stress probability distribution models. The false alarm rate was no higher than 5% and the detection rate was no lower than 90% as constraints. The critical stress value was repeatedly checked in combination with the distribution model, and the stress classification thresholds corresponding to the first, second and third level defects were dynamically adjusted to achieve adaptive updating of the thresholds.

[0066] Step S4: Based on the defect level and attenuation slope, intelligently classify and generate reinforcement parameter schemes.

[0067] S4-1: Determine the first / second / third level defects of the component based on the comparison between the real-time stress σ and the dynamic grading threshold.

[0068] Level 1 (light) (1.00 < σ ≤ 1.05): The defects are mostly local incomplete welding, missing gaskets, and slight corrosion. Reinforcement solutions: repair welding, replacement of fasteners, and local anti-corrosion treatment.

[0069] Level II, moderate (1.05 < σ ≤ 1.13): stiffening ribs bulge outwards, local sections are weak. Reinforcement plan: add additional channel steel / flat steel for reinforcement, and optimize the rib plate arrangement spacing.

[0070] Level 3 severe (σ>1.13): wall panel tearing, column corrosion and perforation, support breakage. Reinforcement plan: add built-in pipe supports, large area of ​​panel reinforcement, and replace damaged steel.

[0071] It should be noted that the above defect judgment range is the initial baseline threshold range of the system and is only used as an initial judgment standard. Combined with the stress probability distribution model constructed in step S3, under the constraints of false alarm rate ≤5% and detection rate ≥90%, the system will continuously calculate and update the upper and lower limits of the range according to the equipment operating conditions, component aging degree, and environmental changes. Therefore, the classification boundary is not permanently fixed.

[0072] When conducting defect assessment, the initial values ​​are no longer used. Instead, a dynamically updated threshold range is used to classify the defects. The initial values ​​are only used for system initialization, and the adaptively corrected thresholds are used throughout the process to achieve dynamic classification.

[0073] S4-2: Combine the health decay slope k to correct the reinforcement margin: the larger k is, the faster the degradation, and the safety margin of the reinforcement section is increased accordingly.

[0074] S4-3: Output a list of structural reinforcement materials, including specifications of reinforcing steel, spacing, welding process, material consumption, and construction procedures.

[0075] In this process, step S4-1 determines the defect level of the component based on the dynamic stress threshold and matches the foundation reinforcement scheme; step S4-2 determines the deterioration rate by combining the health decay slope and dynamically adjusts the reinforcement safety margin. Together, these two steps complete the fine setting of reinforcement parameters; step S4-3 integrates the defect level, reinforcement margin and other information obtained from the first two steps and summarizes them into a complete structured reinforcement list that includes material specifications, construction requirements and material consumption. This is the practical output of the analysis results from the first two steps.

[0076] Step S5: Implement reinforcement construction on-site in different grades and zones, and simultaneously maintain a construction process log.

[0077] Construction was carried out in zones according to the structural reinforcement list, based on the unit shutdown window. Actual measurements of wall thickness, welds, and dimensions of the same component were recorded before and after reinforcement. On-site work was conducted strictly according to the steel specifications, layout, welding processes, and construction procedures in the list, with work carried out in different zones and at different levels. Simultaneously, the measured data and construction details before and after reinforcement were recorded in a logbook for future verification and traceability.

[0078] Step S6: Dual closed-loop simulation of extreme conditions + field measurement, to enhance parameter iterative optimization.

[0079] S601: Simulates three major verification conditions: ① Support 1.2 times full ash load; ② Ash hopper 1.5 times full ash load; ③ 4.903kPa ultimate negative pressure, with measured stress collected at all measuring points.

[0080] The operating condition simulation consists of two parts: first, finite element simulation, which uses a full structural model of the dust collector to apply three types of loads: 1.2 times the full load of the support, 1.5 times the full load of the ash hopper, and 4.903 kPa ultimate negative pressure, to calculate the overall theoretical stress and the theoretical stress at each measuring point; second, on-site actual operating condition simulation, which reproduces the above three ultimate operating conditions on the equipment through ash accumulation counterweights and negative pressure adjustment devices, and uses deployed sensors to collect the actual stress values ​​at each measuring point. The stress surplus coefficient is calculated by combining the simulation results with the on-site measured data, and then the reinforcement parameters are iteratively adjusted according to the coefficient range until all measuring points meet the standards.

[0081] S602: Calculate the measured stress surplus factor .in, This refers to the allowable stress of the component, i.e., the maximum allowable stress value required by the specification. This represents the actual working stress of the component obtained from on-site measurements under extreme working conditions. It is the stress margin factor; the larger the value, the more sufficient the structural safety margin.

[0082] when <0: Insufficient reinforcement. The reinforcement section is enlarged by the correction factor / auxiliary stiffening is added, and iterative optimization is performed. The correction factor is calculated based on the stress surplus factor gap and is specifically used to enlarge or reduce the cross-sectional specifications of the reinforcement steel.

[0083] The calculation process for the reinforcement and amplification correction factor includes the following:

[0084] Stress surplus coefficient formula: ;

[0085] when Strength gap value ;

[0086] Set the lower limit of the baseline safety margin Cross-section magnification correction factor Calculation formula:

[0087] In the formula: Cross-section magnification correction factor, always greater than 1;

[0088] The absolute value of the stress surplus coefficient represents the current strength deficit.

[0089] The minimum safety margin requirement is specified in the standard.

[0090] Engineering application: Multiply the original reinforcing member's cross-sectional dimensions, plate thickness, and steel section area by... New reinforcement specifications are obtained; if the verification after enlarging the cross-section still does not meet the requirements, horizontal and vertical auxiliary stiffening ribs and supporting components are added simultaneously.

[0091] Example: Actual test under extreme working conditions , ;

[0092] The original 8mm reinforcing steel plate was replaced with 8×2.2=17.6mm, and 18mm steel plate was selected to carry out extreme working condition test verification again.

[0093] when >0.15: Reinforce redundancy, and optimize the use of materials for subsequent similar components. If the reinforcement exceeds the preset reasonable margin limit, it is judged as reinforcement redundancy. At this time, although the current component reinforcement meets the standard, the material consumption is too high. When encountering similar defective components in the future, we will actively reduce the reinforcement section, steel usage, or adjust the stiffening arrangement to reduce material consumption while meeting the specifications.

[0094] The calculation process for the reinforcement reduction correction factor includes the following:

[0095] Preset reasonable margin upper limit surplus margin ;

[0096] Section reduction correction factor Calculation formula: ;

[0097] In the formula:

[0098] : Cross-section reduction correction factor, with a value of 0~1;

[0099] Excessive stress margin beyond the reasonable allowance.

[0100] Engineering Application: The reinforcement scheme for this component remains unchanged. When designing similar components with the same defect level and working conditions in the future, the standard reinforcement section size will be multiplied by [the specified value]. Reduce steel consumption while meeting safety standards.

[0101] Example: Actual measurement ,

[0102] The thickness of the reinforcing plate for subsequent similar components can be 0.67 times that of the original specification, reducing material consumption.

[0103] Standard range determination

[0104] when No correction factor is needed; the current reinforcement parameters are finalized, and acceptance can proceed directly.

[0105] Iterative optimization: After completing the cross-section enlargement and reinforcement modifications such as adding auxiliary stiffeners, the three extreme working conditions were reproduced again in S601 and the stress was measured. The redundancy factor was then recalculated. Repeatedly adjust and verify the reinforcement parameters until all measuring points are covered. If the data is within a reasonable range, the previous data modeling steps will not be repeated.

[0106] 0≤ ≤Reasonable Margin: Reinforcement meets standards, and the plan is finalized. This reasonable margin is a fixed range defined by combining thermal power industry specifications, equipment standards, and operational experience with electrostatic precipitators, generally between 0.05 and 0.15. Once the standard is met, the current reinforcement parameters and construction plan are officially finalized and will not be adjusted further.

[0107] S603: Iterate until all measurement points meet the specification indicators, and complete the closed-loop acceptance.

[0108] Step S7: Long-term monitoring and prediction to generate periodic preventive reinforcement plans.

[0109] Based on the continuously updated coupling feature matrix and attenuation slope, the remaining safe service life of components is predicted, and a list of minor repair and preventive reinforcement is generated annually / quarterly to achieve early minor reinforcement and avoid large-scale renovations later.

[0110] During equipment operation, the system continuously collects stress, negative pressure, dust load, and corrosion data according to the original 15-minute sampling cycle, constantly generating new time-series data and synchronously recording it into the full life cycle standardized database, and refreshing the four-dimensional coupling feature matrix in real time; then, based on the updated matrix, it recalculates the component health degradation slope, dynamically corrects the deterioration trend and remaining service life, thereby achieving continuous updates of data, models, and parameters.

[0111] The system relies on a continuously updated four-dimensional coupling feature matrix to periodically recalculate the component health degradation slope. Combining historical degradation patterns, stress thresholds, and specification limits, it extrapolates the time when the component stress reaches the critical value of the defect under the current degradation rate, thereby determining the remaining safe service life.

[0112] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A graded intelligent reinforcement and optimization method for electrostatic precipitators, characterized in that: Includes the following steps: S1: Obtain the static inherent parameters, historical time-series data and real-time dynamic data of the entire structure of the electrostatic precipitator, establish a time-series index according to the unique component code, and build a standardized database for the entire life cycle. S2: Based on the aforementioned standardized database for the entire life cycle, four types of characteristic quantities are extracted: ash accumulation load, equivalent stress, cross-sectional corrosion thinning rate, and negative pressure inside the shell. Multi-field coupling correction coefficients are introduced to calculate the state deviation coefficient, and a four-dimensional coupling characteristic matrix of load-stress-corrosion-negative pressure is constructed. S3: The four-dimensional coupling feature matrix is ​​split into multiple time windows. An improved grey relational algorithm is used to calculate the correlation between the state deviation coefficient under each time window and the degradation feature curve of the historical failure sample in the full life cycle standardized database. After weighted fusion, the health decay slope of each component is fitted and updated. S4: Based on the health decay slope and dynamic grading threshold, determine the component defect level, generate and output a graded reinforcement scheme; S5: Implement reinforcement construction on the components according to the graded reinforcement scheme; S6: Simulate the extreme verification conditions and conduct on-site measurements to calculate the stress surplus coefficient at each measuring point. Iterate and optimize the reinforcement parameters based on the stress surplus coefficient until all measuring points meet the preset specification requirements.

2. The graded intelligent reinforcement and optimization method for electrostatic precipitators according to claim 1, characterized in that, In step S2, the multi-field coupling correction coefficient includes load addition coefficient, corrosion reduction coefficient and negative pressure addition coefficient. The multi-field coupling correction coefficient is combined with structural mechanics theory to construct a coupling calculation formula, generate comprehensive characteristic values ​​of each measuring point, collect the reference stress values ​​of each measuring point under no-load, clean and dust-free working conditions, and compare the comprehensive characteristic values ​​with the reference stress values ​​to obtain the state deviation coefficient. The four-dimensional coupling characteristic matrix is ​​constructed based on the state deviation coefficient.

3. The graded intelligent reinforcement and optimization method for electrostatic precipitators according to claim 1, characterized in that, The four-dimensional coupling feature matrix is ​​formed by filling data row by row in chronological order, with each effective sampling sequence of the day as the row and the state deviation coefficients corresponding to four types of feature quantities, namely, ash load, equivalent stress, cross-sectional corrosion thinning rate and negative pressure inside the shell, as the columns, and finally forming an Mn×4 four-dimensional coupling feature matrix, where n is the number of effective samplings on the day.

4. The graded intelligent reinforcement and optimization method for electrostatic precipitators according to claim 1, characterized in that, In step S3, the multi-level time window includes a daily window, a monthly window, and a quarterly window; the weighted fusion uses fixed weights for fusion, and fits a univariate linear degradation equation with the cumulative number of days of operation as the independent variable and the comprehensive correlation degree as the dependent variable to solve for the health decay slope.

5. The graded intelligent reinforcement and optimization method for electrostatic precipitators according to claim 1, characterized in that, In step S4, the method for adjusting the dynamic grading threshold is as follows: retrieve the historical stress sample datasets of normal, excessive and failed types from the full life cycle standardized database, construct the three types of stress probability distribution models respectively, and dynamically correct the stress grading thresholds corresponding to each level of defect with preset false alarm rate and detection rate as constraints.

6. The graded intelligent reinforcement and optimization method for electrostatic precipitators according to claim 4, characterized in that, The defect levels include Level 1 mild defects, Level 2 moderate defects, and Level 3 severe defects; each level of defect corresponds to a different stress grading threshold range and is matched with different foundation reinforcement schemes; the health attenuation slope is used to correct the reinforcement safety margin of the foundation reinforcement scheme.

7. The graded intelligent reinforcement and optimization method for electrostatic precipitators according to claim 1, characterized in that, In step S6, the extreme verification conditions include the support full ash load condition, the ash hopper full ash load condition, and the extreme negative pressure condition. The stress surplus coefficient is calculated by dividing the difference between the allowable stress of the component and the measured stress under extreme conditions by the allowable stress.

8. The graded intelligent reinforcement and optimization method for electrostatic precipitators according to claim 6, characterized in that, In step S6, the iterative optimization specifically involves: when the stress surplus coefficient is less than the preset lower limit, enlarging the reinforcing section or adding auxiliary stiffening components and re-verifying; when the stress surplus coefficient is greater than the preset upper limit, reducing the amount of material used in subsequent reinforcement of similar components until the stress surplus coefficient of all measuring points falls within the preset reasonable range.

9. The graded intelligent reinforcement and optimization method for electrostatic precipitators according to claim 1, characterized in that, The method further includes step S7: based on the continuously updated four-dimensional coupling feature matrix and the health decay slope, predict the remaining safe service life of each component and generate a periodic preventive reinforcement list.

10. The graded intelligent reinforcement and optimization method for electrostatic precipitators according to claim 1, characterized in that, In step S3, the degradation characteristic curve of the historical failure sample is obtained by fitting historical monitoring data of the same type of component retrieved from the full life cycle standardized database.

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