High-temperature pressure-bearing component risk assessment method and system based on multi-dimensional parameters

Abstract

Embodiments of the present application disclose a high-temperature pressure-bearing component risk assessment method and system based on multi-dimensional parameters, wherein the method comprises collecting basic parameters, operation data and inspection and detection data of a target component, and preprocessing the basic parameters and the inspection and detection data; performing risk quantitative analysis on the preprocessed inspection and detection data to generate risk values corresponding to each item of inspection and detection data; performing risk index calculation based on each risk value and performing risk grade division; and outputting a risk assessment report and a repair strategy based on the risk grade. The system comprises a data acquisition module, a quantitative analysis module, a risk calculation module and a strategy generation module to respectively perform the above steps. By coupling key parameters such as wall thickness, defects, hardness and stress with weighting, risk assessment based on multi-dimensional parameters is realized, the stability and reliability of the evaluation results are improved, and through preprocessing, standard dynamic updating is realized to improve the timeliness of the evaluation.

Description

Technical Field

[0001] This application relates to the field of power equipment safety assessment technology, and in particular to a risk assessment method and system for high-temperature pressure-bearing components based on multi-dimensional parameters. Background Technology

[0002] High-temperature pressure-bearing components are important equipment components in thermal power generating units that withstand high-temperature and high-pressure conditions. These include main steam pipes, reheat steam pipes, and superheater headers. These components are in a high-temperature and high-pressure operating environment for a long time. During service, their materials will undergo various forms of performance degradation, such as microstructure degradation, creep damage, fatigue crack initiation and propagation, oxidation and corrosion. If risks are not identified and maintenance measures are not taken in time, leakage or rupture accidents may occur, threatening the safe and stable operation of the unit.

[0003] Currently, the condition assessment of high-temperature pressure-bearing components in thermal power plants typically relies on a single parameter. However, in practical engineering, existing assessment methods still suffer from the following problems: First, the assessment indicators are relatively singular. Some methods only use wall thickness reduction or hardness changes as the basis for judgment, failing to comprehensively consider the coupled effects of defects, material performance degradation, and stress state, making it difficult to accurately reflect the true safety level of the components. Second, the allowable stress values ​​of high-temperature heat-resistant steel materials are adjusted with standard updates, but some assessment systems have not established an update mechanism to match the standard versions, potentially leading to insufficient strength margins for components deemed qualified under the old standards under the new standards. Third, defect detection records are mostly qualitative descriptions, lacking quantitative correlation analysis of the number, level, location, and stress concentration of defects, resulting in insufficient identification of defect expansion trends and potential failure risks. Fourth, maintenance and inspection strategies are mostly implemented on a fixed cycle, failing to dynamically adjust according to the component's risk level. This leads to insufficient monitoring of some high-risk areas, while low-risk areas may undergo unnecessary over-maintenance, resulting in a waste of maintenance resources.

[0004] Therefore, how to establish a quantifiable, gradable, and dynamically adaptable risk assessment method based on multi-source data, and formulate differentiated maintenance strategies accordingly, has become an urgent problem to be solved in the field of safety management of high-temperature pressure components. Summary of the Invention

[0005] The embodiments of this application provide a risk assessment method and system for high-temperature pressure-bearing components based on multi-dimensional parameters, so as to establish a quantifiable, analyzable risk assessment scheme that can be dynamically adapted to standard updates based on multi-source data.

[0006] To address the aforementioned technical problems, embodiments of this application disclose the following technical solutions: Firstly, a risk assessment method for high-temperature pressure-bearing components based on multi-dimensional parameters is provided, including: collecting basic parameters, operational data, and inspection and testing data of the target component, and preprocessing the basic parameters and inspection and testing data; performing risk quantification analysis on the preprocessed inspection and testing data to generate risk values ​​for each inspection and testing data; calculating risk indices based on each risk value and classifying risk levels; and outputting a risk assessment report and maintenance strategy based on the risk levels.

[0007] Furthermore, the basic parameters include component name, material grade, specifications, design pressure, design temperature, allowable stress, and standard update information; the operating data includes cumulative operating time, number of start-stop cycles, over-temperature and over-pressure records, and actual operating pressure and temperature; the inspection and testing data includes wall thickness data, hardness data, defect data, and stress data, wherein the wall thickness data includes the measured wall thickness values ​​of each part of the target component and the minimum required wall thickness, the hardness data includes the measured hardness values ​​of the base material and weld of the target component and the hardness range specified by the standard, the defect data includes defect type, location, size, detection method, and defect level, and the stress data includes the stress coefficient of the structural type at each location in the corresponding target component; the preprocessing includes removing data with measurement errors exceeding a preset threshold, and updating the allowable stress and the minimum required wall thickness according to the standard update information.

[0008] Furthermore, the risk quantification analysis includes calculating risk values ​​for wall thickness, hardness, defects, and stress respectively. Specifically, the risk value for wall thickness is assigned in segments based on the difference between the measured wall thickness and the minimum required wall thickness, and a coefficient is applied based on the number of measurement points. The risk value for hardness is assigned in segments based on the difference between the measured hardness value and the lower limit of the hardness range, and a coefficient is applied based on the number of measurement points. The risk value for defects is assigned based on a combination of defect level, defect quantity, and defect location, and an upper limit is set. The risk value for stress is assigned based on the structural type and its corresponding stress coefficient at each location, and a correlation coefficient is applied based on the number of defects, as well as an upper limit is set.

[0009] Furthermore, the risk index is calculated based on the weighted sum of the various risk values, and the risk values ​​arranged in descending order of weight coefficients are wall thickness, defects, stress, and hardness.

[0010] Furthermore, the risk levels include high risk, medium-high risk, medium risk, and low risk, wherein the risk index of high risk is greater than or equal to 80, the risk index of medium-high risk is less than 80 but greater than or equal to 60, the risk index of medium risk is less than 60 but greater than or equal to 40, and the risk index of low risk is less than 40.

[0011] Furthermore, the risk assessment report includes at least the risk level distribution of the target components, a list of high-risk parts, and a defect trend analysis; the maintenance strategy includes maintenance cycles set according to risk level differences.

[0012] Furthermore, the stress data is obtained through finite element analysis, and different structural types are mapped to stress coefficients. The structural types include at least tees, elbows, and straight pipe sections, and the stress coefficient at the tee position is higher than that at the elbow position, and the stress coefficient at the elbow position is higher than that at the straight pipe section position, so as to reflect the difference in stress concentration.

[0013] Furthermore, the methods for acquiring the test data include ultrasonic testing, magnetic particle testing, and / or penetrant testing.

[0014] Secondly, a risk assessment system for high-temperature pressure-bearing components based on multi-dimensional parameters is provided, comprising: a data acquisition module for collecting basic parameters, operational data, and inspection and testing data of the target component and preprocessing the basic parameters and inspection and testing data, and having a built-in standard database to dynamically update the standard database according to the collected standard update information; a quantitative analysis module for performing risk quantitative analysis on the preprocessed inspection and testing data and generating risk values ​​for each inspection and testing data; a risk calculation module for calculating risk indices based on each risk value and classifying risk levels; and a strategy generation module for outputting risk assessment reports and maintenance strategies based on the risk levels.

[0015] Furthermore, the data acquisition module includes a design data interface, an operational data interface, and an inspection record interface, used to collect the basic parameters, operational data, and inspection and testing data of the target component, respectively; the quantitative analysis module includes a wall thickness calculation unit, a hardness analysis unit, a defect classification unit, and a stress coefficient unit, used to calculate the measured wall thickness value, calculate the measured hardness value, classify the defect level, and calculate the stress coefficient, respectively; the risk calculation module includes a weight configuration unit, a risk index calculation unit, and a risk level determination unit, used to assign weights to various risk values, calculate the risk index based on a weighted summation method, and determine the risk level based on the risk index, respectively; the strategy generation module includes a report generation unit and a maintenance plan unit, used to output a risk assessment report and a maintenance plan, respectively.

[0016] The aforementioned technical solutions have the following advantages or beneficial effects: By weighted coupling modeling of key parameters such as wall thickness, defects, hardness, and stress, the one-sidedness of traditional single-index assessment is overcome, making the risk status quantifiable, comparable, and traceable, adaptable to the full life-cycle management scenario of high-temperature pressure-bearing components in thermal power units; by dynamically adjusting the data basis of risk assessment through the collection of standard update information, and automatically recalculating the minimum required wall thickness as allowable stress parameters such as ASME are updated, the strength judgment error caused by the use of old standards is significantly reduced, reducing risk misjudgment and omission from the source; defect assessment is closely coupled with structural stress coefficient, by combining defect level, quantity, and location stress... The concentration coefficient amplifies the weight of potential hazards in high-stress areas, enhancing the sensitivity and differentiation of critical location failure risks, and more closely reflecting actual failure mechanisms. Based on the risk index classification results, it automatically generates differentiated maintenance and monitoring strategies, avoiding over-inspection of low-risk components while enabling more frequent and targeted technical measures for medium- and high-risk areas, balancing equipment safety and operation and maintenance economy. It can also output clear risk indices and maintenance recommendations in engineering cases. It is applicable to typical components such as main steam pipes, reheat pipes, and superheater headers of 1000MW ultra-supercritical units with P91 / P92 / P122 as the main materials, and has universality and scalability. Attached Figure Description

[0017] The technical solution and its beneficial effects will become apparent from the following detailed description of specific embodiments of this application, in conjunction with the accompanying drawings.

[0018] Figure 1 A flowchart illustrating an exemplary risk assessment method provided in this application; Figure 2 A block diagram of an exemplary risk assessment system 100 provided for this application; Figure 3 A unit-level block diagram of an exemplary risk assessment system provided in this application.

[0019] Explanation of reference numerals in the attached figures: 100. Exemplary Risk Assessment System; 110. Data Acquisition Module; 120. Quantitative Analysis Module; 130. Risk Calculation Module; 140. Strategy Generation Module; 111. Design Data Interface; 112. Operational Data Interface; 113. Inspection Record Interface; 121. Wall Thickness Calculation Unit; 122. Hardness Analysis Unit; 123. Defect Classification Unit; 124. Stress Coefficient Unit; 131. Weight Configuration Unit; 132. Risk Index Calculation Unit; 133. Risk Level Determination Unit; 141. Report Generation Unit; 142. Maintenance Plan Unit. Detailed Implementation

[0020] To make the objectives, technical solutions, and beneficial effects of this application clearer, the following detailed description, in conjunction with the accompanying drawings and specific embodiments, further illustrates this application. It should be understood that the specific embodiments described in this specification are merely for explaining this application and are not intended to limit it.

[0021] Unless otherwise expressly defined, the terms “first,” “second,” “third,” etc., are used only to distinguish objects and do not limit quantity, order, or importance; “including / contains / has” is an open-ended term indicating the presence of, but not limited to, the listed elements; “at least” can be understood as “one or more” or “one or more”; “and / or” indicates any or all of a parallel relationship; unless otherwise specified, numerical ranges include the endpoints; “based on” should be understood as “at least partially based on”.

[0022] In the description of this application, the engineering term "high-temperature pressure-bearing component" refers to pressure-bearing components in thermal power plant units that operate under high temperature and high pressure conditions for extended periods. Typical components include main steam pipes, reheat steam pipes, and superheater headers, with common material grades being P91, P92, and P122. "Base material / weld" is used in this application to distinguish between hardness and defects using statistical methods. "Tee / elbow / straight pipe section" indicates typical structural locations and their stress concentration differences. "Allowable stress" refers to the stress value that a material can safely withstand for a long period at a specific temperature according to current standards. "Standard update information" refers to dynamic revisions of parameters such as allowable stress according to ASME Code Cases, national standards, or industry standards, which can be used to automatically adapt and update allowable stress and related calculation methods during data processing. "Minimum required wall thickness" refers to the minimum wall thickness that meets strength requirements calculated according to specifications under given design / operating conditions and allowable stress. To avoid ambiguity, this application does not limit specific finite element modeling details, defect quantitative evaluation algorithms, or specific standard clause versions, unless explicitly specified in the embodiments; when industry standard updates lead to adjustments in allowable stress or inspection grade caliber, the meaning of terms remains unchanged, and only the parameter library and thresholds used for calculation and judgment are updated accordingly.

[0023] In this application, terms such as "module / unit / device / system / processor / storage medium / computer program" can be implemented through software, hardware, or a combination of both. Terms such as "acquire / collect / read / receive / calculate / determine / output / generate" are used interchangeably when there is no ambiguity. "Computer-readable storage medium" can be any non-transitory storage medium. Terms such as "acquire / collect / read / receive / calculate / determine / output / generate" are used interchangeably when there is no ambiguity, representing routine data processing operations performed by the system / device / program. Those skilled in the art can reasonably understand the above terms based on the specific context and make equivalent substitutions for their specific implementations without departing from the spirit and scope of this application.

[0024] In the in-service assessment of high-temperature pressure-bearing components in thermal power plants, the long-term high-temperature and high-pressure service, coupled with the temperature and pressure fluctuations caused by start-up and shutdown and deep peak shaving, can easily trigger simultaneous issues such as wall thickness reduction, material hardness decrease, buried defects, and structural stress concentration. At the same time, standards such as allowable stress of materials are constantly and dynamically adjusted. Parts that were previously deemed qualified according to the old standards may have insufficient strength margin under the latest standard caliber. Traditional judgment methods that rely on a single parameter and fixed maintenance cycles cannot reflect the real risks in a timely and comprehensive manner. Moreover, defect records are mostly qualitative and decoupled from location stress, resulting in inconsistent assessment calibers and mismatched strategies.

[0025] To this end, this application proposes a risk assessment method and system for high-temperature pressure-bearing components based on multi-dimensional parameters. It collects multi-source data on the target component, including design, materials, operation, and inspection data, and performs structured preprocessing and quantitative analysis on four core indicators: wall thickness, hardness, defects, and stress. The method and system incorporate a standard dynamic adaptation mechanism and a standard database, calculates the minimum required wall thickness according to the latest allowable stress caliber, and standardizes the testing data caliber to ensure the timeliness and comparability of the assessment data.

[0026] Based on the above, this specific embodiment provides the following exemplary method and system for risk assessment of high-temperature pressure-bearing components based on multi-dimensional parameters.

[0027] Figure 1 A flowchart illustrating an exemplary risk assessment method provided in this application. Figure 1As shown, this embodiment provides an exemplary risk assessment method for high-temperature pressure-bearing components based on multi-dimensional parameters, including the following steps: S1, collecting basic parameters, operating data, and inspection and testing data of the target component, and preprocessing the basic parameters and inspection and testing data; S2, performing risk quantification analysis on the preprocessed inspection and testing data to generate risk values ​​for each inspection and testing data item; S3, calculating risk indices based on each risk value and classifying risk levels; S4, outputting a risk assessment report and maintenance strategy based on the risk levels.

[0028] Figure 2 A block diagram of an exemplary risk assessment system 100 provided for this application. Figure 2 As shown, this embodiment proposes an exemplary risk assessment system 100 based on the aforementioned exemplary risk assessment method. This system includes: a data acquisition module 110, used to collect basic parameters, operational data, and inspection and testing data of the target component, and to preprocess the basic parameters and inspection and testing data. It also has a built-in standard database to dynamically update the standard database based on the collected standard update information; a quantitative analysis module 120, used to perform risk quantitative analysis on the preprocessed inspection and testing data, generating risk values ​​for each item of inspection and testing data; a risk calculation module 130, used to calculate risk indices based on each risk value and to classify risk levels; and a strategy generation module 140, used to output risk assessment reports and maintenance strategies based on risk levels. In this embodiment, the standard database built into the data acquisition module 110 adopts the ASME Code Case, i.e., a temporary standard supplement document issued by the American Society of Mechanical Engineers (ASME), which updates parameters such as allowable stress of materials. For example, the allowable stress of P92 is lowered from 79.4 MPa to 68.8 MPa. In another implementation, the data acquisition module 110 can also incorporate the ASME BPVC (Boiler and Pressure Vessel Code) to adapt to reheat steam pipes and headers, calculating the minimum required wall thickness according to the allowable stress in Volume II, Part D, 2019 edition (TYPE I, p. 91). The calculation formula for the minimum required wall thickness refers to GB / T16507.4-2013 (boiler manufacturer scope) and DL / T5366-2014 (design institute scope).

[0029] The following is a detailed description of the method and system based on the above-described exemplary risk assessment method and exemplary risk assessment system 100.

[0030] In step S1 above, the basic parameters include component name, material grade, specifications, design pressure, design temperature, allowable stress, and standard update information. Operating data includes cumulative operating time, number of start-stop cycles, over-temperature and over-pressure records, and actual operating pressure and temperature. Inspection and testing data includes wall thickness data, hardness data, defect data, and stress data. The wall thickness data includes the measured wall thickness values ​​of each part of the target component and the minimum required wall thickness, such as the average of 3 to 5 measurement points of ultrasonic testing at each part (accurate to 0.01 mm); the hardness data includes the measured hardness values ​​of the base material and weld of the target component and the hardness range specified by the standard, such as the average of 3 Leeb hardness test measurements of the base material and weld (accurate to 1 HB) and the hardness range specified by the standard (180-250 HB for P92, 185-270 HB for P91); the defect data includes the defect type, location, size, detection method and defect level, such as the defect type being point-like or crack-like, the defect location being a tee, bend, or straight pipe, the detection method being UT (ultrasonic testing), MT (magnetic particle testing), or PT (penetrating penetrant testing), and the defect level being Class I, Class II and Class III according to the NB / T47013 standard; the stress data includes the stress coefficient of the structural type at each location in the corresponding target component. Preprocessing includes discarding data with measurement errors exceeding a preset threshold and updating the allowable stress and minimum required wall thickness based on standard update information. In this embodiment, the preset threshold is set to 5%, meaning data with measurement errors exceeding 5% are discarded. Inspection and testing data are obtained through ultrasonic testing, magnetic particle testing, and / or penetrant testing. Stress data is obtained through finite element analysis, mapping different structural types to stress coefficients. The structural types include at least tees, elbows, and straight pipe sections, with the stress coefficient at tees being higher than that at elbows, and the stress coefficient at elbows being higher than that at straight pipe sections, to reflect differences in stress concentration.

[0031] In step S2 above, the risk quantification analysis includes calculating risk values ​​for the wall thickness, hardness, defects, and stress of the target component. Specifically, the risk value for wall thickness is assigned in segments based on the difference between the measured wall thickness and the minimum required wall thickness, and a coefficient is adjusted according to the number of measurement points. The risk value for hardness is assigned in segments based on the difference between the measured hardness value and the lower limit of the hardness range, and a coefficient is adjusted according to the number of measurement points. The risk value for defects is assigned based on a combination of defect level, defect quantity, and defect location, and an upper limit is set. The risk value for stress is assigned based on the structural type and its corresponding stress coefficient at each location, and a correlation coefficient and an upper limit are applied according to the number of defects.

[0032] This application provides the following table based on the above-mentioned method for assigning and correcting risk values ​​for wall thickness.

[0033]

[0034] Table 1 Table 1 shows the risk value assignment and correction coefficient for wall thickness, where the final risk value of wall thickness is equal to the product of the base value and the data volume correction coefficient.

[0035] This application provides the following table based on the aforementioned method for assigning and correcting risk values ​​for hardness.

[0036]

[0037] Table 2 Table 2 shows the risk value assignment and correction coefficient for hardness. The risk value of hardness is equal to the product of the base value and the data volume correction coefficient, with an upper limit of 100.

[0038] This application provides the following table based on the risk value combination assignment method for the aforementioned defects.

[0039]

[0040] Table 3 Table 3 shows the risk value assignment and correction coefficient for defects. The risk value of a defect is equal to the product of the base value, the data volume correction coefficient, and the location correction coefficient, with an upper limit of 100. The base value is classified according to the NB / T47013 standard. Level I defects are assigned a value of 30, Level II defects are assigned a value of 60, and Level III and above are assigned a value of 100. The risk value of defects located in high stress areas (stress coefficient 60) is multiplied by a coefficient of 1.2.

[0041] This application provides the following table based on the above-mentioned method for assigning and correcting the risk value of stress.

[0042]

[0043] Table 4 Table 4 shows the stress risk value assignment and correction coefficient table. The stress risk value is equal to the product of the base value and the defect correlation coefficient. The base value in this table is based on the stress coefficient. For example, when the stress coefficient is 60, it indicates that the stress is too concentrated and the base value should be assigned 100.

[0044] In step S3 above, the risk index is calculated based on the weighted sum of various risk values, and the risk values ​​arranged in descending order of weight coefficients are wall thickness, defects, stress, and hardness. An optional weight coefficient allocation is: 35% for wall thickness risk values, 30% for defects, 20% for stress risk values, and 15% for hardness risk values. Risk levels include high risk, medium-high risk, medium risk, and low risk. Specifically, a high-risk risk index is greater than or equal to 80, a medium-high risk risk index is less than 80 but greater than or equal to 60, a medium-risk risk index is less than 60 but greater than or equal to 40, and a low-risk risk index is less than 40.

[0045] In step S4 above, the risk assessment report includes at least the risk level distribution of the target components, a list of high-risk parts, and a defect trend analysis; the maintenance strategy includes maintenance cycles set according to risk level differences. In one feasible embodiment, the maintenance strategy for high-risk parts is specified as replacement within one year or monthly inspection, while medium- and high-risk parts are subject to full inspection every two years.

[0046] Figure 3 A unit-level block diagram of an exemplary risk assessment system 100 provided in this application. Figure 3 As shown, in this exemplary risk assessment system 100, the data acquisition module 110 includes a design data interface 111, an operation data interface 112, and an inspection record interface 113, which are used to collect the basic parameters, operation data, and inspection and testing data of the target component, respectively; the quantitative analysis module 120 includes a wall thickness calculation unit 121, a hardness analysis unit 122, a defect classification unit 123, and a stress coefficient unit 124, which are used to calculate the measured wall thickness value, calculate the measured hardness value, classify the defect level, and calculate the stress coefficient, respectively; the risk calculation module 130 includes a weight configuration unit 131, a risk index calculation unit 132, and a risk level determination unit 133, which are used to configure weights for each risk value, calculate the risk index based on the weighted summation method, and determine the risk level based on the risk index, respectively; the strategy generation module 140 includes a report generation unit 141 and a maintenance plan unit 142, which are used to output a risk assessment report and a maintenance plan, respectively.

[0047] Combination Figures 1 to 3 The risk assessment of the main steam pipeline of Unit 3 of a thermal power plant is as follows.

[0048] The design data interface 111 in the data acquisition module 110 collected the design temperature of 610℃, the design pressure of 28.83MPa, and the allowable stress of 68.8MPa according to ASME Code Case 2179-8; the operation data interface 112 collected the cumulative operation of 100413 hours, 54 start-ups and shutdowns, the highest temperature exceedance of 3℃ (608℃), and the duration of 0.72 hours; the inspection record interface 113 collected the measured wall thickness of 95mm, the minimum required wall thickness of 96.84mm (610℃ condition), the wall thickness difference of -1.84mm, the local base material hardness of 175HB (standard range 180HB-250HB), the hardness deviation of -5HB, and the presence of 9 Class I point defects in weld HA5, which is an elbow with a stress concentration factor of 40. Based on the above data, the quantitative analysis module 120 outputs the following values ​​through the wall thickness calculation unit 121, hardness analysis unit 122, defect classification unit 123, and stress coefficient unit 124: wall thickness risk value 50 (-5mm to 0mm range, base value 50 × data volume correction factor 1.0), hardness risk value 30 (-10HB to 0HB range, base value 30 × data volume correction factor 1.0), defect risk value 100 (9 Class I defects, base value 80 × quantity correction factor 2.0 × position correction factor 1.0, capped at 100), and stress risk value 60 (elbow stress coefficient 40, base value 60 × defect correlation coefficient 1.0). Based on the aforementioned risk values, the risk calculation module 130 assigns weight coefficients of 35%, 30%, 20%, and 15% to the wall thickness risk value, defect risk value, stress risk value, and hardness risk value respectively through the weight configuration unit 131. The risk index is calculated by the risk index calculation unit 132 as 50×35%+30×15%+100×30%+60×20%=17.5+4.5+30+12=64. Based on the risk level determination unit 133 and the risk level classification threshold set in step S3 above, it is determined to be of medium to high risk. Based on the risk level determination and various data, the strategy generation module 140 outputs a risk assessment report through the report generation unit 141, indicating the locations of medium-high risk and medium-high risk, and indicating whether there is a serious expansion of the defect trend based on the inspection record data. The maintenance plan is output through the maintenance plan unit 142 as follows: the part needs to undergo full-circumference ultrasonic testing and hardness retesting once every 2 years, and the defect expansion trend should be monitored. At the same time, referring to the actual operation and adjustment experience records of Unit 3, the main steam temperature should be controlled below 595℃ to reduce the creep damage rate.

[0049] In summary, this application focuses on high-temperature pressure-bearing components and constructs a weighted quantitative evaluation model based on four types of inspection and testing data: wall thickness, hardness, defects, and stress. By collecting multi-source information such as design, materials, operation, and inspection records, and performing outlier removal and dynamic standard adaptation, and based on automatically updating the minimum required wall thickness according to the latest allowable stress, the wall thickness difference, hardness deviation, defect level and location, and structural stress coefficient are uniformly quantified into parameter risk values ​​from 0 to 100. The risk index is calculated according to preset weights, and the risk level is classified. This leads to the generation of an evaluation report and differentiated maintenance strategies that include risk distribution, high-risk details, and defect trend analysis. The technical solution of this application compensates for the lack of identification of hidden dangers in high-stress areas by using multi-parameter coupled modeling and associating defects with stress. It reduces the risk of misjudgment caused by the lag in allowable stress updates by using standard self-adaptation. It improves the stability and comparability of the assessment by correcting the amount of data and processing the position coefficient. It drives the frequency and means of maintenance by risk level, taking into account both operational safety and maintenance economy. It is applicable to the whole life cycle risk management of materials such as P91, P92, and P122 in ultra-supercritical units.

[0050] The above embodiments are provided only to help understand the methods, systems, and core ideas of this application. Those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the scope of protection of the claims.

Claims

1. A risk assessment method for high-temperature pressure-bearing components based on multi-dimensional parameters, characterized in that, include: Collect the basic parameters, operating data, and inspection and testing data of the target component, and preprocess the basic parameters and the inspection and testing data; Risk quantification analysis is performed on the preprocessed test and inspection data to generate risk values ​​for each test and inspection data item. Based on the risk values ​​mentioned above, a risk index is calculated, and risk levels are classified. Based on the risk level, a risk assessment report and maintenance strategy will be generated.

2. The risk assessment method for high-temperature pressure-bearing components based on multi-dimensional parameters as described in claim 1, characterized in that, The basic parameters include component name, material grade, specifications, design pressure, design temperature, allowable stress, and standard update information; The operational data includes cumulative operating time, number of start-stop cycles, over-temperature and over-pressure records, and actual operating pressure and temperature. The inspection and testing data includes wall thickness data, hardness data, defect data, and stress data. The wall thickness data includes the measured wall thickness values ​​of each part of the target component and the minimum required wall thickness. The hardness data includes the measured hardness values ​​of the base material and weld of the target component and the hardness range specified by the standard. The defect data includes the defect type, location, size, detection method, and defect level. The stress data includes the stress coefficient of the structural type at each location in the target component. The preprocessing includes discarding data whose measurement error exceeds a preset threshold, and updating the allowable stress and the minimum required wall thickness based on standard update information.

3. The risk assessment method for high-temperature pressure-bearing components based on multi-dimensional parameters as described in claim 2, characterized in that, The risk quantification analysis includes calculating risk values ​​for wall thickness, hardness, defects, and stress, respectively. The risk value of wall thickness is assigned in segments based on the difference between the measured wall thickness and the minimum required wall thickness, and the coefficient is adjusted according to the number of measurement points. The risk value of hardness is assigned in segments based on the difference between the measured hardness value and the lower limit of the hardness range, and a coefficient is adjusted according to the number of measurement points. The risk value of a defect is assigned based on a combination of defect level, defect quantity, and defect location, with an upper limit set. The stress risk value is assigned based on the structural type and its corresponding stress coefficient at each location, and a correlation coefficient and upper limit are applied according to the number of defects.

4. The risk assessment method for high-temperature pressure-bearing components based on multi-dimensional parameters as described in claim 1, characterized in that, The risk index is calculated based on the weighted sum of the risk values, and the risk values ​​arranged in descending order of weight coefficient are wall thickness, defects, stress, and hardness.

5. The risk assessment method for high-temperature pressure-bearing components based on multi-dimensional parameters as described in claim 1, characterized in that, The risk levels include high risk, medium-high risk, medium risk, and low risk. The risk index of high risk is greater than or equal to 80, the risk index of medium-high risk is less than 80 but greater than or equal to 60, the risk index of medium risk is less than 60 but greater than or equal to 40, and the risk index of low risk is less than 40.

6. The risk assessment method for high-temperature pressure-bearing components based on multi-dimensional parameters as described in any one of claims 1 to 5, characterized in that, The risk assessment report shall include at least the risk level distribution of the target component, a list of high-risk parts, and a defect trend analysis; The maintenance strategy includes maintenance cycles that are differentiated according to risk level.

7. The risk assessment method for high-temperature pressure-bearing components based on multi-dimensional parameters as described in claim 2, characterized in that, The stress data is obtained through finite element analysis, and different structural types are mapped to stress coefficients. The structural types include at least tees, elbows, and straight pipe sections. The stress coefficient at the tee position is higher than that at the elbow position, and the stress coefficient at the elbow position is higher than that at the straight pipe section position, so as to reflect the difference in stress concentration.

8. The risk assessment method for high-temperature pressure-bearing components based on multi-dimensional parameters as described in any one of claims 1 to 5, characterized in that, The methods for acquiring the test data include ultrasonic testing, magnetic particle testing, and / or penetrant testing.

9. A risk assessment system for high-temperature pressure-bearing components based on multi-dimensional parameters, characterized in that, include: The data acquisition module is used to collect the basic parameters, operating data and inspection and testing data of the target component, and to preprocess the basic parameters and inspection and testing data. It also has a built-in standard database to dynamically update the standard database according to the collected standard update information. The quantitative analysis module is used to perform risk quantitative analysis on the preprocessed inspection and testing data and generate risk values ​​for each item of inspection and testing data. The risk calculation module is used to calculate the risk index based on various risk values ​​and classify the risk level. as well as The strategy generation module is used to output a risk assessment report and maintenance strategy based on the risk level.

10. The risk assessment system for high-temperature pressure-bearing components based on multi-dimensional parameters as described in claim 9, characterized in that, The data acquisition module includes a design data interface, an operation data interface, and an inspection record interface, which are used to collect the basic parameters, operation data, and inspection and testing data of the target component, respectively. The quantitative analysis module includes a wall thickness calculation unit, a hardness analysis unit, a defect classification unit, and a stress coefficient unit, which are used to calculate the measured wall thickness value, calculate the measured hardness value, classify the defect level, and calculate the stress coefficient, respectively. The risk calculation module includes a weight configuration unit, a risk index calculation unit, and a risk level determination unit, which are respectively used to configure weights for each risk value, calculate the risk index based on the weighted summation method, and determine the risk level based on the risk index. The strategy generation module includes a report generation unit and a maintenance plan unit, which are used to output a risk assessment report and a maintenance plan, respectively.

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