A method for predicting and analyzing corrosion failure of naval equipment

Abstract

The present invention relates to a method for predicting and analyzing corrosion failure of naval equipment, and the specific steps are: 1. Analysis and judgment of corrosion failure modes; 2. Determination of statistical functions of naval equipment under typical corrosion failure modes; 3. Analysis of life function based on MATLAB: through the first and second analyses, and through test data or actual ship data, the corrosion basic parameters of the analysis object are obtained, the Weibull distribution function is analyzed, and the statistical laws of corrosion failure are obtained through numerical analysis, and then prediction and judgment are carried out. The method for predicting and analyzing corrosion failure of naval equipment provided by the present invention is based on experimental test data and software analysis. Compared with existing prediction methods, the prediction results are more accurate and can be used for systematic analysis of corrosion failure assessment under typical corrosion failure modes of key systems of naval equipment, providing key step support for life prediction, failure risk prediction, accelerated testing and process monitoring and monitoring, and long-term failure mode correction of equipment during its service period.

Description

Technical Field

[0001] The present invention belongs to the technical field of complex mathematical operations, and in particular relates to a method for predicting and analyzing corrosion failure of naval vessel equipment. Background Art

[0002] A wide range of research has been conducted at home and abroad on the reliability of naval equipment in service and safety throughout its life cycle. The focus includes the relationship between the service equipment environment and corrosion failure, as well as the assessment and prediction of equipment corrosion and fatigue life.

[0003] Since the 1970s, Western countries represented by the United States and the United Kingdom have focused on aircraft corrosion protection and corrosion control technologies and have carried out fatigue life assessment and life cycle health management of naval aircraft, nuclear-powered and conventional-powered aircraft carrier-based aircraft structures. They have also formulated a large number of supporting technical specifications such as corrosion prevention and control of aerospace weapon systems and naval aircraft corrosion maintenance outlines, and are at the forefront of corrosion prediction technology research.

[0004] For a long time, the corrosion life prediction of my country's ship structural materials has mainly relied on a combination of linear prediction and experimental evaluation. The prediction of the local corrosion rate of the material is very different from the actual environmental conditions. The environmental factors considered in the corrosion failure process are relatively single. The material-level corrosion life is difficult to reflect the actual application life of components and members, and the life prediction results are inaccurate.

[0005] In view of my country's urgent requirement for predictable reliability and safety of current and future marine ship equipment throughout their service life, the present invention proposes a statistically based corrosion failure prediction and analysis method for marine ship equipment, which can be used to systematically analyze the corrosion failure assessment of key systems (materials / structures / components) of marine ship equipment under typical corrosion failure modes, providing key step support for life prediction and failure risk prediction of marine ship equipment during its service period. The analysis and prediction results can also be inverted in the ship equipment development stage for iterative optimization design. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to address the above-mentioned deficiencies in the prior art and provide a method for predicting and analyzing corrosion failure of ship equipment. This method overcomes the defects of traditional methods for predicting corrosion failure of ship equipment due to the small number of samples and insufficient consideration of the complexity of application environment factors, resulting in large deviations between the life prediction results and the actual results. The method has the advantages of evolvable failure probability statistics and relatively accurate life prediction.

[0007] In order to solve the above technical problems, the technical solution provided by the present invention is:

[0008] A method for predicting and analyzing corrosion failure of naval vessel equipment is provided. The specific steps are as follows:

[0009] 1. Analysis and determination of corrosion failure mode:

[0010] S1. Analyze the environmental conditions of ship equipment: Decompose the ship equipment into system parts a1, a2, ..., ai, where i is an integer, according to the structure or system. Then analyze the material characteristics, structural characteristics, corrosion environment conditions, stress and relative motion of each system part, etc.;

[0011] S2. Determine the corrosion mode of ship equipment: Based on the environmental condition analysis results of S1, analyze the possible corrosion modes of each system part, provide simulated environmental conditions for possible corrosion phenomena, observe the corrosion phenomena produced by each system part under the simulated environmental conditions, and thus determine the corrosion failure mode of each system part, which can be specifically divided into general corrosion (wear, erosion, friction), pitting corrosion, intergranular corrosion, stress corrosion or fatigue cracking;

[0012] 2. Determination of statistical functions of ship equipment under typical corrosion failure modes:

[0013] Based on the environmental condition analysis and corrosion mode determination in step 1, determine the statistical function of each system part under the typical corrosion failure mode, set the unreliable statistical function of corrosion failure as F(t), the corrosion failure density function as f(t), and establish the corresponding relationship between the corrosion failure mode and the statistical function;

[0014] The corrosion failure density function f(t) based on Weibull probability distribution is expressed as:

[0015]

[0016] Among them, β and η are the shape parameter and scale parameter of the Weibull distribution function, t0 is the location parameter, and t is time.

[0017] The meaning of the above parameters is:

[0018] β represents the change of corrosion failure probability over time, and the shape of the Weibull distribution function reflects the failure law of the analysis object at different stages;

[0019] η represents the rate of increase of corrosion failure behavior over time, that is, it reflects the failure rate of the analyzed object;

[0020] t0 represents the initial stage of corrosion failure behavior. If the object being analyzed begins to be used and enters its service life, t0 = 0.

[0021] The unreliable statistical function F(t) of corrosion failure is:

[0022]

[0023] Substituting (1) into (2) yields:

[0024]

[0025] The mathematical expectation E(t) and variance formula D(t) of the Weibull distribution function are:

[0026]

[0027]

[0028] The corrosion failure index is set as α. The value of α is generally determined based on the impact of corrosion failure on the key systems (materials / structures / components) of ship equipment. Its quantitative value is determined by the failure ratio of piping, welds, regions or units in the key systems of ship equipment. Referring to HB5192-81 "Appearance Corrosion Grade Assessment Method for Coatings and Chemical Coverings", the value of α is determined according to formula (6):

[0029]

[0030] Where F0 is the area of ​​the corrosion spot in the analysis area, F1 is the total area of ​​the analysis area, and C is the corrosion depth correction coefficient.

[0031] According to the standard definition, the corrosion depth levels are divided into four levels: slight corrosion, obvious corrosion, moderate corrosion and severe corrosion. The critical corrosion damage threshold corresponding to the corresponding corrosion level is the failure index α. According to the Navy standard "Technical Requirements for Repair of Pressure Hull Structures", the criterion for steel corrosion failure in seawater is "failure when the depth of the comprehensive corrosion pit reaches 10% of the matrix depth", and the test method is the 9-point method.

[0032] 3. Analysis of life function based on MATLAB

[0033] Through the above-mentioned analyses 1 and 2, and by obtaining the basic corrosion parameters of the analysis object through test data or actual ship data, the Weibull distribution function is analyzed, and the statistical laws of corrosion failure are obtained through numerical analysis, and then prediction and judgment are carried out. Specifically:

[0034] Performing logarithmic transformation on formula (3) yields formula (7):

[0035]

[0036] Formula (7) is in the form of Y = AX + B, where A = β, X = ln(t-t0), and B = -βlnη. Parameters β and η are obtained by linear regression through MATLAB numerical analysis, and then substituted into formula (7) to obtain the unreliable statistical function F(t) of corrosion failure of the analysis object, and the probability of corrosion failure within the corresponding life cycle can be further obtained.

[0037] According to the above scheme, the material characteristics in step 1 S1 include material chemical composition, microbial characteristics, surface properties, and interface characteristics.

[0038] According to the above scheme, the structural features in step 1 S1 include size and geometric shape.

[0039] According to the above scheme, the corrosive environment conditions described in step 1 S1 include the chemical composition, electrochemical composition and fluid characteristics of the liquid medium.

[0040] According to the above solution, the stress and relative movement of the structural connection or welding area in step 1 S1 include the relative position changes of various system parts under the action of environmental external forces or loads.

[0041] According to the above scheme, the simulated environmental conditions in step 1 S2 are the environmental characteristics of each system part ai so that it has the physical, chemical or mechanical conditions for the occurrence of corresponding corrosion phenomena, such as chemical thermodynamic conditions, stress and strain conditions, etc.

[0042] According to the above scheme, the method for obtaining the statistical law of corrosion failure through numerical analysis in step three is to use the least squares method for analysis.

[0043] The present invention determines the corrosion failure mode of ship equipment by conducting environmental analysis, material analysis, and corrosion failure mode analysis on ship equipment (materials / structures / components). Through analysis and deduction, the Weibull distribution function (the above formula 1), life distribution function (based on formula 1, the Weibull distribution function of shape parameters and size parameter values ​​is obtained by analysis), failure function (formula 3), and corrosion failure density function are established, and a statistical framework is established. Finally, MATLAB numerical analysis is used to obtain the corrosion failure function and life prediction of ship equipment.

[0044] The corrosion failure prediction and analysis method of the present invention performs statistical calculations on the basis of experimental sampling rather than pure theoretical analysis and estimation, thus overcoming the problem of fuzzy and inaccurate prediction results caused by human intervention and judgment, and the prediction results are more scientific, reasonable and objective.

[0045] The beneficial effects of the present invention are as follows: the ship equipment corrosion failure prediction and analysis method provided by the present invention is obtained based on experimental test data and software analysis. Compared with existing prediction methods, the prediction results are more accurate and can be used for systematic analysis of corrosion failure assessment under typical corrosion failure modes of key systems of ship equipment. It provides key step support for life prediction, failure risk prediction, accelerated testing and process monitoring and supervision, and long-term failure mode correction of equipment during its service period. The analysis and prediction results can be inverted in the equipment development stage as input for iterative optimization design. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a diagram showing the corresponding relationship between the corrosion failure mode and the statistical function in Example 1 of the present invention;

[0047] Figure 2 This is the result of the corrosion pit depth and test time of the sample in Example 1. DETAILED DESCRIPTION

[0048] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below with reference to the accompanying drawings.

[0049] Example 1

[0050] The following describes the present invention in detail, based on the example of corrosion failure of a steel plate specimen used in a local structure of a conventional Chinese ship during service in the waters of Qingdao. This experiment follows the Navy standard "Technical Requirements for Repair of Pressure Hull Structures," which stipulates that steel corrosion failure in seawater is considered "failure when the depth of a general corrosion pit reaches 10% of the substrate depth, with a design life objective (DLO) of 5 years." The specific corrosion failure prediction and analysis method is as follows:

[0051] Step 1: Analysis and determination of corrosion failure mode:

[0052] S1. Environmental Condition Analysis: The steel plate samples used in this embodiment were analyzed for their material characteristics (chemical composition, microbial characteristics, surface properties, interface characteristics), structural characteristics (size, geometry), corrosive environmental conditions (chemical composition of the liquid medium, electrochemical composition, and surface fluid characteristics), stress and relative motion in the structural connection or weld area (relative position changes of various system parts under the influence of environmental external forces or loads), and other environmental conditions. The results are as follows:

[0053] Chemical composition of steel plate material: low carbon steel, element mass percentage C≤0.22%, Mn≤1.4%, Si

[0054] ≤0.35%, S≤0.05%, P≤0.045%;

[0055] Microorganisms: The impact is very small and can be ignored;

[0056] Mechanical properties of steel plate: tensile strength σ b 370~500MPa, yield strength σ s 185~235;

[0057] Steel plate surface characteristics: The steel plate specimen in the test is a steel plate, without cathodic protection, coating and surface treatment, the test surface is polished, and the roughness Ra is 3.2μm;

[0058] Steel plate sample size: single piece, size is 50mm*30mm*10mm;

[0059] Phase interface: solid-liquid interface, solid-vapor interface, solid-liquid-vapor three-phase interface exist locally;

[0060] The corrosion environment is natural seawater environment. The seawater environment data comes from the Bohai Seawater Corrosion Test Station in my country: the main chemical components of seawater are [Cl - Electrolyte MCl x (NaCl, MgCl2, KCl, CaCl2, etc.), O2, H2O, as well as minor components such as trace nutrients, amino acids, and humic organic matter. Among them, the main chemical components closely related to corrosion exist in liquid and dissolved forms;

[0061] Electrochemical composition of seawater: electrochemical potential under dissolved oxygen environment is -0.85V~-1.10V (Ag / AgCl), average seawater temperature is 13.7℃, dissolved oxygen concentration in seawater is 5.9mol / L, average salinity is 32‰, and pH value is 8.3;

[0062] Surface fluid: The average flow rate of seawater is 0.1m / s, the surface is stress-free, and there is no local stringing phenomenon caused by high-speed scouring. The corrosion product deposits generated during the corrosion process are peeled off with the fluid;

[0063] Stress and relative movement in structural connections or welding areas: The impact is small and can be ignored.

[0064] The analysis results of the corrosion environment conditions of the steel plate samples described above are shown in Table 1.

[0065] Table 1

[0066]

[0067] S2. Determination of corrosion failure mode: Based on the above analysis of environmental conditions, the corrosion process of steel plate specimens in seawater is a natural corrosion reaction at the interface between the steel structure surface and seawater, that is, overall corrosion and local pitting corrosion caused by differences in metal microstructure.

[0068] Step 2: Determination of statistical functions of steel plate specimens under typical corrosion failure modes:

[0069] Based on the environmental condition analysis and corrosion mode determination in step 1, determine the statistical function of each system part under the typical corrosion failure mode, set the unreliable statistical function of corrosion failure as F(t), the corrosion failure density function as f(t), and establish the corresponding relationship between the corrosion failure mode and the statistical function;

[0070] The corrosion failure life of the steel plate specimens was obtained using the Weibull probability distribution based on the weakest link model, which can fully reflect the impact of material defects on material life.

[0071] The corrosion failure density function f(t) of the steel plate specimen is expressed as the aforementioned formula (1), and the unreliable statistical functions of corrosion failure are the aforementioned formulas (2) and (3):

[0072] Assume that the corrosion life probability is P(t1≤t)=P, where P is the normal life probability value, t is the remaining life, and t1 is the failure probability at the start time.

[0073] When P = 0.5 (unreliable probability function value with 50% probability), t p =t 50 , t 50 The median of the corresponding sample corrosion life is:

[0074]

[0075] Taking the logarithm of (8):

[0076]

[0077] The corresponding relationship between corrosion failure mode and statistical function is shown in the figure Figure 1 shown.

[0078] Solve the nonlinear equations (4)(5)(8) to obtain the reliability influencing factors (β, η), and then obtain the unreliable statistical function F(t) of corrosion failure and the corrosion failure density function f(t).

[0079] Step 3: Life function analysis based on MATLAB

[0080] The Qingdao sea area is located at 36°3′N, 120°25′E. The initial thickness of the steel plate sample was 10mm, and 100 samples were tested. After 1, 2, 4, and 8 years in the aforementioned sea area, the average corrosion pit depths were 0.41mm, 1.02mm, 1.14mm, and 1.15mm, respectively. The data was fitted using MATLAB numerical analysis to obtain a curve corresponding to the test time (Y-axis) and the corrosion pit depth (X-axis), as shown in the figure. Figure 2 shown.

[0081] The average pitting depth of each specimen during the test was measured using a nine-point method. This method measures the pitting depth of nine equally spaced points, centered on the specimen's corrosion pit. The average pitting depth at each of these nine points is taken during the sampling period, and the average is calculated as the average pitting depth at the current corrosion time. Material failure was determined based on a 10% initial thickness reduction. The average time to failure for the steel plate was 1.96 years, or approximately 706 days. The corrosion failure time (days) sequence for each specimen (in order of longest to shortest) is shown in Table 2 below.

[0082] Table 2

[0083]

[0084]

[0085]

[0086] Referring to the median rank method of Weibull probability distribution, the failure probability of the i-th failed specimen is:

[0087]

[0088] i: failed sample sequence number, N: total number of samples, in this embodiment, N=100,

[0089] Failure probability of the first failed specimen: Using this method, the failure probability of each sample is shown in Table 3.

[0090] Table 3

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] Using the least squares method, we obtain X(i)=lnt, Y(i)=lnln(1 / 1-F(t)), X(i)X(i), X(i)Y(i), Y(i)Y(i) and the calculated data are summarized in Table 3. We obtain A and B derived from formula (7) by solving:

[0097]

[0098]

[0099] The solution is: A≈1.5; B=-8.85

[0100] β≈1.5, η=365

[0101] Estimation of specimen life index: After the above calculation, the unreliable statistical function of corrosion failure of steel plate specimen is:

[0102]

[0103] When p = 0.5, t 50 The median of the corrosion life of the corresponding sample is calculated as t 50 =386, that is, after 386 days, the remaining life is half of the failure life.

[0104] According to the corrosion failure criterion that the depth of the pitting of the steel plate specimen in seawater reaches 10% of the substrate depth, and the design life objective (DLO) is 5 years, this embodiment analyzes the statistical law of the corrosion failure behavior of the steel plate specimen in the natural sea based on statistical results. The results show that: for a steel plate specimen in an unprotected state, when p = 0.5, t 50 =386, meaning that after 386 days, the remaining life is half the failure life. This means that after 1.07 years, the test specimen will have reached half its design corrosion life, resulting in failure at approximately 2.14 years (771 days). This difference of 65 days from the measured average failure time of 1.96 years (approximately 706 days) for steel plates indicates that the failure prediction results are generally close to the measured average. Furthermore, based on the test results and statistical analysis, appropriate protection measures are necessary to achieve the original target corrosion failure life of 5 years.

Claims

1. A method for predicting and analyzing corrosion failure of naval vessel equipment, characterized in that: The specific steps are as follows:

1. Analysis and determination of corrosion failure mode: S1. Analyze the environmental conditions of ship equipment: Decompose the ship equipment into system parts a1, a2, ..., ai, where i is an integer, according to the structure or system level. Then analyze the material characteristics, structural characteristics, corrosion environment conditions, stress and relative motion of each system part; S2. Determine the corrosion mode of ship equipment: Based on the environmental condition analysis results of S1, analyze the possible corrosion modes of each system part, provide simulated environmental conditions for possible corrosion phenomena, observe the corrosion phenomena produced by each system part under the simulated environmental conditions, and thus determine the corrosion failure mode of each system part, which can be specifically divided into general corrosion, pitting corrosion, intergranular corrosion, stress corrosion or fatigue cracking; 2. Determination of statistical functions of ship equipment under typical corrosion failure modes: Based on the environmental condition analysis and corrosion mode determination in step 1, determine the statistical function of each system part under the typical corrosion failure mode, set the unreliable statistical function of corrosion failure as F(t), the corrosion failure density function as f(t), and establish the corresponding relationship between the corrosion failure mode and the statistical function; The corrosion failure density function f(t) based on Weibull probability distribution is expressed as: Where β and η are the shape parameter and scale parameter of the Weibull distribution function, t0 is the location parameter, and t is the time; The meaning of the above parameters is: β represents the change of corrosion failure probability over time, and the shape of the Weibull distribution function reflects the failure pattern of the analysis object at different stages; η represents the rate of increase of corrosion failure behavior over time, that is, it reflects the failure rate of the analyzed object; t0 represents the initial stage of corrosion failure behavior. If the object being analyzed begins to be used and enters its service life, t0 = 0; The unreliable statistical function F(t) of corrosion failure is: Substituting (1) into (2) yields: The mathematical expectation E(t) and variance formula D(t) of the Weibull distribution function are: The corrosion failure index is set to α, and the value of α is determined according to formula (6): Where F0 is the area of ​​corrosion spots in the analysis area, F1 is the total area of ​​the analysis area, and C is the corrosion depth correction coefficient; 3. Analysis of life function based on MATLAB Through the above-mentioned analyses 1 and 2, and by obtaining the basic corrosion parameters of the analysis object through test data or actual ship data, the Weibull distribution function is analyzed, and the statistical laws of corrosion failure are obtained through numerical analysis, and then prediction and judgment are carried out. Specifically: Performing logarithmic transformation on formula (3) yields formula (7): Formula (7) is in the form of Y = AX + B, where A = β, X = ln(t-t0), and B = -βlnη. Parameters β and η are obtained by linear regression through MATLAB numerical analysis, and then substituted into formula (7) to obtain the unreliable statistical function F(t) of corrosion failure of the analysis object, and the probability of corrosion failure within the corresponding life cycle can be further obtained.

2. The method for predicting and analyzing corrosion failure of naval vessel equipment according to claim 1, characterized in that: The material characteristics in step 1 S1 include material chemical composition, microbial characteristics, surface properties, and interface characteristics.

3. The method for predicting and analyzing corrosion failure of naval vessel equipment according to claim 1, characterized in that: The structural features in step 1 S1 include size and geometric shape.

4. The method for predicting and analyzing corrosion failure of naval vessel equipment according to claim 1, characterized in that: The corrosive environment conditions described in step 1 S1 include the chemical composition, electrochemical composition and fluid characteristics of the liquid medium.

5. The method for predicting and analyzing corrosion failure of naval vessel equipment according to claim 1, characterized in that: The stress and relative movement of the structural connection or welding area in step 1 S1 include the relative position changes of various system parts under the action of environmental external forces or loads.

6. The method for predicting and analyzing corrosion failure of naval vessel equipment according to claim 1, characterized in that: The simulated environmental conditions in step 1 S2 are the environmental characteristics of each system part ai so as to provide the physical, chemical or mechanical conditions for the occurrence of the corresponding corrosion phenomenon.

7. The method for predicting and analyzing corrosion failure of naval vessel equipment according to claim 1, characterized in that: In step three, the method for obtaining the statistical law of corrosion failure through numerical analysis is to use the least squares method for analysis.

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