Structural damage monitoring-protection joint planning method for hierarchical corrosion control

Abstract

The application provides a structure damage monitoring-protection combined planning method for hierarchical corrosion control, and the steps are: 1. constructing a generalized corrosion damage acceleration effect model of the equipment structure; 2. self-adaptive evaluation and prediction of the failure risk of the equipment structure; and 3. optimization of the hierarchical control strategy of the equipment structure damage monitoring-protection. According to the corrosion amount monitoring data, the application can self-adaptively identify the corrosion process of the equipment structure, the required data is easy to obtain, the identification method and process are simple and fast, the model parameters can be accurately estimated, the effective prediction of the failure risk of the equipment structure is realized, technical support is provided for ensuring the reliability and safety of the equipment structure under the corrosion acceleration failure, the damage monitoring and preventive maintenance interval time of the equipment structure are jointly optimized, the service economy of the equipment structure under the corrosion environment can be significantly improved, the method is scientific, the application objects include but are not limited to mechanical equipment running under the corrosion environment, and the method has wide popularization and application value.

Description

Technical Field

[0001] The present invention provides a structural damage monitoring and protection joint planning method for hierarchical corrosion control, namely, a method for adaptive risk assessment and hierarchical protection strategy optimization for structures with accelerated failure due to corrosion damage. This method is based on the concept of process adaptive identification and strategy joint optimization. For structures with accelerated failure due to corrosion damage, a generalized corrosion damage acceleration effect model is constructed based on regular monitoring data of the generalized corrosion amount of the structure. This method realizes adaptive identification of the corrosion process of the equipment structure and adaptive assessment and prediction of the failure risk. Based on the prediction results, the damage monitoring and protection hierarchical control strategy is jointly optimized, thereby improving the safety and service economy of the structure in a corrosive environment. This method is suitable for the field of structural damage monitoring and protection optimization in the presence of accelerated failure due to corrosion. Background Art

[0002] In harsh corrosive environments like the ocean, equipment structures frequently experience surface corrosion, such as corrosion on ships, corrosion at structural connection points, and rust at valve interfaces. This often leads to reduced thickness and strength, and in severe cases, even structural fracture. These corrosion phenomena are exacerbated by complex environmental factors such as high temperature, high humidity, high salt spray, strong ultraviolet radiation, and sulfur oxide pollution. These corrosion phenomena not only shorten the service life of equipment but also increase the risk of failure, posing a serious threat to safety and potentially resulting in significant economic losses. Therefore, developing effective damage monitoring and protection hierarchical control strategies to accurately predict the failure risk of equipment in harsh corrosive environments is of great practical significance for ensuring the safety of equipment structures and extending their service life.

[0003] However, existing monitoring and protection methods have some obvious limitations: on the one hand, traditional damage monitoring methods usually only judge the safety of the structure based on the corrosion depth, ignoring the accelerating effect of corrosion damage on the failure process, and are unable to comprehensively assess the risk of equipment; on the other hand, existing protection measures often adopt a regular maintenance method, failing to fully consider the specific impact of different corrosion types and environmental conditions on the protection plan, resulting in insufficient accuracy and effectiveness of the protection measures, and unable to achieve the expected protection effect in actual application.

[0004] To address these issues, this study proposes a joint structural damage monitoring and protection planning method for hierarchical corrosion control. Based on regular monitoring data of generalized structural corrosion, this method constructs a generalized corrosion damage acceleration effect model, enabling adaptive identification of the corrosion process of equipment structures and adaptive assessment and prediction of failure risks. The method then jointly optimizes the hierarchical damage monitoring and protection control strategy based on the prediction results. By jointly optimizing monitoring and protection measures, this method can provide more accurate protection plans for equipment structures in harsh corrosive environments, effectively reducing failure risks and extending the service life of equipment structures. This provides more reliable technical support for corrosion failure protection of equipment structures in harsh corrosive environments such as the ocean. Summary of the Invention

[0005] (1) The purpose of the present invention is to address the problem of how to carry out damage monitoring and protection optimization for structures with accelerated corrosion failure. Based on the regular monitoring data of the generalized corrosion amount of the structure, a generalized corrosion damage acceleration effect model is constructed to achieve adaptive identification of the corrosion process of the equipment structure and adaptive assessment and prediction of the failure risk. The damage monitoring-protection hierarchical control strategy is jointly optimized based on the prediction results, thereby improving the safety and service economy of the structure under corrosive environment.

[0006] (2) Technical solution:

[0007] The present invention provides a structural damage monitoring and protection joint planning method for hierarchical corrosion control, which is implemented through the following three steps:

[0008] Step 1: Construct a generalized corrosion damage acceleration effect model for equipment structures

[0009] Surface corrosion of equipment structures often leads to reduced thickness and strength, and in severe cases, even structural fracture. These corrosion phenomena are exacerbated by complex environmental factors such as high temperature, high humidity, high salt fog, strong ultraviolet radiation, and sulfur oxide pollution. They not only shorten the service life of equipment but also increase the risk of failure. Therefore, based on the phenomenon of corrosion-accelerated failure, this paper constructs a generalized corrosion damage acceleration effect model to describe the impact of corrosion on the failure rate of equipment structures, as shown below:

[0010]

[0011] Where t is the age of the equipment structure since the last maintenance; x t is the generalized corrosion amount of the equipment structure at age t; λ(t,x t) is the failure rate of the equipment structure at age t considering the accelerated generalized corrosion damage; λ0(t) is the baseline failure rate of the equipment structure at age t, which can usually be expressed by the failure rate function of the Weibull distribution; exp(·) is the natural exponential function; (α, β, t0, ξ) are all model parameters, where α and β are the shape parameter and scale parameter of the Weibull distribution, respectively, t0 is the starting time of equipment structure failure, and ξ is the corrosion damage acceleration factor.

[0012] Generalized corrosion amount x t This can be obtained by regularly conducting damage monitoring on the equipment structure, including the corrosion depth of the equipment structure, the diameter of the corrosion hole, the resistance change value, the mass change value, the breakdown potential change value, the maintenance current change value, etc. t The time-varying process is mainly fitted using the following two processes:

[0013] (1) Power-law process,

[0014] (2) Weibull process,

[0015] Among them, x ∞ It represents the corrosion limit of the equipment structure after long-term corrosion, which can usually be directly obtained from the corrosion test data of the equipment structure; (k1, k2) are model parameters. For the power-law process, k1 and k2 are the corrosion rate and corrosion index respectively. For the Weibull process, k1 and k2 are the corrosion shape parameter and corrosion scale parameter respectively.

[0016] Combining equation (1), the generalized corrosion damage acceleration effect model of equipment structure can be obtained as follows:

[0017]

[0018] Step 2: Adaptive assessment and prediction of equipment structure failure risk

[0019] The failure rate of the equipment structure considering the accelerated generalized corrosion damage at age t is λ(t,x t ), according to the relationship between failure rate and failure probability, the failure risk probability of the equipment structure considering the accelerated generalized corrosion damage at age t is:

[0020]

[0021] Before calculating the equipment structure failure risk probability, we must first estimate the model parameters in equation (2), that is, estimate the parameters (α, β, t0, ξ, k1, k2), and then substitute equation (2) as the integrand into equation (3).

[0022] The estimated parameters (α, β, t0) can be based on the failure data of the equipment structure in a non-corrosive environment, that is, assuming that the equipment structure is in a non-corrosive environment at t1, t2, ..., t m If failure occurs at age

[0023]

[0024]

[0025] Among them, i is the index variable.

[0026] Before estimating the parameters (k1, k2), it is necessary to first clarify which type of process should be used to fit the corrosion process of the equipment structure. Therefore, the present invention provides an adaptive corrosion process discrimination method based on the regular monitoring data of the generalized corrosion amount of the structure, as shown below:

[0027] (1) If the corrosion process of the equipment structure belongs to a power law process

[0028] Equivalence Performing mathematical transformations yields:

[0029]

[0030] Where ln(θ) is the natural logarithm function. Let y = lnx t ,x=ln(t-t0),a=k2,b=lnk1, equation (6) can be transformed into y=ax+b, where (x t ,t) can be obtained through regular corrosion monitoring of the equipment structure, so the estimated parameters (k1, k2) can be converted into estimated parameters (a, b), which can be achieved by the least squares method of univariate linear regression.

[0031] (2) If the equipment structure corrosion process belongs to the Weibull process

[0032] Equivalence Performing mathematical transformations yields:

[0033]

[0034] Similarly, x=ln(t-t0),a=k2,b=-k2lnk1,

[0035] Equation (7) can be transformed into y=ax+b, where (x t ,t) can be obtained through regular corrosion monitoring of the equipment structure, so the estimated parameters (k1, k2) can be converted into estimated parameters (a, b), which can be achieved by the least squares method of univariate linear regression.

[0036] In summary, in obtaining regular corrosion monitoring data of equipment structure (x t ,t), we can set x=ln(t-t0), y1=lnx t , Calculate the correlation coefficient between (x,y1) and (x,y2), that is:

[0037]

[0038] Where Cov(x,y) is the covariance of (x,y), and D(x) and D(y) are the variances of x and y respectively.

[0039] The type of corrosion process in the device structure is adaptively determined based on the proximity of the correlation coefficients r1 and r2 to 1. If r1 is closer to 1, the device structure corrosion process is a power-law process; if r2 is closer to 1, the device structure corrosion process is a Weibull process. After determining the type of corrosion process in the device structure, the parameters (k1, k2) can be estimated by estimating the parameters (a, b) using the least squares method of univariate linear regression.

[0040] The estimated parameter ξ needs to be based on the failure data of the equipment structure in the corrosive environment, that is, assuming that the equipment structure is in the corrosive environment at t1, t2, ..., t n If failure occurs at age, the maximum likelihood estimation function can be constructed:

[0041]

[0042] Substituting the estimated values ​​of the parameters (α, β, t0, k1, k2), we can get the estimated value of the parameter ξ by maximizing lnL(ξ):

[0043]

[0044] Get the estimated values ​​of all parameters (α, β, t0, ξ, k1, k2) Then, substituting it into equations (3) and (2) can realize the adaptive evaluation and prediction of the equipment structure failure risk probability.

[0045] Step 3: Equipment structure damage monitoring - protection level control strategy optimization

[0046] In order to cope with the influence of environmental corrosion on accelerating the failure of equipment structure, it is necessary to monitor the corrosion of equipment structure and install coating protection at intervals of τ. The coating life time follows the normal distribution N(τ,σ 2), during which the equipment structure will not corrode or worsen. Maintenance of equipment structures includes preventive maintenance and corrective replacement. The former is maintenance when the equipment structure has not failed, and the latter is maintenance when the equipment structure has failed. Both can reset the failure risk of the equipment structure to its initial state. Preventive maintenance can reduce sudden failures and reduce downtime. Therefore, it is necessary to set appropriate corrosion monitoring intervals τ and preventive maintenance intervals kτ to carry out equipment structure damage monitoring-protection graded control strategy optimization, thereby improving the safety and service economy of the structure in a corrosive environment.

[0047] Therefore, the present invention considers the failure risk probability of accelerated generalized corrosion damage based on the equipment structure and aims to minimize the protection cost of structural equipment. A damage monitoring-protection hierarchical control strategy optimization method is provided as follows:

[0048] First, the protection cost rate model of the equipment structure is established as follows:

[0049]

[0050] Among them, c d The cost of carrying out a corrosion monitoring for the equipment structure; c c Cost of adding a coating to the equipment structure; c p The cost of a preventive maintenance of the equipment structure; c r The cost of a corrective replacement of the equipment structure; P p (kτ) is the probability that the equipment structure will not fail at the age of kτ; P r (kτ) is the probability of failure of the equipment structure at the age of kτ.

[0051] Then calculate the probability P that the equipment structure will not fail at the age kτ p (kτ) is:

[0052]

[0053] Where Φ(·) is the cumulative function of the standard normal distribution, Φ(0) = 0.5, Φ(1) = 0.8413; is the density function of the standard normal distribution.

[0054] Correspondingly, the probability of failure of the equipment structure at kτ age is P r (kτ) is:

[0055]

[0056] Substituting equations (12) and (13) into equation (11), we can obtain the protection cost rate expression C(k, τ) of the equipment structure. By minimizing C(k, τ), we can obtain the optimal damage monitoring interval and preventive maintenance interval, thus completing the optimization of the damage monitoring-protection hierarchical control strategy.

[0057] Through the above steps, the present invention realizes the construction of a generalized corrosion damage acceleration effect model based on multi-source structural corrosion and failure data, carries out adaptive identification of structural corrosion processes and adaptive assessment and prediction of failure risks, formulates the optimal damage monitoring-protection hierarchical control strategy, and solves the problem of how to scientifically carry out corrosion hierarchical control on structures with corrosion-accelerated failures.

[0058] (3) Advantages and effects:

[0059] 1. The present invention can adaptively identify the corrosion process of equipment structures based on generalized corrosion monitoring data. The required data is easy to obtain, and the identification method and process are simple and fast.

[0060] 2. The present invention can accurately estimate model parameters and effectively predict the risk of equipment structure failure, providing technical support for ensuring the reliability and safety of equipment structure service under accelerated corrosion failure conditions;

[0061] 3. The present invention can significantly improve the service economy of equipment structures in corrosive environments by jointly optimizing damage monitoring and preventive maintenance intervals of equipment structures;

[0062] 4. The method of the present invention is scientific and its application objects include but are not limited to mechanical equipment operating in harsh corrosive environments. By changing the baseline failure rate, the method can be extended to various other types of equipment, and has broad promotion and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 The figure is a flow chart of the method of the present invention.

[0064] Figure 2 Fitting curve diagram for the corrosion process.

[0065] Figure 3 Failure risk probability curve. DETAILED DESCRIPTION

[0066] The present invention will be further described below with reference to examples.

[0067] Take a ship's sea valve that has been in service in the marine environment for a long time as an example. Since the ship's sea valve is exposed to the harsh environment of high-salt seawater for a long time, it faces serious corrosion risks. The corrosion of seawater on the valve will increase the risk of valve failure, thereby shortening the service life of the valve, posing a serious threat to the safety of the ship, and may cause huge economic losses.

[0068] Multiple corrosion sensors are typically installed on ship structures, including electrochemical corrosion sensors, ultrasonic sensors, and mass loss sensors. Each sensor monitors the generalized corrosion of a specific area. Electrochemical sensors detect changes in current on the steel surface, ultrasonic sensors measure changes in corrosion depth, and mass loss sensors record surface mass loss. By regularly acquiring this monitoring data, the generalized corrosion level of the equipment at different points in time can be determined, including corrosion depth, hole diameter, and resistance change.

[0069] In this example, ultrasonic sensors were installed on the ship's sea-going valves to regularly measure changes in the clearance between the internal disc and seat. After these measurements, the anti-corrosion coating was reapplied. As seawater corrodes the disc and seat, the gap between them continues to widen, ultimately causing the gap between the disc and seat to become too large to block seawater when the valve is closed. Because valves themselves experience wear and fatigue during normal operation, the continued increase in wear and fatigue cracks over long periods of use can also lead to liquid leakage at the valve joints. Therefore, it can be assumed that environmental corrosion damages the valve, significantly accelerating its failure.

[0070] Table 1. Valve clearance change data obtained from monitoring over a period of time

[0071]

[0072]

[0073] Table 2. Failure data of valves in corrosive and non-corrosive environments over a period of time

[0074]

[0075] Table 3. Costs of corrosion monitoring, coating, preventive maintenance, and corrective replacement for valves

[0076]

[0077] Table 1 shows the valve clearance change data obtained from monitoring over a period of time, which can be regarded as generalized corrosion monitoring data. Table 2 shows the valve failure data under corrosive and non-corrosive environments over a period of time. Table 3 shows the cost of corrosion monitoring, coating, preventive maintenance and corrective replacement of valves. Based on these data, the present invention provides a structural damage monitoring-protection joint planning method for hierarchical corrosion control, that is, an adaptive risk assessment and hierarchical protection strategy optimization method for accelerated structural failure caused by corrosion damage, see Figure 1 As shown, this method is implemented through the following three steps:

[0078] Step 1: Construct a generalized corrosion damage acceleration effect model for equipment structures

[0079] The following generalized corrosion damage acceleration effect model for equipment structure is constructed as the basic model for subsequent analysis:

[0080]

[0081] Step 2: Adaptive assessment and prediction of equipment structure failure risk

[0082] 1. Estimate the parameters (α, β, t0);

[0083] Based on the valve failure data in non-corrosive environments in Table 2, the estimated values ​​of the parameters (α, β, t0) are:

[0084]

[0085] 2. Estimate the parameters (k1, k2);

[0086] Based on the valve corrosion monitoring data in Table 1 (x t ,t) and the known corrosion limit value x ∞ =0.4, let x=ln(t-t0), y1=lnx t , Calculate the correlation coefficient between (x, y1) and (x, y2), and adaptively determine the type of corrosion process of the valve based on how close the correlation coefficient is to 1.

[0087] According to the calculation, the correlation coefficient of (x, y1) is 0.94227, and the correlation coefficient of (x, y2) is 0.88545. The fitting curve is as follows Figure 2 As shown. Therefore, it can be considered that the corrosion process of the valve is a power law type, and the estimated values ​​of the parameters are calculated as follows:

[0088]

[0089] 3. Estimate parameter ξ;

[0090] Based on the valve failure data under corrosive environment in Table 2, the estimated value of parameter ξ is:

[0091] 4. Failure risk assessment and prediction.

[0092] Get the estimated values ​​of all parameters (α, β, t0, ξ, k1, k2) After that, it can be substituted into equations (2) and (3) to realize the evaluation and prediction of valve failure risk probability. The failure risk probability curve is as follows: Figure 3 shown.

[0093] Step 3: Equipment structure damage monitoring - protection level control strategy optimization

[0094] All parameter estimates Substituting the maintenance cost data in Table 3 into equations (11), (12), and (13) yields the protection cost rate expression C(k, τ) for the equipment structure. By minimizing C(k, τ), we can obtain the optimal damage monitoring interval τ = 50 unit time, the optimal preventive maintenance interval kτ = 500 unit time, and the minimum maintenance cost rate C = 85.6595 unit cost. This means that the valve is monitored for corrosion and coated every 50 unit time, and preventive maintenance is performed every 500 unit time. If a valve fails during this period, it is immediately replaced.

[0095] The results show that the present invention can construct a generalized corrosion damage acceleration effect model based on multi-source structural corrosion and failure data, carry out adaptive identification of structural corrosion processes and adaptive assessment and prediction of failure risks, formulate an optimal damage monitoring-protection hierarchical control strategy, and solve the problem of how to scientifically carry out corrosion hierarchical control on structures with corrosion-accelerated failures, thus achieving the expected purpose.

[0096] In summary, the present invention targets structures with accelerated failure due to corrosion damage. Based on the regular monitoring data of the generalized corrosion amount of the structure, a generalized corrosion damage acceleration effect model is constructed to achieve adaptive identification of the corrosion process of the equipment structure and adaptive assessment and prediction of the failure risk. The damage monitoring-protection hierarchical control strategy is jointly optimized based on the prediction results, providing theoretical and technical support for improving the safety and service economy of structures in corrosive environments.

Claims

1. A structural damage monitoring and protection joint planning method for hierarchical corrosion control, characterized by: The steps include: Step 1: Construct a generalized corrosion damage acceleration effect model for equipment structure; A generalized corrosion damage acceleration effect model is constructed to describe the impact of corrosion on the failure rate of equipment structures, as shown below: Where t is the age of the equipment structure since the last maintenance; x t is the generalized corrosion amount of the equipment structure at age t; λ(t,x t ) is the failure rate of the equipment structure at age t considering the accelerated generalized corrosion damage; λ0(t) is the baseline failure rate of the equipment structure at age t, expressed as the failure rate function of the Weibull distribution; exp(·) is the natural exponential function; (α, β, t0, ξ) are all model parameters, where α and β are the shape parameter and scale parameter of the Weibull distribution, respectively; t0 is the starting time of equipment structure failure; and ξ is the corrosion damage acceleration factor. The generalized corrosion damage acceleration effect model of equipment structure is: Step 2: Adaptive assessment and prediction of equipment structure failure risk The failure rate of the equipment structure considering the accelerated generalized corrosion damage at age t is λ(t,x t ), according to the relationship between failure rate and failure probability, the failure risk probability of the equipment structure considering the accelerated generalized corrosion damage at age t is: Before calculating the probability of equipment structure failure risk, we must first estimate the model parameters in equation (2), that is, estimate the parameters (α, β, t0, ξ, k1, k2), and then substitute equation (2) as the integrand into equation (3); Step 3: Optimize the equipment structure damage monitoring-protection hierarchical control strategy; The corrosion monitoring interval τ and preventive maintenance interval kτ are set to carry out equipment structure damage monitoring-protection hierarchical control strategy optimization to improve the safety and service economy of the structure under corrosive environment. The failure risk probability of accelerated generalized corrosion damage is considered based on the equipment structure, and the optimization is achieved with the goal of minimizing the protection cost of the structural equipment.

2. The structural damage monitoring and protection joint planning method for hierarchical corrosion control according to claim 1 is characterized by: In step 1, the generalized corrosion amount x t The corrosion amount x is obtained by regularly conducting damage monitoring on the equipment structure. t The time-varying process is fitted using the following two procedures: (1) Power-law process, (2) Weibull process, Among them, x ∞ It represents the corrosion limit of the equipment structure after long-term corrosion, which is directly obtained from the corrosion test data of the equipment structure; (k1, k2) are model parameters. For the power-law process, k1 and k2 are the corrosion rate and corrosion index, respectively. For the Weibull process, k1 and k2 are the corrosion shape parameter and corrosion scale parameter, respectively.

3. The structural damage monitoring and protection joint planning method for hierarchical corrosion control according to claim 1 is characterized by: In step 2, the estimated parameters (α, β, t0) are based on the failure data of the equipment structure in a non-corrosive environment. Assume that the equipment structure is in a non-corrosive environment at t1, t2, ..., t m If failure occurs at age Among them, i is the index variable.

4. The structural damage monitoring and protection joint planning method for hierarchical corrosion control according to claim 1 or 3 is characterized by: Before estimating the parameters (k1, k2), the corrosion process of the device structure is clarified and the fitting process is as follows: (1) If the corrosion process of the equipment structure belongs to a power law process Equivalence Perform mathematical transformation to obtain: lnxt=lnk1+k2ln(t-t0)(6) Where ln() is the natural logarithm function; let y = lnx t ,x=ln(t-t0),a=k2,b=lnk1, convert equation (6) into y=ax+b, where (x t ,t) is obtained through regular corrosion monitoring of the equipment structure, so the estimated parameters (k1, k2) are converted into estimated parameters (a, b) and implemented using the least squares method of univariate linear regression; (2) If the equipment structure corrosion process belongs to the Weibull process Equivalence Perform mathematical transformation to obtain: Similarly, x=ln(t-t0),a=k2,b=-k2lnk1, Convert equation (7) to y=ax+b, where (x t ,t) is obtained through regular corrosion monitoring of the equipment structure, so the estimated parameters (k1, k2) are converted into estimated parameters (a, b) using the least squares method of univariate linear regression.

5. The structural damage monitoring and protection joint planning method for hierarchical corrosion control according to claim 4 is characterized by: In obtaining regular corrosion monitoring data of equipment structure (x t ,t), let x=ln(t-t0), y1=lnx t , Calculate the correlation coefficient between (x,y1) and (x,y2), that is: Where Cov(x,y) is the covariance of (x,y), and D(x) and D(y) are the variances of x and y respectively.

6. The structural damage monitoring and protection joint planning method for hierarchical corrosion control according to claim 5 is characterized by: The type of corrosion process of the equipment structure is adaptively determined based on the degree of proximity of the correlation coefficients r1 and r2 to 1: if r1 is closer to 1, the corrosion process of the equipment structure is a power-law process; if r2 is closer to 1, the corrosion process of the equipment structure is a Weibull process; after determining the type of corrosion process of the equipment structure, the parameters (a, b) are estimated by the least squares method of univariate linear regression to achieve estimation of the parameters (k1, k2).

7. The structural damage monitoring and protection joint planning method for hierarchical corrosion control according to claim 1 is characterized by: The estimated parameter ξ needs to be based on the failure data of the equipment structure in the corrosion environment. Assume that the equipment structure is in the corrosion environment at t1, t2, ..., t n If failure occurs at age, the maximum likelihood estimation function is constructed: Substituting the estimated values ​​of the parameters (α, β, t0, k1, k2), we can get the estimated value of the parameter ξ by maximizing lnL(ξ): Get the estimated values ​​of all parameters (α, β, t0, ξ, k1, k2) Then, it is substituted into equations (3) and (2) to realize the adaptive evaluation and prediction of the equipment structure failure risk probability.

8. The structural damage monitoring and protection joint planning method for hierarchical corrosion control according to claim 1 is characterized by: In step three, the protection cost rate model of the equipment structure is established as: Among them, c d The cost of carrying out a corrosion monitoring for the equipment structure; c c Cost of adding a coating to the equipment structure; c p The cost of a preventive maintenance of the equipment structure; c r The cost of a corrective replacement of the equipment structure; P p (kτ) is the probability that the equipment structure will not fail at the age of kτ; P r (kτ) is the probability of failure of the equipment structure at the age of kτ.

9. The structural damage monitoring and protection joint planning method for hierarchical corrosion control according to claim 8 is characterized by: Calculate the probability P that the device structure will not fail at the age kτ p (kτ) is: Where Φ(·) is the cumulative function of the standard normal distribution, Φ(0) = 0.5, Φ(1) = 0.8413; is the density function of the standard normal distribution.

10. The structural damage monitoring and protection joint planning method for hierarchical corrosion control according to claim 9 is characterized in that: In step 1, the probability P of the equipment structure failing at kτ age is r (kτ) is: Substituting equations (12) and (13) into equation (11), we obtain the protection cost rate expression C(k,τ) of the equipment structure. By minimizing C(k,τ), we can obtain the optimal damage monitoring interval and preventive maintenance interval, thus completing the optimization of the damage monitoring-protection hierarchical control strategy.

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