JP7084344B2 - Corrosion rate estimation device, corrosion rate estimation method and program - Google Patents

Description

The present invention relates to a corrosion rate estimation device, a corrosion rate estimation method and a program.

Galvanic steel used for steel towers and aluminum-based electric wires used for power transmission lines are corroded inside where dissimilar metals come into contact with each other, making visual corrosion inspection difficult. In addition, since steel towers and transmission lines have a large number of facilities and are installed over a wide area, it is necessary to formulate a rational renovation plan in consideration of the aging situation. For this reason, research is underway on methods for highly accurate evaluation of the effects of corrosion on steel towers and transmission lines. For example, Patent Document 1 discloses a method for evaluating the remaining life of zinc-based plating equipment by creating a correlation diagram between the corrosion rate of zinc-based plating equipment and the glossiness or weight increase of copper pieces. There is.

JP-A-2017-106785

The method of Patent Document 1 does not consider the influence of the surrounding environment, which differs depending on the installation location of the tower or transmission line, on the corrosion of the tower or transmission line, and there is room for improvement in terms of the accuracy of the estimated corrosion rate. .. And, such a problem is not limited to the case of a steel tower or a transmission line, and also exists in the case of estimating the corrosion rate of other metal materials installed in an outdoor corrosive environment or the like, for example.

The present invention has been made based on such a background, and provides a corrosion rate estimation device, a corrosion rate estimation method, and a program capable of quantifying the corrosion rate of a metal material in consideration of the influence of the surrounding environment. The purpose is to provide.

In order to achieve the above object, the corrosion rate estimation device according to the first aspect of the present invention is
By performing multiple regression analysis with the corrosion rate of the metal material as the objective variable and the corrosive environment factors including the annual amount of sea salt particles flying in the year, the wetting time, and the dry-wet cycle that affect the corrosion of the metal material as the explanatory variables. An estimation formula creation method for creating an estimation formula for the corrosion rate of metal materials,
A corrosion rate estimation means for estimating the corrosion rate of the metal material by inputting a corrosion environment factor including an annual amount of sea salt particles, a wetting time, and a dry / wet cycle into the estimation formula.
Equipped with
The dry-wet cycle is the number of cycles in which a wet state for a predetermined time or longer and a dry state for a predetermined time or longer are alternately continued.

The estimation formula may be as shown in the following formula.
Y = α + β ₁ X ₁ + β ₂ X ₂ + β ₃ X ₃
However, Y is the corrosion rate of the metal material, X ₁ is the amount of sea salt particles flying annually, X ₂ is the wetting time, X ₃ is the dry / wet cycle, α is a constant, and β ₁ to β ₃ are coefficients.

The annual amount of sea salt particles, the wetting time, and the dry / wet cycle in the estimation formula creating means may all be simulation values obtained from the meteorological analysis code.

The metal material is a steel member constituting a steel tower.
The corrosion rate may be a reduction in the galvanized thickness applied to the steel member per year.

The metal material is an aluminum wire constituting a power transmission line.
The corrosion rate may be the amount of decrease in the tensile strength of the aluminum wire per year.

For the wetting time and the dry-wet cycle, a plurality of defined patterns may be prepared, and the defined pattern with the best analysis result may be selected when the multiple regression analysis is performed.

A corrosion rate map creating means may be provided for creating a corrosion rate map showing the corrosion rate of the metal material in a predetermined area based on the estimated corrosion rate of the metal material.

In order to achieve the above object, the corrosion rate estimation method according to the second aspect of the present invention is:
By performing multiple regression analysis with the corrosion rate of the metal material as the objective variable and the corrosive environment factors including the annual amount of sea salt particles flying in the year, the wetting time, and the dry-wet cycle that affect the corrosion of the metal material as the explanatory variables. An estimation formula creation step to create an estimation formula for the corrosion rate of metal materials, and
A corrosion rate estimation step for estimating the corrosion rate of the metal material by inputting a corrosion environment factor including an annual amount of sea salt particles, a wetting time, and a dry / wet cycle into the estimation formula.
Including
The dry-wet cycle is the number of cycles in which a wet state for a predetermined time or longer and a dry state for a predetermined time or longer are alternately continued.

In order to achieve the above object, the program according to the third aspect of the present invention is
Computer,
By performing multiple regression analysis with the corrosion rate of the metal material as the objective variable and the corrosive environment factors including the annual amount of sea salt particles flying in the year, the wetting time, and the dry-wet cycle that affect the corrosion of the metal material as the explanatory variables. An estimation formula creation method for creating an estimation formula for the corrosion rate of metal materials,
A corrosion rate estimation means for estimating the corrosion rate of the metal material by inputting the corrosion environment factors including the annual amount of sea salt particles, the wetting time and the dry / wet cycle into the estimation formula.
It is a program to function as
The dry-wet cycle is the number of cycles in which a wet state for a predetermined time or longer and a dry state for a predetermined time or longer are alternately continued.

INDUSTRIAL APPLICABILITY According to the present invention, it is possible to provide a corrosion rate estimation device, a corrosion rate estimation method and a program capable of quantifying the corrosion rate of a metal material in consideration of the influence of the surrounding environment.

It is a block diagram which shows the hardware composition of the corrosion rate estimation apparatus which concerns on embodiment of this invention. (A) is an example of an annual sea salt particle amount data table, (b) is an example of a wet time data table, (c) is an example of a dry / wet cycle data table, and (d) is an example of a corrosion record data table. It is a figure which shows an example. (A) to (c) are diagrams for explaining a specific example of the method of totaling the dry / wet cycle. It is a figure for demonstrating the explanatory variables of the annual amount of sea salt particles, the wetting time, and the dry-wet cycle. It is a flowchart which shows the flow of the corrosion rate estimation process which concerns on embodiment of this invention. It is a flowchart which shows the flow of the estimation formula making process which concerns on embodiment of this invention. It is a flowchart which shows the flow of the outlier exclusion processing which concerns on embodiment of this invention. It is a figure which shows the result of the multiple regression analysis in Example 1. FIG. It is a figure which shows the steel tower corrosion rate map in Example 1. FIG. It is a figure which shows the result of the multiple regression analysis in Example 2. FIG. It is a figure which shows the electric wire corrosion rate map in Example 2. FIG.

Hereinafter, embodiments of the corrosion rate estimation device, the corrosion rate estimation method, and the program according to the present invention will be described in detail with reference to the drawings. In each drawing, the same or equivalent parts are designated by the same reference numerals. Hereinafter, a case of calculating the corrosion rate of a steel tower will be described as an example.

In the corrosion rate estimation method according to the embodiment, an estimation formula for obtaining the corrosion rate of the steel tower from the corrosion environment factor is created based on the pair of the corrosion rate of the steel tower and the corrosion environment factor, and the created estimation formula is used for each point. The corrosion rate of the steel tower at each point is calculated by substituting the measured value of the corrosion environment factor at the position.

Multiple regression analysis is used with the corrosion rate as the objective variable and the corrosion environment factor as the explanatory variable to create the estimation formula for determining the corrosion rate of the tower. In the multiple regression analysis, a multiple regression equation representing the objective variable Y is created using a plurality of explanatory variables X ₁ to X _n . The multiple regression equation is expressed by the following equation (1), where α is a constant and β ₁ to β _n are coefficients.
Y = α + β ₁・ X ₁ + β ₂・ X ₂ ＋… β _n・ X _n … (1)

In the corrosion rate estimation method according to the embodiment, the corrosion rate, which is the objective variable Y, and the corrosive environment factors, which are the explanatory variables X1 to _Xn corresponding to the objective variable Y, are acquired at _a plurality of locations and acquired at a plurality of locations. By determining the constant α and the coefficients β ₁ to β _n based on the objective variable Y and the explanatory variables X ₁ to X _n , an estimation formula (multiple regression equation) expressing the corrosion rate of the steel tower is created.

Corrosion influencing factors are environmental factors that affect the corrosion of metallic materials and include, for example, the annual amount of sea salt particles, wetting time, and wet / dry cycle. The corrosive environmental factor is a simulation value obtained from the meteorological analysis code. The corrosive environmental factor is not limited to the simulated value, and may be, for example, an actually measured value obtained by direct observation using an observation aircraft.

Corrosion record data is data showing the degree of corrosion of members when collected from an outdoor installation. For example, when the outdoor installation is a steel tower, the corrosion record data is the thickness (μm) of the zinc plating at the time of collecting the steel tower member. Based on the actual corrosion data, the corrosion rate, which is an index showing the degree of corrosion of the target member progressing in one year, can be calculated. The corrosion rate (μm / year) of a member of a steel tower is calculated by dividing the difference between the time of collection and the thickness of zinc plating from the initial thickness of zinc plating by the number of years of installation of the member.

The estimation formula (1) for the corrosion rate Y of the steel tower is expressed by the following formula (2), where the explanatory variables are the annual amount of sea salt particles X ₁ , the wetting time X ₂ , and the wet / dry cycle X ₃ .
Y = α + β ₁・ X ₁ + β ₂・ X ₂ + β ₃・ X ₃ … (2)

Next, the hardware configuration of the corrosion rate estimation device 1 according to the embodiment will be described. FIG. 1 is a block diagram showing a hardware configuration of the corrosion rate estimation device 1 according to the embodiment.

The corrosion rate estimation device 1 is, for example, a general-purpose computer. The corrosion rate estimation device 1 includes an operation unit 11, a display unit 12, a communication unit 13, a storage unit 14, and a control unit 15. Each part of the corrosion rate estimation device 1 is connected to each other by an internal bus or the like.

The operation unit 11 receives a user's instruction and supplies an operation signal corresponding to the accepted operation to the control unit 15. The operation unit 11 includes, for example, a keyboard and a mouse.

The display unit 12 displays various images to the user based on the image data supplied from the control unit 15. The display unit 12 displays, for example, the estimation result of the corrosion rate at each point.

The communication unit 13 is an interface provided with an antenna or the like and capable of connecting to a communication network such as the Internet. The communication unit 13 communicates with an external terminal, a server, a memory, or the like via a communication network such as the Internet.

The storage unit 14 includes a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, a hard disk drive, and the like. The storage unit 14 stores programs and various data to be executed in the control unit 15. Further, the storage unit 14 functions as a work memory for the control unit 15 to execute the process.

Further, the storage unit 14 includes an annual flying sea salt particle amount data table 141, a wet time data table 142, a dry / wet cycle data table 143, and a corrosion record data table 144. Each data table 141 to 144 stores the annual amount of sea salt particles flying in the sea, the wetting time, and the dry / wet cycle in association with the position information. The corrosion record data table 144 stores the corrosion record data in association with the position information of the tower and the information regarding the acquisition date and time of the data.

FIG. 2A is an example of the annual sea salt particle amount data table 141. The annual sea salt particle amount data table 141 stores, for example, the annual sea salt particle amount data for each region divided by a mesh of 5 km × 5 km. The annual amount of sea salt particles flying is the average value of the amount of sea salt particles (μg / m ² s) flying in one year. The annual amount of sea salt particles is calculated by, for example, using the analysis code NuWiCC-ST developed by the Central Research Institute of Electric Power Industry, setting calculation conditions such as airflow and salinity, and executing a simulation.

FIG. 2B is an example of the wetting time data table 142. The wet time data table 142 stores, for example, wet time data for each region divided by a mesh of 5 km × 5 km. According to ISO (International Organization for Standardization) 9223, the wetting time is a time (hour / year) at a temperature of 0 ° C. or higher and a relative humidity of 80% or higher. The wetting time is determined, for example, based on the analysis code NuWFAS developed by the Central Research Institute of Electric Power Industry.

FIG. 2C is an example of the dry / wet cycle data table 143. The wet / dry cycle data table 143 stores the dry / wet cycle data for each region divided by a mesh of, for example, 5 km × 5 km. In the dry-wet cycle, in general, a state where the temperature is 0 ° C. or higher and a relative humidity of 80% or more (wet state) and a state other than the wet state (dry state) are alternately performed for a predetermined time (for example, 12 hours) or more. It is the number of annual cycles (cycle / year) with the case of continuation as one cycle. The wet-wet cycle is determined, for example, based on the analysis code NuWFAS as well as the wetting time.

FIG. 2D is an example of the corrosion actual data table 144. The corrosion record data table 144 stores the zinc plating thickness (μm) of the member at the acquisition date and time of the data which is the corrosion record data in association with the information regarding the position where the member was installed and the information regarding the data acquisition date and time.

FIG. 3 is a diagram showing a specific example of a method for tabulating the dry / wet cycle. A plurality of vertical lines extending vertically from the horizontal lines in FIGS. 3 (a) to 3 (c) mean an hourly time division. In the case of (a), the time during which the wet state continues in the dry-wet cycle (defined time) is 12 hours or more, and the time during which the dry state continues in the subsequent dry-wet cycle (undefined time) is 12 hours or more. , The dry / wet cycle is one cycle. On the other hand, in the case of (b) and (c), the dry-wet cycle is zero because the defined time or the undefined time is less than 12 hours.

FIG. 4 is a diagram for explaining the explanatory variables of the annual amount of sea salt particles, the wetting time, and the dry / wet cycle. The annual amount of sea salt particles is uniquely defined because it is a physical quantity called the amount of particles. However, various definitions can be made for the wetting time and the dry-wet cycle, and the corrosion rate can be estimated accurately. In order to do so, it is necessary to make an appropriate definition without being bound by a general definition. Therefore, regarding the wetting time and the dry-wet cycle, the above-mentioned general definitions are extended to use the nine types of wet time and the dry-wet cycle shown in FIG. 4, respectively, as explanatory variables.

The wetting time is taken into consideration not only in relative humidity but also in the case of precipitation of 0 mm or more, and the dry-wet cycle includes not only the number of repeated cycles of wet and dry states, but also the period of precipitation of 0 mm or more and the period of precipitation of 0 mm. The number of repeated cycles of is also taken into consideration. Therefore, in the following processing, multiple regression analysis is executed using a combination (pattern) of 9 explanatory variables of wet time and 9 explanatory variables of the dry-wet cycle, in other words, a total of 81 combinations of explanatory variables. Will be done. The number of combinations of explanatory variables is not limited to 81 in total, and appropriate combinations of explanatory variables can be created.

Returning to FIG. 1, the control unit 15 includes a CPU (Central Processing Unit) and the like, and controls each unit of the corrosion rate estimation device 1. By executing the program stored in the storage unit 14, the control unit 15 executes the corrosion rate estimation process of FIG. 5, the estimation formula creation process of FIG. 6, and the outlier exclusion process of FIG. 7.

The control unit 15 functionally includes an estimation formula creation unit 151, a corrosion rate estimation unit 152, and a corrosion rate map creation unit 153.

The estimation formula creation unit 151 performs a multiple regression analysis based on the corrosion record data of the tower member stored in the storage unit 14 and a pair of a plurality of types of corrosion environment factors, so that the tower can be converted from a plurality of types of corrosion environment factors. Create an estimation formula to calculate the corrosion rate.

The estimation formula creation unit 151 performs multiple regression analysis using a total of 81 combinations of explanatory variables, and selects the most appropriate wet time and wet / wet cycle explanatory variables from the total of 81 combinations of explanatory variables. Specifically, from a total of 81 combinations of explanatory variables, the combination of the explanatory variables of the wet time and the wet-wet cycle with the highest significance probability of zero and the highest coefficient of determination ^R2 is selected.

The coefficient of determination R ² is an index showing the goodness of the fit of the regression model. The coefficient of determination R ² is within the range of 0 ≦ R ² ≦ 1, and the closer it is to 1, the higher the accuracy of the created multiple regression equation. The significance probability, also called a P-value, is an index used for the significance test of the coefficient of determination. The significance probability can be judged to be significant as it approaches zero, and generally, it can be judged to be significant when it is smaller than 0.05.

From the pair of objective variable and explanatory variable including the most accurate combination of explanatory variables, the estimation formula creation unit 151 obtains three indexes of DfBetas, Cook distance, and lever ratio used for evaluating the influence of outliers. The outliers are excluded and the multiple regression analysis is performed, and the result of the multiple regression analysis using the index with the best result among the three indicators is selected.

DfBetas represents the change in the regression coefficient when one piece of data is removed. When the absolute value of DfBetas exceeds 2 / √N (N is the total number of samples), the data is considered outlier.

The Cook distance represents the distance related to the discrepancy between the case where all the data are used and the case where the predicted value by the regression equation obtained after excluding one data is used. If the distance of Cook is large, it means that one of the excluded data has a great influence on the predicted value by the regression equation.

The lever ratio represents the amount of change in the predicted value when the value of the objective variable Y is changed by 1 without changing the data of the explanatory variables for each observation point (sample) of the regression analysis. When the leverage ratio is large, it means that the data in which the value of the objective variable Y is changed by 1 has a great influence on the predicted value by the regression equation.

The corrosion rate estimation unit 152 estimates the corrosion rate of the tower member at each point by substituting the corrosion environmental factor at each point into the estimation formula created by the estimation formula creation unit 151.

The corrosion rate map creating unit 153 creates a corrosion rate map based on the corrosion rate at each point estimated by the corrosion rate estimation unit 152. As shown in FIGS. 9 and 11, for example, the corrosion rate map is a map in which each point in the target area is displayed in a color divided according to the corrosion rate. The above is the hardware configuration of the corrosion rate estimation device 1.

FIG. 5 is a flowchart showing the flow of the corrosion rate estimation method according to the embodiment. Below, the data on the annual amount of sea salt particles X ₁ , the wetting time X ₂ , and the dry / wet cycle X ₃ acquired at multiple points, and the data on the corrosion rate Y acquired at each point where these data were acquired. Are already stored in the storage unit 14.

First, the control unit 15 reads out the annual flying sea salt particle amount X ₁ , the wetting time X ₂ , and the dry / wet cycle X ₃ which are explanatory variables from the storage unit 14 (step S1). For the wet time X ₂ and the dry / wet cycle X ₃ , data are acquired when the nine types of explanatory variables shown in FIG. 4 are used.

Next, the control unit 15 reads out the corrosion rate Y of the steel tower, which is the objective variable, from the storage unit 14 (step S2). For the corrosion rate Y of the iron tower, read out the zinc plating thickness at the time of collecting the iron tower member stored in the storage unit 14 and the initial thickness of the zinc plating, and divide the difference in the zinc plating thickness by the number of years of installation of the iron tower member. It is calculated by. The number of years of installation of the tower member is obtained by calculating the difference between the installation date and time of the tower member stored in the storage unit 14 and the collection date and time of the tower member.

Next, the estimation formula creation unit 151 executes multiple regression analysis based on the explanatory variables X1 to X3 of _each point acquired in step S1 and the objective variable Y of _each point acquired in step S2, and formulates the equation. By calculating the constant α and the variables β ₁ to β ₃ in (2), an estimation formula for the corrosion rate Y is created (step S3). Hereinafter, the process of creating the estimation formula in step S3 (estimation formula creation process) will be described with reference to the flowchart of FIG.

First, the estimation formula creation unit 151 executed multiple regression analysis for each of a total of 81 combinations of explanatory variables read from the storage unit 14, and the significance probability was zero and the coefficient of determination ^R2 was closest to 1. Select a combination of explanatory variables (step S31).

Next, the estimation formula creation unit 151 executes a process of excluding outliers from the actual corrosion data (step S32). Hereinafter, the process of excluding the outliers in step S32 (outlier exclusion process) will be described with reference to the flowchart of FIG. 7.

FIG. 7 is a flowchart showing a flow of outlier exclusion processing of corrosion actual data executed by the estimation formula creation unit 151. First, the estimation formula creation unit 151 performs multiple regression analysis based on all the corrosion record data (number of data: N) read from the storage unit 14 (step S321).

Next, the estimation formula creation unit 151 calculates DfBetas of the actual corrosion data, excludes α outliers due to the calculated DfBetas, and excludes the outliers due to DfBetas (number of data: N-α). ) Is performed (step S322).

Next, the estimation formula creation unit 151 calculates the Cook distance of the corrosion actual data, excludes the calculated outliers due to the Cook distance to the top α, and excludes the outliers due to the Cook distance. Multiple regression analysis is performed based on (number of data: N—α) (step S323).

Next, the estimation formula creation unit 151 calculates the lever ratio of the corrosion actual data, excludes the calculated outliers due to the iron ratio to the top α, and excludes the outliers due to the iron ratio (number of data). : N—α) is used for multiple regression analysis (step S324).

Next, the estimation formula creation unit 151 compares each coefficient of determination ^R2 obtained as a result of the multiple regression analysis executed in steps S322 to S324, and the outliers in which the coefficient of determination ^R2 is closest to 1. An index is selected, and outliers when the index is used are excluded from the actual corrosion data (step S325). The above is the flow of the process for excluding the outliers of the corrosion record data in step S32.

Returning to FIG. 6, the estimation formula creation unit 151 re-uses the corrosion actual data excluded by the outlier exclusion process in step S32 and the explanatory variables selected from a total of 81 types corresponding to the corrosion actual data. By executing regression analysis and calculating the constant α and the coefficients β ₁ to β ₃ of the equation (2), an estimation equation of the corrosion rate Y is created (step S33). The above is the estimation formula creation process in step S3.

Returning to FIG. 5, the corrosion rate estimation unit 152 inputs the explanatory variables X1 to X3 of each point stored in the data tables 141 to 143 into the corrosion rate estimation formula created in step S3 _, and inputs the explanatory variables X1 to X3 at _each location. The corrosion rate Y of the steel tower member at the point is estimated (step S4).

Next, the corrosion rate map creating unit 153 creates a corrosion rate map showing the distribution of the corrosion rate in a predetermined region based on the corrosion rate Y at each point estimated in step S4 (step S5). The corrosion rate map created in step S5 is displayed on the display unit 12 of the corrosion rate estimation device 1 and is used by the user to formulate a repair plan for the steel tower. The above is a series of steps of the corrosion rate estimation process.

The corrosion rate estimation device 1 according to the embodiment performs multiple regression analysis with the corrosion rate of the steel tower as the objective variable and the annual amount of sea salt particles flying in the sea, the wetting time, and the dry / wet cycle that affect the corrosion of the steel tower as explanatory variables. Therefore, it is configured to create an estimation formula for the corrosion rate of the steel tower. Therefore, the corrosion rate of the steel tower can be estimated accurately in consideration of the influence of the surrounding environment.

Hereinafter, the present invention will be specifically described with reference to examples. However, the present invention is not limited to these examples.

(Example 1)
In Example 1, a corrosion velocity map of the tower was created by performing multiple regression analysis based on the corrosion record data obtained from the members collected from the existing tower. For the multiple regression analysis, for example, statistical analysis software "Statistical Package for Social Science Ver.21" was used.

First, a method of measuring the zinc plating thickness at the time of collecting members, which is the actual corrosion data, will be described. Members collected from steel towers at multiple points were cut to a length of about 50 cm, photographs were taken, corrosion products were removed from the members, and the thickness of zinc plating containing corrosive organisms was measured with a magnetic film thickness meter. (150 locations / book). A film thickness meter supported by the XYZ drive arm was scanned on the cut member, and the film thickness was measured at 150 points on the member. The reason why the zinc plating thickness at 150 points per member was measured is to obtain the average value of the zinc plating thickness.

Next, the initial thickness of the zinc plating of the member was examined. The initial thickness of the zinc plating of the tower member is at least the specified value or more, but the actual thickness is unknown. Therefore, assuming that the initial thickness of zinc plating is the measured value in the part (wrapped part) that is estimated not to be affected by the corrosion of the member, there is a large difference between the corrosion rate calculation value by the estimation formula and the actual corrosion data. In the field, the initial value was assumed to be 100 μm.

FIG. 8 shows the results of multiple regression analysis based on the actual corrosion data of the tower member. Regardless of which index of DfBetas, Cook distance, and lever ratio was adopted, the same outliers were excluded. Therefore, the estimation formula of the corrosion rate was created by excluding only the outliers. In this case, the coefficient of determination R ² was 0.403 and the significance probability was 0.000.

The corrosion rate estimation formula for the steel tower is expressed by the following formula (3).
Corrosion rate of steel tower members (μm / year) = -0.774545 + 1.211769 x Annual amount of sea salt particles +0.000159 x Wetting time (ISO or precipitation 0 mm or more) +0.003600 x Wet and dry cycle (ISO or 0 ° C or more) , Precipitation 0 mm or more, 6h) ... (3)

FIG. 9 shows a tower corrosion rate map in which the corrosion rate is classified into three stages of high, medium, and slow in order to clearly classify the corrosion rate. The unit of the corrosion rate in FIG. 9 is μm / year, and the unit of the mesh is 5 km × 5 km. From the tower corrosion rate map, it can be understood that the tower corrosion rate tends to be faster on the Sea of Japan side and slower on the Pacific Ocean side and the Sea of Okhotsk side. It can also be understood that in some parts of Northern Hokkaido, Southern Hokkaido, and Central Hokkaido, the corrosion rate tends to be high even in the inland areas. It was also confirmed that these results are in agreement with the actual corrosion data.

The classification of the corrosion rate in FIG. 9 is the average value of the initial thickness of zinc plating, the life of zinc plating, the useful life of the electric business fixed assets of the steel tower, the thickness of the η layer which is a pure zinc layer in the cross-sectional structure of galvanization, etc. In consideration of, the settings are as follows.
Corrosion rate "fast" 1.49 μm / year or more Corrosion rate "medium" 0.66 μm / year to less than 1.49 μm / year Corrosion rate “slow” less than 0.66 μm / year

(Example 2)
In Example 2, a wire corrosion rate map was created by performing multiple regression analysis based on the corrosion record data obtained from the steel core aluminum stranded wire (electric wire) collected from the existing transmission line. The same statistical analysis software as in Example 1 was used for the multiple regression analysis.

The actual corrosion data of the electric wire is the tensile strength (MPa) of the aluminum wire collected from the steel core aluminum stranded wire. The reason why the tensile strength of the aluminum wire is used as the corrosion record data is that the aluminum wire is more likely to deteriorate due to aging and environmental factors than the steel wire, and the remaining life of the steel core aluminum stranded wire is estimated more appropriately. Because it can be done. Corrosion-proof wires coated with anti-corrosion ingredients are excluded from the actual corrosion data in order to avoid affecting the accuracy of the estimation formula.

In order to evaluate the corrosion record data of the electric wire on the safe side, the tensile test of the aluminum wire was executed multiple times, and the measured tensile strength was set to the minimum value. The corrosion rate (MPa / year) of the aluminum wire was calculated by dividing the difference between the initial tensile strength of the aluminum wire and the minimum value of the tensile strength at the time of the test by the number of years elapsed after the wire was installed. .. The tensile strength of the aluminum wire in the initial state was 1.2 times the standard value.

FIG. 10 shows the result of multiple regression analysis based on the actual corrosion data of the electric wire. Since the best analysis results were obtained when 6 outliers were excluded by the lever ratio, an estimation formula for the corrosion rate was created by excluding only these 6 outliers. In this case, the coefficient of determination R ² was 0.228 and the significance probability was 0.001.

The corrosion rate estimation formula for electric wires is expressed by the following formula (4).
Corrosion rate of electric wire (MPa / year) = -0.972796 + 5.169522 x Annual amount of sea salt particles +0.000172 x Wetting time (humidity 80% or more or precipitation 0 mm or more at temperature 0 ° C or higher) -0.039616 x Wet and dry cycle (temperature 0 ° C or higher, humidity 80% or higher or precipitation 0 mm or higher, 12h) ... (4)

FIG. 11 shows a corrosion rate map of an electric wire in which the corrosion rate is classified into three stages of high, medium, and slow in order to clearly classify the corrosion rate. The unit of corrosion rate in FIG. 11 is MPa / year. It can be understood that the corrosion rate of electric wires greatly depends on the amount of sea salt particles flying annually, and is fast near the coast and slow in the inland area. In addition, it can be understood that the corrosion rate is high on the Sea of Japan side and slow on the Pacific Ocean side and the Sea of Okhotsk side. Furthermore, in the Wakkanai region, it can be understood that the corrosion rate is high over a wide area because sea salt particles fly to the inland area. It was also confirmed that these results are in agreement with the actual corrosion data.

The classification of the corrosion rate in FIG. 11 is set as follows in consideration of the initial value and standard value of the steel core aluminum stranded wire ACSR160 mm ² , the useful life of the electric business fixed assets of the electric wire, and the like.
Corrosion rate "fast" 1.69 MPa / year or more Corrosion rate "medium" 0.845 MPa / year to less than 1.69 MPa / year Corrosion rate "slow" less than 0.845 MPa / year

In addition, in FIG. 9 and FIG. 11, the corrosion rate is classified into three categories of fast, medium, and slow, but this is just an example, and the corrosion rate may be classified into two or more. You may.

The present invention is not limited to the above embodiment, and the modifications described below are also possible.

(Modification example)
In the above embodiment, the annual amount of sea salt particles flying in the sea, the wetting time, and the dry / wet cycle are used as explanatory variables, but the present invention is not limited to this. In addition to the annual amount of sea salt particles, wetting time, and dry-wet cycle, other corrosive environmental factors may be added as explanatory variables.

In the above embodiment, the amount of decrease in the galvanized thickness applied to the steel member constituting the steel tower per year is defined as the corrosion rate, but the present invention is not limited to this. For example, when evaluating the corrosion of a transmission line, the amount of decrease in the tensile strength of the aluminum wire per year may be used as the corrosion rate.

In the above embodiment, an explanatory variable is selected from a combination of 81 types of explanatory variables, and an index of outliers is selected by performing multiple regression analysis using the selected explanatory variable. Not limited to. For example, a multivariate analysis is performed based on the explanatory variables and all the actual corrosion data (provisional objective variables) that do not exclude the outliers, and the DfBatas, Cook distances, and the lever ratios are obtained from the results of the multivariate analysis. By excluding the outliers of the actual corrosion data using the above, the optimum objective variable may be determined from all 243 combinations consisting of the explanatory variables (81 ways) and the method of determining the objective variable (3 ways).

In the above embodiment, the corrosion rate of the steel tower is estimated by using the corrosion rate estimation device 1, but the present invention is not limited to this. For example, the corrosion rate estimation device 1 may be used to estimate the corrosion rate of electric wires, overhead ground wires, steel pipes, pillar transformers, vehicle bodies, various housings, and the like.

In the above embodiment, the corrosion rate map is displayed on the display unit 12 of the corrosion rate estimation device 1, but the present invention is not limited to this. For example, it may be output to a terminal or the like outside the corrosion rate estimation device 1.

In the above embodiment, data such as the annual amount of sea salt particles flying, the wetting time, and the dry / wet cycle are stored in the storage unit 14 of the corrosion rate estimation device 1, but the present invention is not limited thereto. For example, all or part of the various data may be stored in an external server, computer, or the like via a communication network.

In the above embodiment, the estimation formula of the corrosion rate has been derived by performing the multiple regression analysis, but the present invention is not limited to this. Any method that defines the relationship between input data and output data and can supply data to the input layer and obtain output data from the output layer can be used to derive the corrosion rate estimation formula. good.

For example, an estimation formula of the corrosion rate may be derived using a prediction model using a hierarchical neural network including an input layer, an intermediate layer, and an output layer. More specifically, a weighting coefficient that sets the connection state between neurons is set between the input neuron and the intermediate neuron, and between the intermediate neuron and the output neuron, and learning calculation processing by the error back propagation method is performed. By executing the above, the weighting coefficient may be optimized using the training data consisting of the input data and the output data.

In the above embodiment, the Internet is used as the communication network, but the present invention is not limited to this. For example, the communication network may be realized by using a LAN (Local Area Network), a dedicated line, or the like.

In the above embodiment, the corrosion rate estimation device 1 operates based on the program stored in the storage unit 14, but the present invention is not limited thereto. For example, the functional configuration realized by the program may be realized by hardware.

In the above embodiment, the corrosion rate estimation device 1 is, for example, a general-purpose computer, but the present invention is not limited to this. For example, the corrosion rate estimation device 1 may be realized by a dedicated system or may be a computer provided on the cloud. Further, the process executed by the corrosion rate estimation device 1 is realized by, for example, an apparatus having the above-mentioned physical configuration executing a program stored in the storage unit 14, but the present invention has a program. It may be realized as a storage medium in which the program is recorded.

Further, the program for executing the above-mentioned processing operation can be read by a computer such as a flexible disk, a CD-ROM (Compact Disk Read-Only Memory), a DVD (Digital Versatile Disk), or an MO (Magneto-Optical Disk). A device that executes the above-mentioned processing operation may be configured by storing it in a recording medium, distributing it, and installing the program on a computer.

The above embodiments are examples, and the present invention is not limited thereto, and various embodiments are possible without departing from the spirit of the invention described in the claims. The components described in each embodiment and modification can be freely combined. The present invention also includes inventions equivalent to those described in the claims.

1 Corrosion rate estimation device 11 Operation unit 12 Display unit 13 Communication unit 14 Storage unit 141 Annual arrival sea salt particle amount data table 142 Wet time data table 143 Wet and dry cycle data table 144 Corrosion record data table 15 Control unit 151 Estimate formula creation unit 152 Corrosion rate estimation unit 153 Corrosion rate map creation unit

Claims (9)

By performing multiple regression analysis with the corrosion rate of the metal material as the objective variable and the corrosive environment factors including the annual amount of sea salt particles flying in the year, the wetting time, and the dry-wet cycle that affect the corrosion of the metal material as the explanatory variables. An estimation formula creation method for creating an estimation formula for the corrosion rate of metal materials,
A corrosion rate estimation means for estimating the corrosion rate of the metal material by inputting a corrosion environment factor including an annual amount of sea salt particles, a wetting time, and a dry / wet cycle into the estimation formula.
Equipped with
The dry-wet cycle is the number of cycles in which a wet state for a predetermined time or longer and a dry state for a predetermined time or longer are alternately continued.
Corrosion rate estimation device. The corrosion rate estimation device according to claim 1, wherein the estimation formula is as shown in the following formula.
Y = α + β ₁ X ₁ + β ₂ X ₂ + β ₃ X ₃
However, Y is the corrosion rate of the metal material, X ₁ is the amount of sea salt particles flying annually, X ₂ is the wetting time, X ₃ is the dry / wet cycle, α is a constant, and β ₁ to β ₃ are coefficients. The annual amount of sea salt particles, the wetting time, and the dry / wet cycle in the estimation formula creating means are all simulation values obtained from the meteorological analysis code.
The corrosion rate estimation device according to claim 1 or 2. The metal material is a steel member constituting a steel tower.
The corrosion rate is the amount of decrease in the galvanized thickness applied to the steel member per year.
The corrosion rate estimation device according to any one of claims 1 to 3. The metal material is an aluminum wire constituting a power transmission line.
The corrosion rate is the amount of decrease in the tensile strength of the aluminum wire per year.
The corrosion rate estimation device according to any one of claims 1 to 3. For the wetting time and the dry-wet cycle, a plurality of defined patterns are prepared, and the defined pattern with the best analysis result is selected when the multiple regression analysis is performed.
The corrosion rate estimation device according to any one of claims 1 to 5. A means for creating a corrosion rate map is provided, which creates a corrosion rate map showing the corrosion rate of the metal material in a predetermined area based on the estimated corrosion rate of the metal material.
The corrosion rate estimation device according to any one of claims 1 to 6. By performing multiple regression analysis with the corrosion rate of the metal material as the objective variable and the corrosive environment factors including the annual amount of sea salt particles flying in the year, the wetting time, and the dry-wet cycle that affect the corrosion of the metal material as the explanatory variables. An estimation formula creation step to create an estimation formula for the corrosion rate of metal materials, and
A corrosion rate estimation step for estimating the corrosion rate of the metal material by inputting a corrosion environment factor including an annual amount of sea salt particles, a wetting time, and a dry / wet cycle into the estimation formula.
Including
The dry-wet cycle is the number of cycles in which a wet state for a predetermined time or longer and a dry state for a predetermined time or longer are alternately continued.
Corrosion rate estimation method. Computer,
By performing multiple regression analysis with the corrosion rate of the metal material as the objective variable and the corrosive environment factors including the annual amount of sea salt particles flying in the year, the wetting time, and the dry-wet cycle that affect the corrosion of the metal material as the explanatory variables. An estimation formula creation method for creating an estimation formula for the corrosion rate of metal materials,
A corrosion rate estimation means for estimating the corrosion rate of the metal material by inputting the corrosion environment factors including the annual amount of sea salt particles, the wetting time and the dry / wet cycle into the estimation formula.
It is a program to function as
The dry-wet cycle is the number of cycles in which a wet state for a predetermined time or longer and a dry state for a predetermined time or longer are alternately continued.
program.

Images