JP4990877B2 - Equipment risk assessment system and risk assessment method - Google Patents

Description

  The present invention relates to a facility risk evaluation system and a risk evaluation method for performing risk evaluation of devices installed in various facilities.

In the case of an electric power company, for example, there are power distribution facilities including equipment such as utility poles, electric wires, transformers, and switches. The electric power company replaces each device of the distribution facility as necessary. As a replacement factor for these devices,
Replacement due to deterioration Replacement for enhancement by electricity supply to new customers There is replacement by request for equipment transfer from customers. There are various types of power distribution equipment, and there are a variety of replacement factors. Therefore, there is no way to know when equipment replacement occurs, and equipment replacement is performed based on human means, such as inspection results by the person in charge, experience of the person in charge, and information collected from other places. This is the current situation. For this reason, the following problems may occur. For example, after replacing a device due to deterioration, the device may be replaced within a short period of time due to an increase in electricity supply. Such a situation is inefficient and costly.

On the other hand, various devices are also provided in the plant. In order to perform plant safety management, a risk management device has been proposed that determines the failure probability of each device and determines replacement of the device (see, for example, Patent Document 1). When such an apparatus is used, it is possible to determine replacement of equipment without depending on human means.
JP 2004-191359 A

  However, the aforementioned risk management apparatus for a plant has the following problems. This device determines the replacement time by determining the failure rate of each device. That is, this apparatus uses only the failure of the device as a replacement factor, and does not consider other factors such as the above-described replacement factor.

  In addition, when replacing equipment in a facility, it is common to consider and carry out tuned replacement that replaces another equipment together with the equipment to be replaced according to the status of another equipment in the same equipment. . However, the previous apparatus does not consider this point. Furthermore, in the case of facilities installed in a wide range and in a network form under various external environments, it is common to consider and carry out synchronous replacement in consideration of the situation of adjacent facilities. However, the previous apparatus does not consider this point.

  An object of the present invention is to provide a facility risk evaluation system and a risk evaluation method for solving the above-described problems and performing device risk evaluation based on various replacement factors for devices installed in the facility. .

  In order to solve the above-mentioned problems, the invention of claim 1 is characterized in that a storage means for storing data indicating a state of a target device to be replaced while being installed in a facility, and a replacement time of the target device by a single risk evaluation process. And a processing unit that calculates the time based on the passage of time for each replacement factor for replacing the target device based on data representing the state of the target device stored in the storage unit. The risk value is calculated, the limit risk value set in advance is calculated as the first replacement time when the sum of the risk values over time is exceeded, and the sum of the risk values and the target device Based on the replacement cost, the replacement cost per unit risk is calculated as the unit cost, and the time when the unit cost falls below the preset limit unit cost. Calculated as 2 for replacement time, as replacement time of the target device slower to in these replacement timing, a facility risk assessment system which is characterized in that the single risk assessment process.

  In the first aspect of the invention, the storage means stores data representing the state of the device to be replaced while being installed in the facility. Then, the processing means calculates the replacement time of the target device by the single risk evaluation process.

  According to a second aspect of the present invention, in the equipment risk evaluation system according to the first aspect, the processing means calculates the risk for each replacement factor by the occurrence probability of the replacement factor and the degree of influence when the replacement factor occurs. It is obtained by integration.

  According to a third aspect of the present invention, in the equipment risk evaluation system according to the first or second aspect, when the risk value is a discrete value, the processing means uses a least square method based on each discrete value. The risk value corresponding to the passage of time is calculated.

  According to a fourth aspect of the present invention, in the equipment risk assessment system according to the third aspect, the processing means is based on the latest data in the state of the target device stored in the storage means. The probability of occurrence is updated using the theorem.

  The invention according to claim 5 is the equipment risk evaluation system according to any one of claims 1 to 4, wherein the processing unit performs the unit risk evaluation process on another device different from the target device. The cost of replacing two devices based on the replacement cost when replacing the target device and another device alone, and the replacement cost when replacing both the target device and another device. The reduction rate of unit cost due to the replacement of two devices is calculated, the condition that the replacement time of the other device is earlier than the replacement time of the target device, and the preset marginal cost At least one of the condition that the reduction rate of the cost exceeds the reduction rate and the condition that the reduction rate of the unit cost exceeds the preset limit unit cost reduction rate When satisfied, it is determined that the tuning replace replace both of the target device and another device, characterized in that.

  The invention according to claim 6 is provided with processing means for preliminarily storing data indicating the state of the target device to be replaced while being installed in the facility, and calculating the replacement time of the target device by a single risk evaluation process. Based on the data indicating the status of the target device being operated, a risk value corresponding to the passage of time is calculated for each replacement factor for replacing the target device, and a preset limit risk value is calculated over time. The time when the sum of the risk values corresponding to the risk exceeds the first replacement time, and the replacement cost per unit risk as the unit cost based on the sum of the risk values and the replacement cost of the target device Calculate and calculate the limit unit cost set in advance as the second replacement time when the unit cost is lower, and these replacement times The replacement time of the target device towards slow in the middle, is a method of assessing risk equipment, characterized in that.

  According to the first and sixth aspects of the invention, since the optimum time for replacing the target device is calculated by the single risk evaluation process, the risk evaluation of the target device can be performed based on the replacement time.

  According to the invention of claim 2, since the risk for each replacement factor is obtained by integrating the probability of occurrence and the degree of influence, the risk can be calculated by a simple calculation, and the risk can be calculated quickly. .

  According to invention of Claim 3, when the value of the risk according to the passage of time is a discrete value, the correlation between the passage of time and the risk can be obtained by using the least square method.

  According to the invention of claim 4, since the occurrence probability is updated using Bayes' theorem, the occurrence probability can be updated.

  According to the invention of claim 5, the object based on the tuned replacement is obtained by obtaining the optimum tuned change judgment result based on the replacement time of the two devices, the cost reduction rate, and the unit cost reduction rate. Risk assessment of equipment can be performed.

  Next, embodiments of the present invention will be described in detail with reference to the drawings.

(Embodiment 1)
A facility risk evaluation system according to this embodiment is shown in FIG. The equipment risk evaluation system of FIG. 1 includes a risk evaluation device 10 and a storage device 20 that can be accessed by the risk evaluation device 10.

  The storage device 20 stores various data necessary for risk assessment of equipment as a database. The database stored in the storage device 20 includes an installation date database 21A. In the installation date database 21A, the date of installation of the equipment, which is each component of the power distribution equipment, is recorded as the equipment attribute. An example of the installation date database 21A is shown in FIG. In the installation date database 21A, the installation years and the installation dates are recorded corresponding to the respective devices such as utility poles, switches, transformers, and electric wires.

  The database stored in the storage device 20 includes a material configuration database 21B. In the material configuration database 21 </ b> B, the material forming the utility pole, the switch, the transformer, and the electric wire is recorded as the attribute of the device. An example of the material configuration database 21B is shown in FIG. In the material configuration database 21B, if the equipment of the power distribution facility is a utility pole, classifications such as a concrete pillar, a steel pipe pillar, and a wooden pillar are recorded.

The database stored in the storage device 20 includes an operating state database 21C. The operation state database 21C records the results when the person in charge of inspection patrols each district. An example of the operating state database 21C is shown in FIG. In this operating state database 21C, presence / absence of abnormality is recorded as a patrol result of the utility pole, switch, transformer, and electric wire. In this embodiment, the abnormality of the utility pole, switch, transformer, and electric wire is as follows.
Abnormality of utility poles: cracks, damage, corrosion Abnormalities of switches ... Rust generation, damage, corrosion Corrosion of transformers: Rust generation, damage, overloads Abnormality of wires ... Coverage damage, wire breakage, overload Record these abnormalities The inspection result database 21C accumulates and records inspection results every time inspection is performed.

The database stored in the storage device 20 includes a failure database 21D. In the failure database 21D, past failures in the equipment of the power distribution facility are recorded. An example of the failure database 21D is shown in FIG. In the failure database 21D, for example, the date and time when the failure of the utility pole, switch, transformer, and electric wire occurred, the utility pole number, and the content of the failure are recorded. In this embodiment, the failure of the utility pole, switch, transformer, and wire is as follows.
Failure of power pole: Breakage, collapse, inclination Switch failure ... Breakage, burnout, inoperability Transformer failure ... Breakage, burnout, oil leakage Wire failure ... Disconnection, contact failure The database stored in the storage device 20 contains land There is a usage classification database 21E. The land use classification database 21E records the land use classification of the place where each device is installed. An example of the land use classification database 21E is shown in FIG. In the land use classification database 21E, for example, land use classifications such as utility poles, switches, transformers, and electric wires, for example, residential land, rice field, field, forest, road, and the like are recorded.

The database stored in the storage device 20 includes a demand increase rate database 21F. The demand increase rate database 21 </ b> F records a demand increase rate of devices in a nearby area including a place where each device is installed. An example of the demand increase rate database 21F is shown in FIG. In this demand increase rate database 21F, for example, an average demand increase rate in the nearest three years within a radius of 100 [m] centered on a power pole in which each device is installed is less than 50 [kW / km ² ], It is recorded in the categories of 50 or more and less than 100 [kW / km ² ] and 100 [kW / km ² ] or more.

  The database stored in the storage device 20 includes a supply construction result database 21G. In the supply construction result database 21G, the number of equipment replacement works by supply in the area including the place where each equipment is installed is recorded. An example of the supply construction record database 21G is shown in FIG. In this supply construction result database 21G, for example, the number of annual average supply replacement works for a power pole within a radius of 100 [m] centered on the power pole where each device is installed is less than 3 in the last three years. More than 10 cases, less than 10 cases, and more than 10 cases are recorded.

  The database stored in the storage device 20 includes a peripheral environment database 21H. The surrounding environment database 21H records the surrounding environment in which each device is installed. An example of the surrounding environment database 21H is shown in FIG. In this surrounding environment database 21H, the surrounding environment of the utility pole in which the equipment is installed is recorded, for example, in a category such as a house in the vicinity, the vicinity of the road environment, and the private ground air passage.

  The database stored in the storage device 20 includes a relocation work result database 21J. In the relocation work record database 21J, the number of replacement works by relocation of equipment in the area including the place where each equipment is installed is recorded. An example of the supply construction record database 21G is shown in FIG. In this relocation work result database 21J, for example, the average number of relocation / replacement works in the last three years within a radius of 100 [m] centered on the utility pole where each device is installed is less than 5, for example, 5 More than 10 cases, less than 10 cases, more than 10 cases are recorded.

  The database stored in the storage device 20 includes a failure rank database 21K. In the failure rank database 21K, the rank classification of the damage amount due to the failure of each device is recorded. An example of the failure rank database 21K is shown in FIG. In the failure rank database 21K, as damage amounts due to failures, for example, recovery time, recovery work costs, and social impact are recorded as numerical values.

  The database stored in the storage device 20 includes a supply rank database 21L. In the supply rank database 21L, the rank classification of the damage amount due to the supply work of each device is recorded. An example of the supply rank database 21L is shown in FIG. In the supply rank database 21L, for example, the number of power outages, supply work costs, and investment recovery year are recorded as numerical values as the damage caused by the supply work.

  The database stored in the storage device 20 includes a transfer rank database 21M. In the transfer rank database 21M, the rank classification of the damage amount due to the transfer work of each device is recorded. An example of the transfer rank database 21M is shown in FIG. In the relocation rank database 21M, as the amount of damage due to relocation work, for example, the number of power outages, relocation work costs, and site negotiation costs are recorded as numerical values.

  The risk evaluation apparatus 10 is a computer that performs facility risk evaluation, and includes a display unit 11, an operation unit 12, a processing unit 13, a storage unit 14, and an interface 15.

  The display unit 11 is a display device such as an LCD (Liquid Crystal Display) that displays data necessary for equipment risk assessment, risk assessment results, and the like. The operation unit 12 is a device operated by a person in charge such as a mouse or a keyboard. By operating the operation unit 12, an instruction or the like for starting risk evaluation is input. The interface 15 connects the processing unit 13 to the storage device 20 and enables access to the storage device 20. The storage unit 14 stores in advance various programs required for the computer and programs for evaluating the risk of equipment.

The processing unit 13 executes various programs stored in the storage unit 14. The program executed by the processing unit 13 includes a single risk evaluation process. In this embodiment, the type of risk is
(A) Replacement due to failure (failure)
(Ii) Reinforcement by supplying electricity to new customers (supply)
(U) Relocation (transfer) by application for facility relocation from a customer
These are the three types. In this embodiment, the risks (a) to (u) are
Failure risk = Failure occurrence probability x Failure impact level Supply risk = Supply construction occurrence probability x Supply impact level Relocation risk = Relocation work occurrence probability x Relocation impact degree. Single risk assessment process is a process that evaluates the risk of a single device such as a power pole, switch, transformer, electric wire, etc. (hereinafter referred to as “single risk assessment”). This single risk assessment process is shown in FIGS. In the single risk evaluation process, the risk of aging such as one year or two years later is calculated based on the probability of occurrence of the risk over time and the degree of influence of the risk that has occurred.

  When the unit risk evaluation process shown in FIGS. 14 and 15 is started, the processing unit 13 displays an input screen for selecting a device that is a target device for evaluating the unit risk and is installed in the distribution facility (Step S13). S1). Specifically, a screen for selecting each device installed on the utility pole as the distribution facility is displayed. Thereafter, when the target device is selected by an operation on the operation unit 12, the processing unit 13 receives a selection result from the operation unit 12 (step S2). Specifically, in step S2, the selection result of the utility pole is received as equipment of the distribution facility.

When step S2 ends, the processing unit 13 selects a risk type (step S3). Aged occurrence probability calculation processing is performed on the target device selected in step S2 in accordance with the risk selected in step S3 (step S4). Thereafter, the processing unit 13 determines whether there is an unselected risk (step S5). If there is an unselected risk, the processing unit 13 selects another risk (step S6) and returns the process to step S4. The processing unit 13 performs the aging probability of each risk by the series of processes of steps S3 to S6, that is,
Aged failure occurrence probability Aged supply occurrence probability Aged transfer occurrence probability is calculated.

  For example, if the target device is a utility pole and the risk selected in step S3 is, for example, the failure (A), the processing unit 13 calculates the failure occurrence probability for the utility pole as the failure occurrence probability for the aged in step S4. This is calculated by processing (aging probability calculation processing). This process is based on the basic process described below.

  Hereinafter, basic processing will be described. When the processing unit 13 starts the basic processing shown in FIG. 19 and FIG. 20, the processing unit 13 refers to each database in the storage device 20 and refers to data related to a target device that is a single risk evaluation target (hereinafter referred to as “related data”). ) Is extracted (step SA1). When the processing unit 13 receives the related data from the storage device 20, the processing unit 13 creates data combining the related data (hereinafter referred to as “combination data”) (step SA2). This combination data is data representing the classification of devices. Specifically, when a utility pole is selected as the target device, as illustrated in FIG. 21, the processing unit 13 extracts the installation year data of the utility pole extracted from the installation date database 21A and the material configuration database 21B. The material composition data is used as related data. Then, the processing unit 13 creates combination data by combining these two related data.

  When the combination data is created in step SA2, the processing unit 13 creates past failure record data with reference to the failure database 21D based on an arbitrary item in the combination data (step SA3). Specifically, when 20 years or more of the installation year classification of the combination data is an arbitrary item, and the concrete column of the equipment material classification is an optional item, the processing unit 13 extracts the corresponding data from the failure database 21D. To do. Using these data, the processing unit 13 creates past failure record data including the corresponding utility pole number and the presence or absence of failure as shown in FIG. Arbitrary items may be designated by the person in charge or may be extracted by the processing unit 13.

When the processing unit 13 creates the failure record data in step SA3, the processing unit 13 performs update processing shown in FIG. 20 (step SA4). In this update process, the processing unit 13 calculates the failure rate (posterior probability) by updating the failure rate (prior probability) of the facility with the latest patrol record using Bayes' theorem. Hereinafter, a case where the installation age category is 20 years or more and the equipment material category is a concrete column, that is, a concrete column of 20 years or more will be described as a specific example. The processing unit 13
A phenomenon that a telephone pole of a concrete pole over 20 years fails
A phenomenon where abnormalities are discovered by patrol in a utility pole of a concrete pole for more than 20 years
And When starting the update process, the processing unit 13 calculates the prior probability of the facility (step SA21). In the case of a specific example, the processing unit 13 calculates the failure rate (prior probability) of a concrete column from the following formula from the past failure record data in FIG.

When step SA21 ends, the processing unit 13 calculates the probability that no accident will occur in the facility (step SA22). In the case of a specific example, the processing unit 13 calculates the probability that an accident will not occur in a concrete column from the following formula.

  Thereafter, the processing unit 13 refers to each database in the storage device 20 and creates the latest inspection result data (step SA23). In the case of a specific example, the processing unit 13 refers to the operation state database 21C, and creates the latest inspection result data including the utility pole number and presence / absence of an abnormality as shown in FIG.

また、故障が発生しなかった設備のうち、巡視で異常が発見されるケースが図２５に示す通りである。これにより、処理部１３は、次の式から、故障が発生しなかった設備のうち、巡視で異常が発見される確率を算出する。

Thereafter, the processing unit 13 uses the past failure record data created in step SA3 and the latest patrol record data created in step SA23 to determine the probability that an abnormality will be found during the patrol among the facilities in which the failure occurred. The probability that an abnormality will be found by patrol among the facilities in which no failure has occurred is calculated (step SA24). In the case of a specific example, a case where an abnormality is discovered by patrol among facilities in which a failure has occurred is as shown in FIG. Thereby, the processing unit 13 calculates the probability that an abnormality will be found by patrol among the facilities in which a failure has occurred, from the following equation.

Moreover, the case where abnormality is discovered by patrol among facilities in which no failure has occurred is as shown in FIG. Thereby, the processing unit 13 calculates the probability that an abnormality is found by patrol among the facilities in which no failure has occurred, from the following equation.

巡視で異常が発見された設備が、故障する確率（事後確率）を算出する（ステップＳＡ２５）。具体例の場合には、処理部１３は、次の式を用いて、事後確率を算出する。

この数６式を用いた算出結果は０．８０である。処理部１３は、設備の故障率（事前確率）である０．６を、巡視で異常が発見された設備が故障する確率（事後確率）である０．８０として更新する。つまり、処理部１３は、巡視実績によって設備の故障率を更新する。これにより、設置年数区分が２０年以上のコンクリート柱の、最新の故障率は０．８０になる。 When the processing unit 13 calculates two probabilities that an abnormality is found by patrol, the processing unit 13 uses Bayes' theorem, that is, the following equation:

The probability (a posteriori probability) that the facility in which an abnormality is found by patrol will break down is calculated (step SA25). In the case of a specific example, the processing unit 13 calculates the posterior probability using the following formula.

The calculation result using Equation 6 is 0.80. The processing unit 13 updates 0.6, which is the failure rate (prior probability) of the equipment, as 0.80, which is the probability (posterior probability) of failure of the equipment in which an abnormality is found by inspection. In other words, the processing unit 13 updates the equipment failure rate based on the patrol results. As a result, the latest failure rate of concrete columns with an installation age category of 20 years or more is 0.80.

  When step SA25 ends, the processing unit 13 ends the update process of step SA4. Then, when the update process ends, the processing unit 13 ends the basic process.

  Based on such basic processing, the processing unit 13 performs an aged failure occurrence probability calculation process in step S4. The failure occurrence probability obtained by the basic processing represents the probability at the time of inspection, but in the failure occurrence probability calculation processing of the aging, the processing unit 13 may be one year after inspection, two years later, etc. The probability that a failure will occur is calculated for every year. This aged failure occurrence probability calculation process is shown in FIG. When the processing unit 13 starts the aged failure occurrence probability calculation process, the processing unit 13 refers to the database of the storage device 20 and extracts related data related to the target device that is the evaluation target of the single risk (step S41). The processing unit 13 creates result data based on the extraction result (step S42). For example, when the utility pole is selected as the target device in step S2, the processing unit 13 creates the record data shown in FIG. Here, an example of an electric pole of “concrete pole” that has passed “more than 20 years” is taken as an example.

  When step S42 ends, the processing unit 13 calculates an aging posterior probability (step S43). This process is based on the basic process described above. Moreover, in this embodiment, the case of calculating the a posteriori probability after one year, two years, and five years as an aging a posteriori probability is taken as an example.

また、１年後に故障が発生しない確率を、次の式から算出する。

また、１年後に故障が発生した電柱のうち、最新の巡視で異常が発見される確率を、実績データから作成した、図２７の参照データを基にして、次の式から算出する。

また、１年後に故障が発生しなかった電柱のうち、最新の巡視で異常が発見される確率を、故障巡視実績データから作成した、図２８の参照データを基にして、次の式から算出する。

First, the processing unit 13 calculates a posterior probability after one year. The processing unit 13 calculates the failure occurrence probability (prior probability) of the utility pole after one year from the actual data using the following formula.

The probability that no failure will occur after one year is calculated from the following equation.

Moreover, the probability that an abnormality will be found by the latest patrol among power poles that have failed after one year is calculated from the following formula based on the reference data shown in FIG.

In addition, the probability that an abnormality is found in the latest patrol among power poles that did not fail after one year was calculated from the following formula based on the reference data shown in FIG. To do.

に対して、数７式〜数１０式の算出結果を代入し、次の式から事後確率を算出する。

つまり、最新の巡視で異常が発見された電柱については、１年後に故障する確率が値０．３３として求められ、値０．４の事前確率が巡視実績によって更新されたことになる。 Thereafter, the processing unit 13 performs the Bayes' theorem described above.

Then, the calculation results of Equation 7 to Equation 10 are substituted, and the posterior probability is calculated from the following equation.

That is, for the utility pole in which an abnormality has been found in the latest inspection, the probability of failure after one year is obtained as a value of 0.33, and the prior probability of a value of 0.4 has been updated with the inspection results.

また、２年後に故障が発生しない確率を、次の式から算出する。

また、２年後に故障が発生した電柱のうち、最新の巡視で異常が発見される確率を、実績データから作成した、図２９の参照データを基にして、次の式から算出する。

また、２年後に故障が発生しなかった電柱のうち、最新の巡視で異常が発見される確率を、実績データから作成した、図３０の参照データを基にして、次の式から算出する。

この後、処理部１３は、ベイズの定理に対して、数１３式〜数１６式の算出結果を代入し、次の式から事後確率を算出する。

つまり、最新の巡視で異常が発見された電柱については、２年後に故障する確率が値０．５０として求められ、値０．６０の事前確率が巡視実績によって更新されたことになる。 Next, the processing unit 13 calculates a posteriori probability after two years. The processing unit 13 calculates the failure occurrence probability (prior probability) of the utility pole after two years from the actual data created in step S42 from the following formula.

Also, the probability that no failure will occur after 2 years is calculated from the following equation.

In addition, the probability that an abnormality will be found by the latest patrol among power poles that have failed after two years is calculated from the following formula based on the reference data shown in FIG.

Moreover, the probability that an abnormality will be discovered by the latest patrol among power poles in which no failure has occurred after two years is calculated from the following formula based on the reference data shown in FIG.

Thereafter, the processing unit 13 substitutes the calculation results of Expressions 13 to 16 for Bayes' theorem, and calculates the posterior probability from the following expression.

That is, for a utility pole in which an abnormality has been found in the latest inspection, the probability of failure after two years is obtained as a value of 0.50, and the prior probability of a value of 0.60 has been updated by the inspection results.

また、５年後に故障が発生しない確率を、次の式から算出する。

また、５年後に故障が発生した電柱のうち、最新の巡視で異常が発見される確率を、実績データから作成した、図３１の参照データを基にして、次の式から算出する。

また、５年後に故障が発生しなかった電柱のうち、最新の巡視で異常が発見される確率を、実績データから作成した、図３２の参照データを基にして、次の式から算出する。

この後、処理部１３は、ベイズの定理に対して、数１８式〜数２１式の算出結果を代入し、次の式から事後確率を算出する。

つまり、最新の巡視で異常が発見された電柱については、５年後に故障する確率が値０．６７として求められ、値０．７０の事前確率が巡視実績によって更新されたことになる。 Finally, the processing unit 13 calculates the posterior probability after 5 years. The processing unit 13 calculates the failure occurrence probability (prior probability) of the utility pole after 5 years from the following data based on the result data created in step S42.

Also, the probability that no failure will occur after 5 years is calculated from the following equation.

Moreover, the probability that an abnormality will be found in the latest patrol among power poles that have failed after five years is calculated from the following formula based on the reference data shown in FIG.

Moreover, the probability that an abnormality will be found by the latest patrol among power poles in which no failure has occurred after 5 years is calculated from the following formula based on the reference data shown in FIG.

Thereafter, the processing unit 13 substitutes the calculation results of Expressions 18 to 21 for Bayes' theorem, and calculates the posterior probability from the following expression.

That is, for a utility pole in which an abnormality has been found in the latest inspection, the probability of failure after 5 years is obtained as a value of 0.67, and the prior probability of a value of 0.70 has been updated by the inspection results.

  In this way, the processing unit 13 calculates an aging posterior probability, that is, an aging failure probability in step S43. Thereafter, the processing unit 13 creates the aged failure probability data shown in FIG. 33 from the calculated failure probability (step S44). In general, patrols are regularly performed about once a year, so the failure probability obtained based on patrols becomes discrete data such as one year later and two years later. Moreover, the failure record of every year is not necessarily obtained. Therefore, the processing unit 13 uses the aged failure probability data and calculates an expression representing the relationship between the aging and the failure probability, that is, an expression expressed in the form of y = a + bx by the least square method (step S45). ).

とすると、回帰直線からの偏差の２乗和Ｄが、

となり、この偏差の２乗和Ｄが最小となる値ａ、ｂを求める。値ａは数２３式の切片を表す回帰母数であり、値ｂは数２３式の傾きを表す回帰母数である。なお、数２４式では、距離ｄは、例えば図３４の距離ｄ１〜ｄ４のそれぞれを表している。 The least square method is the following calculation. When there are two variables x and y, and the change of the value y with respect to the value x occurs more or less regularly, it is known that the interval between the values x and y fits fairly well by a straight line (regression line). However, the obtained data is not necessarily on the regression line, and generally shows some variation with respect to this line. In such a case, the least square method is known as a method for obtaining a regression line that best fits the obtained data. In the least square method, as shown in FIG. 34, the straight line L is obtained so that the square sum of the distances d1 to d4 in the y-axis direction between the obtained data D1 to D4 and the straight line L is minimized. This method uses the regression line equation

Then, the square sum D of the deviation from the regression line is

Thus, values a and b that minimize the square sum D of the deviations are obtained. The value a is a regression parameter that represents the intercept of Equation 23, and the value b is a regression parameter that represents the slope of Equation 23. In Equation 24, the distance d represents, for example, each of the distances d1 to d4 in FIG.

なお、数２５式の中で、値Ｎは、得られたデータｘ、ｙの個数を表す。 The regression line values a and b are obtained from the following simultaneous equations.

In the equation 25, the value N represents the number of obtained data x and y.

数２７式の演算結果を最小２乗法の数２５式に当てはめて、処理部１３は次の式を得る。

同じように、処理部１３は図３５（ａ）の座標データを利用して、次の演算を行う。

数２９式の演算結果を最小２乗法の数２６式に当てはめて、処理部１３は次の式を得る。

この後、処理部１３は、数２８式と数３０式とを連立方程式とし、

この連立方程式を解くと、次の解を得る。

さらに、処理部１３は、この解から次の回帰直線を得る。

In the case of using the aged failure probability data shown in FIG. 33 for such a least square method, the processing unit 13 performs step S45 as follows. The coordinate data obtained from this failure probability data is as shown in FIG. 35 (a). These relationships are as shown in FIG. 35 (b) when the failure probability is the y axis and the aging is the x axis. Become. The processing unit 13 performs the following calculation using the coordinate data of FIG.

By applying the calculation result of Expression 27 to Expression 25 of the least square method, the processing unit 13 obtains the following expression.

Similarly, the processing unit 13 performs the following calculation using the coordinate data of FIG.

By applying the calculation result of Expression 29 to Expression 26 of the least square method, the processing unit 13 obtains the following expression.

Thereafter, the processing unit 13 sets Equation 28 and Equation 30 as simultaneous equations,

Solving these simultaneous equations gives the following solution:

Further, the processing unit 13 obtains the next regression line from this solution.

  When the processing unit 13 obtains the regression line, the processing unit 13 ends the aged failure occurrence probability calculation process in step S4. The regression line obtained by the aged failure occurrence probability calculation processing is an expression representing the aging risk, and this straight line is shown in FIG. By using this regression line, the failure probability in any year and the failure probability in units of months during the year can be obtained. The regression line is updated using Bayes' theorem based on the latest patrol results. As a result, the future failure probability can be obtained with high accuracy.

  On the other hand, if the risk selected in step S3 is, for example, the supply (i), the processing unit 13 performs an aged supply work occurrence probability calculation process in step S4 for the target device. For example, if the target device is a transformer, the processing unit 13 calculates the occurrence probability of supply work for the transformer in step S4. This process is based on the following basic process.

  Here, this basic process will be described. When the processing unit 13 starts the basic processing shown in FIGS. 37 and 38, the processing unit 13 refers to each database in the storage device 20 and extracts the relevant data of the target device that is the evaluation target of the single risk (step SB1). When receiving the related data from the storage device 20, the processing unit 13 creates combination data of the related data (step SB2). This combination data is data representing the classification of devices. For example, when the device is a transformer, the processing unit 13 creates combination data shown in FIG. 39 using the land use classification database 21E and the demand increase rate database 21F.

When the combination data is created in step SB2, the processing unit 13 creates supply work data based on any item in the combination data (step SB3). For example, as an optional item, if the land use classification of the combination data is residential land and the demand increase rate is 100 [kW / km ² ], the processing unit 13 corresponds to the optional item as shown in FIG. Supply work data consisting of the past replacement work of transformers and the latest development plan information is created. The latest development plan information is information obtained from the outside.

When the processing unit 13 creates the supply construction data in step SB3, the processing unit 13 performs the update process shown in FIG. 38 (step SB4). In this update process, the processing unit 13 uses the Bayes' theorem to calculate the occurrence probability (a posteriori probability) of the supply work occurrence probability (prior probability) updated with the latest development plan information. Hereinafter, a case where the land use category is residential land and the demand increase rate is 100 [kW / km ² ] or more will be described as a specific example. The processing unit 13
An event in which a transformer with a land use classification of residential land and a demand increase rate of 100 [kW / km ² ] or more is replaced by supply work is A
Events where land use classification is residential land and the demand increase rate is 100 [kW / km ² ] or more where transformers are installed and development plan information (residential land development plan, redevelopment plan, etc.) is obtained B
And When starting the update process, the processing unit 13 calculates the prior probability of the device (step SB21). In the case of a specific example, the processing unit 13 calculates the probability (prior probability) of occurrence of transformer replacement from the following formula from the supply work data of FIG.

When step SB21 ends, the processing unit 13 calculates the probability that no device replacement will occur (step SB22). In the case of a specific example, the processing unit 13 calculates the probability that the transformer will not be replaced from the following equation.

逆に、処理部１３は、取り替えが発生しなかった機器のなかで、開発計画情報が得られる確率を算出する（ステップＳＢ２４）。具体例の場合には、取り替えが発生しなかった変圧器のなかで、開発計画情報が得られるケースが図４２に示す通りである。これにより、処理部１３は、次の式から、取り替えが発生しなかった変圧器のなかで、開発計画情報が得られる確率を算出する。

Thereafter, the processing unit 13 calculates the probability that the development plan information is obtained among the devices that have been replaced (step SB23). In the case of a specific example, a case in which development plan information is obtained in a transformer that has been replaced is as shown in FIG. Thereby, the processing unit 13 calculates the probability that the development plan information is obtained from the following formula among the transformers in which the replacement has occurred.

Conversely, the processing unit 13 calculates the probability that the development plan information is obtained among the devices that have not been replaced (step SB24). In the case of a specific example, FIG. 42 shows a case where development plan information is obtained among transformers that have not been replaced. Thereby, the process part 13 calculates the probability that development plan information will be obtained in the transformer by which replacement | exchange did not generate | occur | produce from the next formula.

この数３８式を用いた算出結果は０．６７である。処理部１３は、変圧器の取り替えが発生する確率（事前確率）である０．４０を、開発計画情報が得られた変圧器を取り替える確率つまり供給工事発生確率である０．６７として更新する。さらに、供給工事発生確率は、これ以降得られる、最新の開発計画情報により更新され、供給工事の発生予測を正確に行うことができる。 When the processing unit 13 calculates the four probabilities in each of the previous steps, it uses the Bayes' theorem to calculate the probability (posterior probability) of replacing the device for which the development plan information is obtained (step SB25). In the case of a specific example, the processing unit 13 calculates the posterior probability using the following formula.

The calculation result using the equation 38 is 0.67. The processing unit 13 updates 0.40, which is the probability of occurrence of transformer replacement (prior probability), as 0.67, which is the probability of replacing the transformer for which development plan information is obtained, that is, the supply construction occurrence probability. Further, the supply work occurrence probability is updated with the latest development plan information obtained thereafter, and the supply work occurrence probability can be accurately predicted.

  When step SB25 ends, the processing unit 13 ends the update process of step SB4. Then, when the update process ends, the processing unit 13 ends the basic process.

Based on such basic processing, the processing unit 13 performs aged construction work probability calculation processing in step S4. The probability of occurrence of supply works obtained in the basic process represents the probability at the time when development plan information is obtained. The probability of supply work occurring is calculated for each year such as one year later, two years later, and so on. FIG. 17 shows the process for calculating the probability of supply construction occurrence over time. When the processing unit 13 starts the aged supply work occurrence probability calculation process, the processing unit 13 refers to the database of the storage device 20 and extracts related data related to the target device that is the target of the supply work (step S61). The processing unit 13 creates supply work data based on the extraction result (step S62). For example, when a transformer is selected as an evaluation target in step S2, the processing unit 13 creates supply work data shown in FIG. Here, a transformer in the case where the land use classification is residential land and the demand increase rate is 100 [kW / km ² ] or more is taken as an example.

  When step S62 ends, the processing unit 13 calculates the probability of occurrence of replacement over time (step S63). This process is based on the basic process described above. Moreover, in this embodiment, the case where the posterior probability of one year later, two years later, and five years later is calculated as an aged replacement occurrence probability is taken as an example.

また、１年後に取り替えが発生しない確率を、次の式から算出する。

また、１年後に取り替えが発生した変圧器のうち、最新の開発計画情報が得られる確率を、供給工事データから作成した、図４４の参照データを基にして、次の式から算出する。

また、１年後に取り替えが発生しなかった変圧器のうち、最新の開発計画情報が得られる確率を、供給工事データから作成した、図４５の参照データを基にして、次の式から算出する。

この後、処理部１３は、前述したベイズの定理に対して、数３９式〜数４２式の算出結果を代入し、次の式から事後確率を算出する。

つまり、最新の開発計画情報が得られた場合、変圧器を１年後に取り替える確率（事後確率）である供給工事発生確率が値０．１７として求められ、値０．２０の事前確率が開発計画情報によって更新されたことになる。 First, the processing unit 13 calculates the replacement occurrence probability after one year of the transformer. The processing unit 13 calculates the replacement occurrence probability (prior probability) after one year of the transformer from the following formula from the supply work data created in step S62.

Also, the probability that no replacement will occur after one year is calculated from the following equation.

Moreover, the probability that the latest development plan information will be obtained among the transformers that have been replaced after one year is calculated from the following equation based on the reference data of FIG. 44 created from the supply construction data.

Moreover, the probability that the latest development plan information is obtained among the transformers that have not been replaced after one year is calculated from the following formula based on the reference data of FIG. 45 created from the supply construction data. .

Thereafter, the processing unit 13 substitutes the calculation results of Expressions 39 to 42 for the Bayes' theorem described above, and calculates the posterior probability from the following expression.

In other words, when the latest development plan information is obtained, the supply construction occurrence probability, which is the probability of replacing the transformer after one year (post-probability), is obtained as a value 0.17, and the prior probability of the value 0.20 is the development plan. It is updated with information.

また、２年後に取り替えが発生しない確率を、次の式から算出する。

また、２年後に取り替えが発生した変圧器のうち、最新の開発計画情報が得られる確率を、供給工事データから作成した、図４６の参照データを基にして、次の式から算出する。

また、２年後に取り替えが発生しなかった変圧器のうち、最新の開発計画情報が得られる確率を、供給工事データから作成した、図４７の参照データを基にして、次の式から算出する。

この後、処理部１３は、ベイズの定理に対して、数４４式〜数４７式の算出結果を代入し、次の式から事後確率を算出する。

つまり、最新の開発計画情報が得られた場合、変圧器を２年後に取り替える確率（事後確率）である供給工事発生確率が値０．５０として求められ、値０．６０の事前確率が開発計画情報によって更新されたことになる。 Next, the processing unit 13 calculates the replacement occurrence probability after two years of the transformer. The processing unit 13 calculates, from the following formula, the replacement occurrence probability (prior probability) of the transformer after two years from the supply work data created in step S62.

The probability that no replacement will occur after 2 years is calculated from the following equation.

Further, the probability that the latest development plan information can be obtained among the transformers that have been replaced after two years is calculated from the following formula based on the reference data of FIG. 46 created from the supply work data.

Of the transformers that have not been replaced after two years, the probability of obtaining the latest development plan information is calculated from the following formula based on the reference data of FIG. 47 created from the supply construction data. .

Thereafter, the processing unit 13 substitutes the calculation results of Expressions 44 to 47 for Bayes' theorem, and calculates the posterior probability from the following expression.

In other words, when the latest development plan information is obtained, the probability of supply construction occurrence, which is the probability of replacing a transformer after two years (the posterior probability), is obtained as a value of 0.50, and a prior probability of 0.60 is the development plan. It is updated with information.

また、５年後に取り替えが発生しない確率を、次の式から算出する。

また、５年後に取り替えが発生した変圧器のうち、最新の開発計画情報が得られる確率を、供給工事データから作成した、図４８の参照データを基にして、次の式から算出する。

また、５年後に取り替えが発生しなかった変圧器のうち、最新の開発計画情報が得られる確率を、供給工事データから作成した、図４９の参照データを基にして、次の式から算出する。

この後、処理部１３は、ベイズの定理に対して、数４９式〜数５２式の算出結果を代入し、次の式から事後確率を算出する。

つまり、最新の開発計画情報が得られた場合、変圧器を５年後に取り替える確率（事後確率）である供給工事発生確率が値０．６７として求められ、値０．７０の事前確率が開発計画情報によって更新されたことになる。 Finally, the processing unit 13 calculates the replacement occurrence probability after 5 years of the transformer. The processing unit 13 calculates the replacement occurrence probability (prior probability) after five years of the transformer from the following formula from the supply work data created in step S62.

The probability that no replacement will occur after 5 years is calculated from the following equation.

Also, the probability that the latest development plan information can be obtained among the transformers that have been replaced after five years is calculated from the following formula based on the reference data of FIG. 48 created from the supply construction data.

Moreover, the probability that the latest development plan information will be obtained among the transformers that have not been replaced after 5 years is calculated from the following formula based on the reference data of FIG. 49 created from the supply construction data. .

Thereafter, the processing unit 13 substitutes the calculation results of Expressions 49 to 52 for Bayes' theorem, and calculates the posterior probability from the following expression.

In other words, when the latest development plan information is obtained, the probability of supply construction occurrence, which is the probability of replacing the transformer after 5 years (the posterior probability), is obtained as a value 0.67, and the prior probability of the value 0.70 is the development plan. It is updated with information.

  In this way, the processing unit 13 calculates the aging posterior probability, that is, the aging replacement probability in step S63. Thereafter, the processing unit 13 creates the aged supply construction occurrence probability data shown in FIG. 50 from the calculated replacement probability (step S64). In general, development plan information is not always available. For this reason, a predetermined period is set, and the supply construction occurrence probability is calculated based on the information obtained within this period. As a result, the probability of occurrence of supply work obtained is discrete data such as one year later and two years later. Therefore, the processing unit 13 uses the aged supply construction occurrence probability data to calculate an expression representing the relationship between the aging and the supply construction occurrence probability, that is, an expression expressed in the form of y = a + bx by the least square method. (Step S65).

数５４式の演算結果を最小２乗法の数２５式に当てはめて、処理部１３は次の式を得る。

同じように、処理部１３は図５１（ａ）の座標データを利用して、次の演算を行う。

数５６式の演算結果を最小２乗法の数２６式に当てはめて、処理部１３は次の式を得る。

この後、処理部１３は、数５５式と数５７式とを連立方程式とし、

この連立方程式を解くと、次の解を得る。

さらに、処理部１３は、この解から次の回帰直線を得る。

When the aged supply work occurrence probability data shown in FIG. 50 is used, the processing unit 13 performs step S65 as follows. The coordinate data obtained from the aged supply construction occurrence probability data is as shown in FIG. 51A, and these relationships are shown in FIG. 51 (b) when the supply construction occurrence probability is taken as the y-axis and the aging is taken as the x-axis. ). The processing unit 13 performs the following calculation using the coordinate data of FIG.

The processing unit 13 obtains the following expression by applying the calculation result of Expression 54 to Expression 25 of the least square method.

Similarly, the processing unit 13 performs the following calculation using the coordinate data of FIG.

Applying the calculation result of Formula 56 to Formula 26 of the least square method, the processing unit 13 obtains the following formula.

Thereafter, the processing unit 13 uses Equations 55 and 57 as simultaneous equations,

Solving these simultaneous equations gives the following solution:

Further, the processing unit 13 obtains the next regression line from this solution.

  When the processing unit 13 obtains the regression line, it ends the aged occurrence probability calculation process in step S4. This regression line is an expression representing the aging risk, and this straight line is shown in FIG. By using this regression line, it is possible to obtain the supply construction occurrence probability in an arbitrary year and the supply construction occurrence probability in units of months during the year. The regression line is updated using Bayes' theorem with the latest development plan information. As a result, it is possible to accurately determine the future supply construction occurrence probability.

  On the other hand, if the risk selected in step S3 is, for example, the above-mentioned transfer work, the processing unit 13 performs an aged transfer work occurrence probability calculation process in step S4 for the target device. For example, if the target device is an electric wire, the processing unit 13 calculates the occurrence probability of the transfer work for the electric wire in step S4. This process is based on the following basic process.

  Here, this basic process will be described. When starting the basic processing shown in FIGS. 53 and 54, the processing unit 13 refers to each database in the storage device 20 and extracts the relevant data of the target device that is the evaluation target of the single risk (step SC1). When the processing unit 13 receives the related data from the storage device 20, the processing unit 13 creates combination data of the related data (step SC2). For example, when the device is an electric wire, the processing unit 13 creates combination data shown in FIG. 55 using the land use classification database 21E and the surrounding environment database 21H.

  When the combination data is created in step SC2, the processing unit 13 creates transfer work data based on any item in the combination data (step SC3). For example, as an optional item, if the land use classification of the combination data is residential land, and the surrounding environment is an electric wire passing through the private ground, the processing unit 13 can perform the replacement results by past relocation as shown in FIG. Create relocation work data consisting of the latest development plan information.

逆に、処理部１３は、電線の取り替えが発生しない確率を、次の式から算出する（ステップＳＣ２２）。

When the transfer unit data is created in step SC3, the processing unit 13 performs the update process shown in FIG. 54 (step SC4). In this update process, the processing unit 13 uses the Bayes' theorem to calculate the occurrence probability (a posteriori probability) of the relocation work occurrence probability (prior probability) updated with the latest development plan information. Hereinafter, a case where the land use classification is residential land and the surrounding environment is private ground air passage will be described as a specific example. The processing unit 13
An event in which land use classification is residential land and the surrounding environment is private ground air-passing electric wire is replaced by relocation.
An event where the land use classification is residential land and the surrounding environment is a place where electric wires passing through the private ground are installed, and the development plan information (land creation plan, road construction plan, building plan, etc.) in the vicinity is obtained B
And When starting the update process, the processing unit 13 calculates the prior probability of the device (step SC21). In the case of a specific example, the processing unit 13 calculates the probability (prior probability) of occurrence of the replacement of the electric wire from the following formula from the transfer work data of FIG.

Conversely, the processing unit 13 calculates the probability that no electric wire replacement will occur from the following equation (step SC22).

逆に、処理部１３は、取り替えが発生しなかった機器のなかで、開発計画情報が得られる確率を算出する（ステップＳＣ２４）。具体例の場合には、取り替えが発生しなかった電線のなかで、開発計画情報が得られるケースが図５８に示す通りである。これにより、処理部１３は、次の式から、取り替えが発生しなかった電線のなかで、開発計画情報が得られる確率を算出する。

Thereafter, the processing unit 13 calculates a probability that the development plan information is obtained among the devices that have been replaced (step SC23). In the case of a specific example, the case where development plan information is obtained among the wires that have been replaced is as shown in FIG. Thereby, the process part 13 calculates the probability that development plan information will be obtained in the electric wire which replacement | exchange occurred from the following formula | equation.

Conversely, the processing unit 13 calculates the probability that the development plan information is obtained among the devices that have not been replaced (step SC24). In the case of a specific example, a case in which development plan information is obtained among wires that have not been replaced is as shown in FIG. Thereby, the process part 13 calculates the probability that development plan information will be obtained from the following formula | equation in the electric wire which replacement | exchange did not generate | occur | produce.

この数６５式を用いた算出結果は０．６７である。処理部１３は、電線の取り替えが発生する確率（事前確率）である０．３０を、開発計画情報が得られた機器を取り替える確率つまり移転工事発生確率である０．６７として更新する。さらに、移転工事発生確率は、これ以降得られる、最新の開発計画情報により更新され、移転工事の発生予測を正確に行うことができる。 After calculating the four probabilities in each of the previous steps, the processing unit 13 uses the Bayes' theorem to calculate the probability (posterior probability) of replacing the device from which the development plan information is obtained (step SC25). In the case of a specific example, the processing unit 13 calculates the posterior probability using the following formula.

The calculation result using Formula 65 is 0.67. The processing unit 13 updates 0.30, which is the probability (preliminary probability) that the electric wire will be replaced, as 0.67, which is the probability that the device whose development plan information is obtained, that is, the transfer work occurrence probability. Furthermore, the probability of occurrence of relocation work is updated with the latest development plan information obtained thereafter, and the occurrence of relocation work can be accurately predicted.

  When step SC25 ends, processing unit 13 ends the update process of step SC4. When the update process ends, the processing unit 13 ends the basic process.

  Based on such basic processing, the processing unit 13 performs the relocation work occurrence probability calculation processing of the aged in step S4. The probability of occurrence of relocation work obtained in the basic process represents the probability at the time when development plan information is obtained, but in the process of calculating the probability of occurrence of relocation work over time, the development plan information is obtained from the time when the development plan information was obtained. The probability that relocation works will occur every year, such as one year later, two years later, etc. The process for calculating the probability of occurrence of relocation work over time is shown in FIG. When the processing unit 13 starts the aged transfer work occurrence probability calculation process, the processing unit 13 refers to the database in the storage device 20 and extracts related data related to the transfer work target (evaluation target) (step S81). The processing unit 13 creates transfer work data based on the extraction result (step S82). For example, when an electric wire is selected as an evaluation target in step S2, the processing unit 13 creates transfer work data shown in FIG. Here, an example is given of an electric wire when the land use classification is residential land and the surrounding environment is private ground air passage.

  When step S82 is completed, the processing unit 13 calculates an aging replacement occurrence probability (step S83). This process is based on the basic process described above. Moreover, in this embodiment, the case where the posterior probability of one year later, two years later, and five years later is calculated as an aged replacement occurrence probability is taken as an example.

また、１年後に取り替えが発生しない確率を、次の式から算出する。

また、１年後に取り替えが発生した電線のうち、最新の開発計画情報が得られる確率を、移転工事データから作成した、図６０の参照データを基にして、次の式から算出する。

また、１年後に取り替えが発生しなかった電線のうち、最新の開発計画情報が得られる確率を、移転工事データから作成した、図６１の参照データを基にして、次の式から算出する。

この後、処理部１３は、前述したベイズの定理に対して、数６６式〜数６９式の算出結果を代入し、次の式から事後確率を算出する。

つまり、最新の開発計画情報が得られた場合、電線を１年後に取り替える確率（事後確率）である移転工事発生確率が値０．１７として求められ、値０．２０の事前確率が開発計画情報によって更新されたことになる。 First, the processing unit 13 calculates a replacement occurrence probability after one year of the electric wire. The processing unit 13 calculates the replacement occurrence probability (prior probability) after one year of the electric wire from the following formula from the transfer work data created in step S82.

Also, the probability that no replacement will occur after one year is calculated from the following equation.

Moreover, the probability that the latest development plan information will be obtained among the wires that have been replaced after one year is calculated from the following formula based on the reference data of FIG. 60 created from the transfer work data.

Moreover, the probability that the latest development plan information will be obtained among the wires that have not been replaced after one year is calculated from the following formula based on the reference data of FIG. 61 created from the transfer work data.

Thereafter, the processing unit 13 substitutes the calculation results of Expressions 66 to 69 for the Bayes' theorem described above, and calculates the posterior probability from the following expression.

In other words, when the latest development plan information is obtained, the probability of relocation work occurrence, which is the probability of replacing the electric wire after one year (the posterior probability), is obtained as the value 0.17, and the prior probability of the value 0.20 is the development plan information. It has been updated by.

また、２年後に取り替えが発生しない確率を、次の式から算出する。

また、２年後に取り替えが発生した電線のうち、最新の開発計画情報が得られる確率を、移転工事データから作成した、図６２の参照データを基にして、次の式から算出する。

また、２年後に取り替えが発生しなかった電線のうち、最新の開発計画情報が得られる確率を、移転工事データから作成した、図６３の参照データを基にして、次の式から算出する。

この後、処理部１３は、ベイズの定理に対して、数７１式〜数７４式の算出結果を代入し、次の式から事後確率を算出する。

つまり、最新の開発計画情報が得られた場合、電線を２年後に取り替える確率（事後確率）である移転工事発生確率が値０．５０として求められ、値０．６０の事前確率が開発計画情報によって更新されたことになる。 Next, the processing unit 13 calculates a replacement occurrence probability after two years of the electric wire. The processing unit 13 calculates the replacement occurrence probability (prior probability) of the electric wire two years later from the following formula from the transfer work data created in step S82.

The probability that no replacement will occur after 2 years is calculated from the following equation.

Moreover, the probability that the latest development plan information will be obtained among the wires that have been replaced after two years is calculated from the following formula based on the reference data of FIG. 62 created from the transfer work data.

Moreover, the probability that the latest development plan information will be obtained among the electric wires that have not been replaced after two years is calculated from the following formula based on the reference data of FIG. 63 created from the transfer work data.

Thereafter, the processing unit 13 substitutes the calculation results of Expressions 71 to 74 for Bayes' theorem, and calculates the posterior probability from the following expression.

In other words, when the latest development plan information is obtained, the probability of relocation work occurrence, which is the probability of replacing an electric wire after two years (the posterior probability), is obtained as a value of 0.50, and the prior probability of a value of 0.60 is the development plan information. It has been updated by.

また、５年後に取り替えが発生しない確率を、次の式から算出する。

また、５年後に取り替えが発生した電線のうち、最新の開発計画情報が得られる確率を、移転工事データから作成した、図６４の参照データを基にして、次の式から算出する。

また、５年後に取り替えが発生しなかった電線のうち、最新の開発計画情報が得られる確率を、移転工事データから作成した、図６５の参照データを基にして、次の式から算出する。

この後、処理部１３は、ベイズの定理に対して、数７６式〜数７９式の算出結果を代入し、次の式から事後確率を算出する。

つまり、最新の開発計画情報が得られた場合、電線を５年後に取り替える確率（事後確率）である移転工事発生確率が値０．８３として求められ、値０．８０の事前確率が開発計画情報によって更新されたことになる。 Finally, the processing unit 13 calculates the replacement occurrence probability after 5 years of the electric wire. The processing unit 13 calculates the replacement occurrence probability (prior probability) of the electric wire 5 years later from the following formula from the transfer work data created in step S82.

The probability that no replacement will occur after 5 years is calculated from the following equation.

Moreover, the probability that the latest development plan information will be obtained among the wires that have been replaced after five years is calculated from the following formula based on the reference data of FIG. 64 created from the transfer work data.

Moreover, the probability that the latest development plan information will be obtained among the wires that have not been replaced after 5 years is calculated from the following formula based on the reference data of FIG. 65 created from the transfer work data.

Thereafter, the processing unit 13 substitutes the calculation results of Expressions 76 to 79 for Bayes' theorem, and calculates the posterior probability from the following expression.

In other words, when the latest development plan information is obtained, the probability of relocation work occurrence, which is the probability of replacing an electric wire after five years (the posterior probability), is obtained as a value 0.83, and the prior probability of a value 0.80 is the development plan information. It has been updated by.

  In this way, the processing unit 13 calculates the aging posterior probability, that is, the aging replacement probability in step S83. Thereafter, the processing unit 13 creates aged relocation work occurrence probability data shown in FIG. 66 from the calculated replacement probability (step S84). In general, development plan information is not always available. For this reason, a predetermined period is set, and the probability of occurrence of relocation work is calculated based on the information obtained within this period. As a result, the probability of occurrence of relocation work obtained becomes discrete data such as one year later and two years later. Therefore, the processing unit 13 uses the relocation work occurrence probability data to calculate an expression representing the relationship between the aging and the relocation work occurrence probability, that is, an expression expressed in the form of y = a + bx by the least square method ( Step S85).

数８１式の演算結果を最小２乗法の数２５式に当てはめて、処理部１３は次の式を得る。

同じように、処理部１３は図６７（ａ）の座標データを利用して、次の演算を行う。

数８３式の演算結果を最小２乗法の数２６式に当てはめて、処理部１３は次の式を得る。

この後、処理部１３は、数８２式と数８４式とを連立方程式とし、

この連立方程式を解くと、次の解を得る。

さらに、処理部１３は、この解から次の回帰直線を得る。

When the aged relocation work occurrence probability data shown in FIG. 66 is used, the processing unit 13 performs step S85 as follows. The coordinate data obtained from the relocation work occurrence probability data is as shown in FIG. 67 (a). These relationships are shown in FIG. 67 (b) when the relocation work occurrence probability is taken as the y-axis and the aging is taken as the x-axis. It will be in the state shown in The processing unit 13 performs the following calculation using the coordinate data of FIG.

By applying the calculation result of Formula 81 to Formula 25 of the least square method, the processing unit 13 obtains the following formula.

Similarly, the processing unit 13 performs the following calculation using the coordinate data shown in FIG.

By applying the calculation result of Expression 83 to Expression 26 of the least square method, the processing unit 13 obtains the following expression.

Thereafter, the processing unit 13 sets the equations 82 and 84 as simultaneous equations,

Solving these simultaneous equations gives the following solution:

Further, the processing unit 13 obtains the next regression line from this solution.

  When the processing unit 13 obtains the regression line, it ends the aged occurrence probability calculation process in step S4. This regression line is an expression representing the aging risk, and this straight line is shown in FIG. By using this regression line, it is possible to obtain the transfer work occurrence probability in any year and the transfer work occurrence probability in units of months during the year. The regression line is updated using Bayes' theorem with the latest development plan information. As a result, the probability of future transfer work occurrence can be obtained with high accuracy.

The processing unit 13 is a device that has been selected by a series of processes in steps S3 to S6.
Probability of occurrence of failure over time Probability of supply over time Proceed with calculation of probability of occurrence of relocation over time. Thereafter, the processing unit 13 creates risk data based on the data obtained by the series of processes in steps S3 to S6 (step S7). An example of this risk data is shown in FIG. In this risk data, the values of one year, two years, and five years after each risk of utility poles, electric wires, and transformers, and a regression line are shown.

  Thereafter, the processing unit 13 selects a risk type (step S8), calculates the degree of influence on the amount of damage (step S9), and creates influence degree data from the calculation result (step S10). Thereafter, the processing unit 13 determines whether there is an unselected risk (step S11). If there is an unselected risk, the processing unit 13 selects another risk (step S12), and returns the process to step S9. The processing unit 13 calculates the degree of influence corresponding to each risk through a series of processes in steps S8 to S12.

この後、処理部１３は、図７０に示すように、算出した各故障影響度を表す故障影響度データを作成する。図７０には、図７１に示すように、複数の機器が設置されている各電柱の故障影響度が記録されている。 For example, when failure is selected as the risk type in step S8, the processing unit 13 refers to the failure rank database 21K stored in the storage device 20, ranks the damage amount, and uses the following formula: To calculate the failure impact level of each device.

Thereafter, as illustrated in FIG. 70, the processing unit 13 creates failure influence degree data representing each calculated failure influence degree. In FIG. 70, as shown in FIG. 71, the failure influence degree of each utility pole in which a plurality of devices are installed is recorded.

It should be noted that the restoration time and restoration work cost of each device of the failure influence degree are given in advance. Moreover, the number of power outages in the social impact level in the failure impact level is linked to the supply unit database (not shown) for each device of each power pole. As the social influence degree, demand density [kW / km ² ], presence / absence of a large-scale factory, etc. may be used in addition to the number of power outages. Furthermore, the number of restoration personnel [persons] may be added to each item of the failure impact level. That is, the greater the number of items and the higher the rank classification, the more detailed the failure influence degree can be calculated.

この後、処理部１３は、図７２に示すように、算出した各供給影響度を表す影響度データを作成する。 If supply is selected as the risk type in step S8, the processing unit 13 refers to the supply rank database 21L stored in the storage device 20, ranks the damage amount, and uses the following formula: To calculate the supply impact of each device.

Thereafter, as shown in FIG. 72, the processing unit 13 creates influence degree data representing each calculated supply influence degree.

  Note that the number of power outages for each device of supply influence is linked to a supply number database (not shown) for each power pole device. The investment recovery year represents the number of years in which the supply construction cost can be recovered based on the income of electricity charges, etc., and is linked to a contract database (not shown). Furthermore, the completion inspection cost [10,000 yen] and the number of supply construction personnel [persons] may be added to each item of the supply impact level. That is, the greater the number of items and the greater the number of ranks, the more detailed the supply influence can be calculated.

この後、処理部１３は、図７３に示すように、算出した各供給影響度を表す影響度データを作成する。 Furthermore, when transfer is selected as the risk type in step S8, the processing unit 13 refers to the transfer rank database 21M stored in the storage device 20, ranks the damage amount, and uses the following formula: To calculate the supply impact of each device.

Thereafter, as illustrated in FIG. 73, the processing unit 13 creates influence degree data representing each calculated supply influence degree.

  In addition, the number of power outages for each device of the degree of relocation influence is linked to a supply number database (not shown) for each device of each power pole. The site negotiation costs represent application costs associated with utility pole relocation, etc., and utility pole site fee generation costs, etc., and are linked to a utility pole site database (not shown). Furthermore, the completion inspection cost [10,000 yen] and the number of relocation works [persons] may be added to each item of the impact of relocation. That is, the greater the number of items and the greater the number of ranks, the more detailed the supply influence can be calculated.

In this way, the processing unit 13 has the selected device by a series of processes S8 to S12.
Failure impact level Supply impact level When the calculation of the transfer impact level is completed, the calculation result is reflected in the risk data (step S13). Specifically, the failure impact data in FIG. 70, the supply impact data in FIG. 72, and the transfer impact data in FIG. 73 are reflected in the risk data. Thereby, the risk data becomes as shown in FIG.

さらに、機器毎の各経年リスクを加算して、図７６に示すように、経年のリスク総和を求める（ステップＳ１５）。 Thereafter, the processing unit 13 calculates an aging risk as shown in FIG. 75 using the following formula for the risk data created in step S13 (step S14).

Further, the aging risks for each device are added to obtain the aging risk sum as shown in FIG. 76 (step S15).

この実施の形態では、平均μ±３σを限界リスクとして用いる。そして、限界リスクを算出する方法は次のようになる。例えば、経年のリスク総和が図７７（ａ）に示す経年リスクデータで与えられていると、処理部１３は、限界リスクに必要とする平均μおよび標準偏差σを、次の式から算出する。

この後、処理部１３は、算出した値を用いて、次のようにして限界リスクを算出する。

また、経年リスクデータが例えば図７７（ｂ）に示すように更新されても、同様の方法で限界リスクは更新される。 When step S15 ends, the processing unit 13 calculates a time when the total risk of each device exceeds the limit risk (step S16). The processing unit 13 calculates the limit risk used in step S16 as follows. Generally, in quality control, a control limit is set, and when the control limit is exceeded, it is determined that there is an abnormality and necessary processing is performed. There are the following calculation methods for control limits. The standard deviation σ is obtained for the target population, and the average μ ± 3σ is set as the control limit by taking three times the standard deviation σ. The average μ and standard deviation σ are calculated from the following equations.

In this embodiment, the average μ ± 3σ is used as the limit risk. The method for calculating the marginal risk is as follows. For example, when the aged risk sum is given by the aged risk data shown in FIG. 77 (a), the processing unit 13 calculates the average μ and the standard deviation σ required for the marginal risk from the following equations.

Thereafter, the processing unit 13 uses the calculated value to calculate the limit risk as follows.

Even if the aging risk data is updated as shown in FIG. 77B, for example, the marginal risk is updated in the same manner.

を満足する時期ｘを算出する。この様子を図７８に示す。処理部１３は、数９５式から、次の値を算出し、

リスク総和が限界リスクを超過する時期として２．５年を得る。 Using the marginal risk calculated in this manner, the processing unit 13 calculates a time when the total risk over time exceeds the marginal risk in step S16. Specifically, if the marginal risk of the utility pole is given as a value of 30,

The time x that satisfies the above is calculated. This is shown in FIG. The processing unit 13 calculates the following value from Equation 95,

Gain 2.5 years as the time when the total risk exceeds the marginal risk.

そして、処理部１３は、単位コストの算出式から単位コストを算出し、単位コストが定められた限界単位コストを下回る時期を算出する（ステップＳ１８）。具体的には、リスクの種類が故障、供給、移転であるので、数９７式の機器の各リスクの総和は、３リスク総和となる。例えば、「〇〇幹１号電柱」の取り替え費用が５０万円、限界単位コストが１．５として与えられていると、処理部１３は、図７６のリスクデータを参照して次の式から、

を満足する時期ｘを算出する。時期ｘは、

となり、単位コストが限界単位コストを下回る時期として３．２年を得る。 When step S16 ends, the total risk over time has been digitized, so the processing unit 13 obtains a replacement cost per unit risk of the device, that is, a calculation formula for unit cost, from the following relationship (step S17).

Then, the processing unit 13 calculates a unit cost from the unit cost calculation formula, and calculates a time when the unit cost falls below the determined limit unit cost (step S18). Specifically, since the types of risk are failure, supply, and transfer, the sum total of each risk of the equipment of Formula 97 is a total of three risks. For example, if the replacement cost of “00 Trunk No. 1 utility pole” is given as 500,000 yen and the limit unit cost is 1.5, the processing unit 13 refers to the risk data in FIG. ,

The time x that satisfies the above is calculated. Time x is

Thus, 3.2 years is obtained as the period when the unit cost falls below the limit unit cost.

  Thereafter, the processing unit 13 determines the later time as the replacement time from the time calculated in step S16 and the time calculated in step S18 (step S19). Specifically, since the time obtained in step S16 is 2.5 years and the time in step S18 is 3.2 years, the processing unit 13 sets the replacement time of “XX trunk 1 utility pole” to 3 Judged as 2 years. In step S19, the processing unit 13 determines that a limit is reached when the time when any one of the three occurrence probabilities of failure, supply, and transfer exceeds a predetermined limit probability is earlier than the time determined as the replacement time. The time when the probability is exceeded is the replacement time. Specifically, if a marginal probability of 95 [%] is given, the time at which all three occurrence probabilities exceed 95 [%] is after 3.2 years, so the replacement time is the same as initially determined. 3. 2 years.

  Thereafter, the processing unit 13 outputs the replacement time obtained in step S19 to the display unit 11 or the like (step S20), and ends the single risk evaluation process.

  Next, a facility risk evaluation method using the facility risk evaluation system of this embodiment will be described. When the person in charge operates the operation unit 12 to start up the equipment risk evaluation system, the risk evaluation apparatus 10 starts a single risk evaluation process, and selects, for example, a device installed in a power distribution facility (electric pole). An input screen for displaying is displayed on the display unit 11. When the person in charge operates the operation unit 12 to select a device, the risk evaluation apparatus 10 calculates the replacement time of the selected device and outputs it to the display unit 11, and then ends the single risk evaluation process.

  Thus, according to this embodiment, it is possible to perform risk assessment by calculating an aging risk from the probability of occurrence for each replacement factor and calculating an optimal replacement time from the aging risk and replacement cost. In addition, since the replacement time is output based on various data and by simple risk calculation, it is possible to perform a quick risk assessment and a reliable risk assessment.

(Embodiment 2)
In the first embodiment, the replacement time is calculated for one device of the power distribution facility. However, in this embodiment, when replacing one device, that is, the target device, another device (hereinafter referred to as “another device”). Risk assessment is also performed by synchronous replacement that replaces equipment). In this embodiment, components and processes that are considered to be the same as or the same as those of the first embodiment described above are denoted by the same reference numerals, and description thereof is omitted.

  FIG. 79 shows a synchronization risk evaluation process for performing a risk evaluation by synchronization replacement. When starting the synchronization risk evaluation process, the processing unit 13 displays an input screen for inputting a device of the distribution facility and another device (step S101). Specifically, a screen for selecting a device installed on one utility pole and a device of a utility pole that is the same as or adjacent to this utility pole is displayed. When a device is selected by operating the operation unit 12, the processing unit 13 receives a selection result from the operation unit 12 (step S102). Specifically, for example, when replacing “XX Trunk No. 1 utility pole” and the target of synchronous replacement is “XX Trunk No. 1 transformer”, in Step S102, “XX Trunk No. 1 utility pole” is displayed. “XX trunk 1 transformer” is received as a selection result.

  Thereafter, the processing unit 13 selects one of the devices selected in Step S102 (Step S103) and performs a single risk evaluation process (Step S104). The single risk evaluation process in step S104 is the process in steps S3 to S20 in the first embodiment. When step S104 ends, the processing unit 13 determines whether there is an unselected device (step S105). If there is an unselected device, the processing unit 13 selects another device (step S106), and returns the process to step S104.

とする。また、「○○幹１号変圧器」の限界リスクを値４とすると、

を満足する時期を算出する。この様子を図８０に示す。処理部１３は、数１０１式から、次の値を算出し、

リスク総和が限界リスクを超過する時期として２．８年を得る。 As a result of such a series of processing, specifically, as described in the first embodiment, the replacement timing of “XX trunk 1 utility pole” is determined to be 3.2 years. For “XX Trunk No. 1 Transformer”, the processing unit 13 uses the risk data of FIG.

And In addition, if the limit risk of “XX Trunk No. 1 Transformer” is a value of 4,

The time to satisfy is calculated. This is shown in FIG. The processing unit 13 calculates the following value from Equation 101,

2.8 years will be obtained as the time when the total risk exceeds the limit risk.

を満足する時期ｘを算出する。時期ｘは、

となり、取り替え時期として２．５年を得る。 Further, the processing unit 13 calculates a unit cost from the three risk sums of “XX Trunk No. 1 transformer” and the replacement cost, and calculates a time when the unit cost falls below a predetermined limit unit cost. For example, if the replacement cost of “XX trunk 1 transformer” is given as 100,000 yen and the limit unit cost of “XX trunk 1 transformer” is 2.7, the processing unit 13 From the formula

The time x that satisfies the above is calculated. Time x is

It will be 2.5 years as a replacement time.

  As a result, the later time 2.8 years from the two calculated times is determined as the replacement time of “XX trunk 1 transformer”. Note that the processing unit 13 replaces the time when the limit probability is exceeded when the time when any one of the three occurrence probabilities exceeds the predetermined limit probability is earlier than the time determined as the replacement time. It is time. Specifically, when a marginal probability of 95 [%] is given, the time when all three occurrence probabilities exceed 95 [%] is after 2.8, so the replacement time is the same as initially determined. 2.8 years.

なお、数１０５式では、「Ａ」、「Ｂ」が機器をそれぞれ表している。 In this way, when a series of processing of Steps S103 to S106 is completed, the processing unit 13 calculates a replacement cost reduction rate by synchronous replacement from the following formula (Step S107), and the calculation result is a predetermined marginal cost reduction. It is determined whether the rate is exceeded (step S108).

In Equation 105, “A” and “B” represent devices.

となる。したがって、処理部１３は、同調取り替えによる費用低減率８．３［％］が限界費用低減率を超えていると判定する。 Specifically, for example, the replacement cost of the target equipment “XX Trunk No. 1 power pole” alone is 500,000 yen, the replacement cost of another equipment “XX Trunk No. 1 transformer” alone is 100,000 yen, If the cost of synchronous replacement of “○ Trunk No. 1 Utility Pole” and “XX Trunk No. 1 Transformer” is 550,000 yen and the marginal cost reduction rate is 5.0 [%], these values are expressed in Equation 105. When applied,

It becomes. Therefore, the processing unit 13 determines that the cost reduction rate 8.3 [%] due to the synchronous replacement exceeds the limit cost reduction rate.

なお、数１０７式では、「Ａ」、「Ｂ」が機器をそれぞれ表している。 When step S108 is completed in this way, the processing unit 13 calculates a unit cost reduction rate due to synchronous replacement of the target device and another device from the following equation (step S109), and a limit unit cost reduction in which the calculation result is predetermined. It is determined whether the rate is exceeded (step S110).

In Equation 107, “A” and “B” represent devices.

で算出され、別機器である「○○幹１号変圧器」の単位コストは、

で算出される。また、「○○幹１号電柱」と「○○幹１号変圧器」の同調取り替えの単位コストは、

で算出される。ここでは、限界単位コスト低減率が例えば５０［％］として与えられていると、処理部１３は、「○○幹１号電柱」の取り替え時期である３．２年後（ｘ＝３．２）の、対象機器「○○幹１号電柱」の単位コスト、別機器「○○幹１号変圧器」の単位コスト、同調取り替えの単位コストを次の式でそれぞれ計算する。

処理部１３は、これらの計算結果から単位コスト低減率を、

として得る。したがって、処理部１３は、単位コスト低減率が限界単位コスト低減率を超えていると判定する。 Specifically, for example, the unit cost of the target device “XX Trunk No. 1 power pole”

The unit cost of “XX Trunk No. 1 Transformer”, which is calculated by

Is calculated by The unit cost of synchronous replacement of “XX Trunk No. 1 Utility Pole” and “XX Trunk No. 1 Transformer” is

Is calculated by Here, when the marginal unit cost reduction rate is given as, for example, 50 [%], the processing unit 13 is 3.2 years later (x = 3.2) which is the replacement time of “XX trunk 1 power pole”. ), The unit cost of the target device “XX trunk 1 utility pole”, the unit cost of another device “XX trunk 1 transformer”, and the unit cost of synchronous replacement are respectively calculated by the following equations.

The processing unit 13 calculates the unit cost reduction rate from these calculation results,

Get as. Therefore, the processing unit 13 determines that the unit cost reduction rate exceeds the limit unit cost reduction rate.

When step S110 ends, the processing unit 13 determines the tuning replacement based on the processing result including steps S103 to S105, the determination result of step S108, and the determination result of step S110 (step S111). . In particular,
(Sa) The replacement time of “XX Trunk No. 1 Transformer” after 2.8 years is earlier than the replacement timing of “XX Trunk No. 1 Telephone Pole” after 3.2 years. 8.3 [%] exceeds the marginal cost reduction rate 5.0 [%] All the conditions that the unit cost reduction rate 61 [%] exceeds the marginal unit cost reduction rate excess 50 [%] are satisfied. If it is determined, the processing unit 13 determines that “XX Trunk No. 1 Transformer” will be replaced with “XX Trunk No. 1 Utility Pole” after 3.2 years.

  Thereafter, the processing unit 13 outputs the determination result of step S111 to the display unit 11 or the like (step S112), and finishes the tuning risk evaluation process.

  In this embodiment, the method for determining whether or not to perform synchronous replacement is described for other equipment installed on the same utility pole. By calculating, it can be similarly determined whether or not the tuning change is performed.

  In this embodiment, in the specific example of step S111, it is determined that the tuning replacement is performed only when the above three conditions (sa) to (su) are satisfied. However, if the two conditions are satisfied, the tuning is performed. You may determine with replacement.

  Thus, according to this embodiment, the aging risk is calculated from the probability of occurrence for each replacement factor, and the optimal replacement time is calculated from the aging risk and the replacement cost, so that other equipment installed on the same utility pole can be calculated. From the deterioration degree, the enhancement plan, the deterioration degree of the adjacent utility pole equipment, the relocation plan, etc., it is possible to quantitatively obtain the equipment to be tuned and the optimum replacement time.

  As mentioned above, although each embodiment of this invention has been described in detail, the specific configuration is not limited to each embodiment, and even if there is a design change or the like without departing from the gist of this invention, It is included in this invention. For example, the present invention can be utilized for daily power distribution design work and annual facility replacement plans. Moreover, it is not limited to electric power facilities, and can be used for a replacement plan for facilities laid out in a wide area and network under various environments such as communication, gas, and water.

It is a block diagram which shows the risk evaluation system of the installation by Embodiment 1. FIG. It is a figure which shows an example of an installation date database. It is a figure which shows an example of a material structure database. It is a figure which shows an example of an operation state database. It is a figure which shows an example of a failure database. It is a figure which shows an example of a land use classification database. It is a figure which shows an example of a demand increase rate database. It is a figure which shows an example of a supply construction performance database. It is a figure which shows an example of a surrounding environment database. It is a figure which shows an example of a transfer construction results database. It is a figure which shows an example of a failure rank database. It is a figure which shows an example of a supply rank database. It is a figure which shows an example of a transfer rank database. It is a flowchart which shows a single-piece | unit risk evaluation process. It is a flowchart which shows a single-piece | unit risk evaluation process. It is a flowchart which shows an aged failure occurrence probability calculation process. It is a flowchart which shows an aged supply construction occurrence probability calculation process. It is a flowchart which shows aged relocation construction occurrence probability calculation processing. It is a flowchart which shows a basic process. It is a flowchart which shows an update process. It is explanatory drawing explaining the production | generation of combination data. It is explanatory drawing explaining the past failure performance data. It is a figure which shows the latest patrol performance data. It is a figure which shows abnormality discovery data. It is a figure which shows abnormality discovery data. It is a figure which shows track record data. It is a figure which shows reference data. It is a figure which shows reference data. It is a figure which shows reference data. It is a figure which shows reference data. It is a figure which shows reference data. It is a figure which shows reference data. It is a figure which shows aged failure probability data. It is a figure which shows a regression line. FIGS. 35A and 35B are explanatory diagrams for graphing failure probability data over time, FIG. 35A is a diagram illustrating coordinate data, and FIG. 35B is a diagram illustrating the coordinate data as a graph. It is a figure showing a regression line. It is a flowchart which shows a basic process. It is a flowchart which shows a correction process. It is a figure which shows combination data. It is a figure which shows supply construction data. It is a figure which shows replacement data. It is a figure which shows replacement data. It is a figure which shows supply construction data. It is a figure which shows reference data. It is a figure which shows reference data. It is a figure which shows reference data. It is a figure which shows reference data. It is a figure which shows reference data. It is a figure which shows reference data. It is a figure which shows aged construction work probability data. FIG. 51A is an explanatory diagram for graphing supply construction occurrence probability data over time, FIG. 51A is a diagram illustrating coordinate data, and FIG. 51B is a diagram in which the coordinate data is graphed. It is a figure showing a regression line. It is a flowchart which shows a basic process. It is a flowchart which shows a correction process. It is a figure which shows combination data. It is a figure which shows transfer construction data. It is a figure which shows replacement data. It is a figure which shows replacement data. It is a figure which shows transfer construction data. It is a figure which shows reference data. It is a figure which shows reference data. It is a figure which shows reference data. It is a figure which shows reference data. It is a figure which shows reference data. It is a figure which shows reference data. It is a figure which shows aged transfer construction probability data. FIG. 67A is an explanatory diagram for graphing the aged relocation work occurrence probability data, FIG. 67A is a diagram illustrating coordinate data, and FIG. 67B is a diagram in which the coordinate data is graphed. It is a figure showing a regression line. It is a figure which shows an example of risk data. It is a figure which shows an example of failure influence degree data. It is a figure which shows the apparatus installed in the utility pole. It is a figure which shows an example of supply influence degree data. It is a figure which shows an example of transfer influence degree data. It is a figure which shows an example of risk data. It is a figure which shows an example of risk data. It is a figure which shows an example of risk data. It is a figure which shows an example of aged risk data, Fig.77 (a) is the data before update, FIG.77 (b) is the data after update. It is a figure explaining calculation of replacement time. 6 is a flowchart showing a synchronization risk evaluation process according to Embodiment 2. It is a figure explaining calculation of replacement time.

Explanation of symbols

10 Risk assessment device (processing means)
DESCRIPTION OF SYMBOLS 11 Display part 12 Operation part 13 Processing part 14 Storage part 15 Interface 20 Storage device (storage means)

Claims (6)

Storage means for storing data indicating the state of the device to be replaced while being installed in the facility;
Processing means for calculating the replacement time of the target device by a single risk assessment process,
The processing means includes
Based on the data representing the state of the target device stored in the storage means, for each replacement factor for replacing the target device, to calculate a risk value according to the passage of time,
Calculate the time when the sum of the risk values over time has exceeded the preset limit risk value as the first replacement time,
Based on the sum of the risk values and the replacement cost of the target device, calculate the replacement cost per unit risk as the unit cost,
Calculate the preset limit unit cost when the unit cost is lower as the second replacement time,
The later of these replacement times is the replacement time for the target device.
A facility risk evaluation system characterized by performing the unit risk evaluation process. The processing means obtains the risk for each replacement factor by integrating the occurrence probability of the replacement factor and the degree of influence when the replacement factor occurs.
The equipment risk evaluation system according to claim 1. When the risk value is a discrete value, the processing means uses a least square method based on each discrete value to calculate a risk value corresponding to the passage of time.
The equipment risk evaluation system according to claim 1 or 2, characterized in that The processing means updates the probability of occurrence using the Bayes' theorem based on the latest data in the state of the target device stored in the storage means.
The equipment risk evaluation system according to claim 3. The processor is
Perform the unit risk assessment process on another device different from the target device, calculate the replacement time,
Based on the replacement cost when replacing the target device and another device alone and the replacement cost when replacing both the target device and another device, calculate the reduction rate of the cost by replacing the two devices,
Calculate the unit cost reduction rate by replacing two devices,
The condition that the replacement time of the other device is earlier than the replacement time of the target device, the condition that the cost reduction rate exceeds the preset marginal cost reduction rate, and the preset limit unit cost reduction Determining the rate as a tuned replacement that replaces both the target device and another device when at least one of the conditions for exceeding the unit cost reduction rate is satisfied,
The equipment risk evaluation system according to any one of claims 1 to 4, wherein Store in advance data representing the status of the equipment that is installed in the facility and that is subject to replacement,
Processing means for calculating the replacement time of the target device by a single risk assessment process,
Based on the data representing the state of the target device stored in advance, for each replacement factor for replacing the target device, to calculate a risk value according to the passage of time,
Calculate the time when the sum of the risk values over time has exceeded the preset limit risk value as the first replacement time,
Based on the sum of the risk values and the replacement cost of the target device, calculate the replacement cost per unit risk as the unit cost,
Calculate the preset limit unit cost when the unit cost is lower as the second replacement time,
The later of these replacement times is the replacement time for the target device.
This is a risk assessment method for equipment.

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