JP6367757B2 - Management method and management apparatus - Google Patents

Description

  The present invention relates to a management method and a management apparatus.

  Aging of social infrastructure equipment (hereinafter referred to as infrastructure equipment) that supports modern society is regarded as a problem. Most infrastructure facilities in Japan are constructed and installed during the period of high growth. Functions are maintained by maintenance such as repairs and renewals, but the failure rate is expected to increase with the increase in equipment with a long service life, and the maintenance management cost is expected to increase.

  The infrastructure facilities are characterized by the variety of installation environments and the number and variety of installation environments. As a maintenance management method for such facilities, time-base maintenance represented by periodic inspection is often applied. Since this is carried out on the basis of a uniform inspection cycle, there are advantages that maintenance management planning and cost calculation are relatively easy, and that it has a high affinity with legal inspections defined by various laws.

  In recent years, however, equipment deterioration has progressed, while maintenance costs have been reduced due to intensifying management competition, and maintenance personnel have decreased due to massive retirement of baby boomers. Operation and cost required for maintenance management in Japan became issues. Therefore, the risk-based inspection / risk-based maintenance (RBI / RBM) method, which is ahead in the plant industry, is said to be effective for maintenance management of infrastructure facilities in recent years (for example, Non-Patent Documents 1 and 2). .

  Specifically, the time-based maintenance is a process for formulating a maintenance plan based on an event that a target facility cannot maintain a predetermined function as time passes or when a predetermined time elapses. For this reason, for example, if the years of use of the equipment to be maintained and the time interval for performing maintenance are determined, the operation can be basically performed without using a standard other than time. Most of the inspections stipulated by law, so-called legal inspections, are time-based maintenance.

  Risk-based maintenance, on the other hand, evaluates the risk of equipment failure (ie, the likelihood of failure) and the magnitude of damage caused by equipment failure (ie, the degree of impact due to the failure). This is a method for preparing a maintenance plan for equipment based on the risk matrix.

  However, when trying to shift from time-based maintenance to risk-based maintenance, the following problems occur.

  For example, time-base maintenance and risk-based maintenance have different maintenance plan formulation principles as described above, and therefore data required for implementation, maintenance procedures, inspection items, and the like may be different. For example, to implement risk-based maintenance, it is necessary to evaluate the risk of failure occurrence and the magnitude of damage to each part of the equipment. The required data may not be included, and extra checks must be performed to obtain this data.

  For this reason, in order to shift from time-based maintenance to risk-based maintenance, instead of applying risk-based maintenance to all facilities at once, places, places, ranges, etc. where risk-based maintenance should be applied with priority. It is realistic to sequentially apply risk-based maintenance from the selected equipment after selecting (screening). Therefore, in order to realize the transition from time-based maintenance to risk-based maintenance without excessive burden, the scope of maintenance and improvement of predetermined functions, safety and economy, etc. by risk-based maintenance, It is necessary to screen the range that can be maintained and improved by timebase maintenance. In formulating a maintenance plan, it is necessary to grasp changes in maintenance operations in the future due to an increase in failure rate as equipment deterioration progresses.

Toshikazu Shibasaki, Current Status of Risk-Based Inspection and Maintenance in Petroleum / Chemical Plants, REAJ 2007 Vol.29, No.4 Hirofuku Fukuyama et al., Application of risk assessment method in railway business, Social Technology Research Vol.5, 163-171, Mar. 2008

  However, conventionally, there have been few studies on a method for selecting a target range in which risk-based maintenance should be prioritized and a method for predicting a change in maintenance operation due to application of risk-based maintenance.

  In view of the above, the present invention provides a method for selecting a target range where risk-based maintenance should be prioritized, and a management method and a management device that can easily predict maintenance operations required due to an increase in the failure rate in the future. To do.

  In order to solve the above-described problems and achieve the object, the management method of the present invention is a management method executed by a management apparatus, and includes equipment information relating to each equipment that is a management target, and inspection of each equipment. The probability calculation step of calculating the failure occurrence probability of each facility in the future based on the inspection information regarding, and the degree of influence at the time of failure of each facility based on the information regarding the area where each facility is disposed, respectively A risk that is a risk value in the future by using the impact calculation step to be calculated, the failure occurrence probability of each facility calculated by the probability calculation step, and the impact of each facility calculated by the impact calculation step Prediction process for predicting the predicted value for each area, and whether the risk predicted value of each area predicted by the prediction process is greater than a preset risk tolerance All the facilities or some of the facilities arranged in the determination process for determining and the areas where the risk prediction value is determined to be larger than the risk allowable value by the determination process are subject to risk-based maintenance. And a selection process for selecting as a feature.

  In addition, the management device of the present invention includes a probability calculation unit that calculates a failure occurrence probability of each facility in the future based on facility information regarding each facility that is a management target and inspection information regarding inspection of each facility, , Based on information about the area where each facility is located, an impact degree calculating unit that calculates the degree of influence at the time of failure of each facility, the failure occurrence probability of each facility calculated by the probability calculating unit, and the A prediction unit that predicts a risk prediction value, which is a risk value in the future, for each area using the influence degree of each facility calculated by the influence calculation unit, and a risk prediction of each area predicted by the prediction unit A determination unit for determining whether the value is greater than a preset risk allowable value; and the determination unit determines that the risk prediction value is greater than the risk allowable value. All equipment or part of equipment disposed by area, characterized in that it comprises a selection unit for selecting as a target facility RBM.

  By using the technology of the present invention, it is possible to grasp the target equipment that should be prioritized for risk-based maintenance, and it is possible to easily predict changes in the maintenance operation of the equipment as the equipment deteriorates in the future.

FIG. 1 is a diagram illustrating an example of a configuration of an equipment maintenance management apparatus. FIG. 2 is a diagram illustrating an example of a steel pipe column facility database. FIG. 3 is a diagram showing an example of a steel pipe column inspection information database. FIG. 4 is a diagram illustrating an example of a general statistical information database. FIG. 5 is a Weibull probability paper plot of steel pipe column failure data. FIG. 6 is a diagram illustrating an example of an influence degree calculation method. FIG. 7 is a flowchart showing equipment maintenance management processing. FIG. 8 is a diagram illustrating an example of risk value prediction. FIG. 9 is a diagram illustrating an example of renewal target selection. FIG. 10 is a diagram illustrating an example of facility renewal rate calculation. FIG. 11 is a diagram illustrating a computer that executes a management program.

  Hereinafter, embodiments of a management method and a management apparatus according to the present application will be described in detail with reference to the drawings. In addition, the management method and management apparatus which concern on this application are not limited by this embodiment.

  In the following embodiments, the configuration of the equipment maintenance management device according to the embodiment, the flow of processing of the equipment maintenance management device will be described in order, and finally the effects of the embodiment will be described. Moreover, in this embodiment, although the steel pipe pillar which is one of the communication infrastructure facilities installed outdoors is made into the management object, the management object is not limited to this.

(Configuration of equipment maintenance management device)
FIG. 1 is a diagram illustrating an example of a configuration of an equipment maintenance management apparatus. As shown in FIG. 1, databases 11, 12, 13 and an output device 300 are connected to the equipment maintenance management device 200. Note that the databases 11, 12, 13 and the output device 300 may be incorporated in the equipment maintenance management device 200.

  The equipment database 11 stores information on each equipment that is a management target. Specifically, the equipment database 11 stores at least identification information of steel pipe columns, position information, construction date, and negotiation line information. The inspection information database 12 stores information related to inspection of each facility. Specifically, the inspection information database 12 stores at least identification information and inspection results of steel pipe columns, and accumulates a sufficient number of records (data number). The general statistical information database 13 stores information related to the area where each facility is arranged. Specifically, the general statistical information database 13 stores at least population information, traffic information, and building information. The output device 300 is an output device such as a display or a printer that outputs the result of the equipment renewal rate by the equipment maintenance management device 200, the renewal target, and the inspection plan.

(Database)
As shown in FIG. 2, the facility database 11 includes columns of “prefecture area”, “facility number”, “location”, “construction date”, and “number of interfering lines”. “Prefectural area” is location information indicating an area where a steel pipe column corresponding to the record is located. “Equipment number” is, for example, identification information for uniquely identifying a steel pipe column corresponding to the record. The “location position” is, for example, position information indicating the latitude and longitude of the steel pipe column corresponding to the record. “Construction date” is, for example, time information indicating the date when the steel pipe column corresponding to the record is installed. The “number of lines to be negotiated” is additional equipment information indicating the number of lines to be negotiated with the steel pipe pillar corresponding to the record.

  As shown in FIG. 3, the inspection information database 12 includes columns of “prefecture area”, “facility number”, “latest inspection date”, and “deterioration state”. “Latest inspection date” is, for example, time information indicating the date when the steel pipe column corresponding to the record was last inspected by the inspection worker. The “degraded state” is, for example, a stepwise result obtained by quantitatively and / or qualitatively evaluating according to a predetermined evaluation item when a steel pipe column corresponding to the record is inspected last by an inspection worker. It is information to show.

  “Degraded state” includes, for example, states from “1” to “4”. “Deterioration state” “1” indicates a state in which the deterioration is most advanced. “Degraded state” “2” indicates a state in which the deterioration has progressed after “Degraded state” “1”. “Deterioration state” “3” indicates a state in which the deterioration has progressed after “Deterioration state” “2”. “Degraded state” “4” indicates a state in which the deterioration is least advanced or a non-degraded state. In the example shown in FIG. 2 and FIG. 3, for example, the steel pipe columns of “prefecture” “A”, “equipment number” “XX1”, “construction date” “April 1979” are “latest inspection date”. “November 2011”, “deterioration state” “1”. In the present embodiment, “degraded state” “1” data is defined as deemed failure data, and other “degraded state” data is defined as non-failure data.

  As shown in FIG. 4, the general statistical information database 13 includes columns such as “prefecture area”, “equipment number”, “steel column length”, “influence area”, “distance to building”, “population”, and “traffic volume”. Have. The “steel pipe column length” is information indicating the height of the steel pipe column. “Affected area” is information indicating a range of influence when a steel pipe column fails (breaks). Here, a circle whose radius is the height of the steel pipe column is assumed. “Distance to building” is information indicating the distance from the position of the steel pipe column to the nearest building. The “population number” is information indicating the number of populations existing within the influence range. The “traffic volume” is information indicating the traffic volume passing within the influence range. The number of populations, building information, and traffic volume within the affected range are converted from data such as mesh population and building position information obtained from census data and general map information.

(Details of processing of each part of equipment maintenance management device)
The facility maintenance management apparatus 200 includes a failure occurrence probability function deriving unit 21, a future failure occurrence probability calculating unit 22, a future impact calculation unit 23, a risk value prediction unit 24, a risk allowable value storage unit 25, a determination unit 26, and a renewal target selection. Unit 27 and maintenance plan formulation unit 28.

  The failure occurrence probability function deriving unit 21 derives a failure occurrence probability function that predicts the average failure rate during the usage time of the facility from the facility information and the inspection information. For example, in the failure occurrence probability function deriving unit 21, first of all, from the equipment database 11 and the inspection information database 12, the latest inspection year “deterioration state” and “construction date” to “latest inspection year” for each steel pipe column in each record. Extract the years of use up to "Month". The above data is plotted on the Weibull probability paper by the Weibull cumulative hazard method (see, for example, FIG. 5), the Weibull parameters (the shape parameter m and the scale parameter η) are obtained, and the failure occurrence probability function λ (t) is derived ( (See the following formula (1)). The Weibull cumulative hazard method will be described in detail later.

  The future failure occurrence probability calculation unit 22 calculates the failure occurrence probability of each facility in the future based on the facility information regarding each facility that is the management target and the inspection information regarding the inspection of each facility. For example, the future failure occurrence probability calculating unit 22 uses the failure occurrence probability function derived by the failure occurrence probability function deriving unit 21 as an input, and inputs the number of years of use t at the future prediction time T of the steel pipe column i. The failure occurrence probability P (i, t) of the steel pipe column i is calculated (see the following formula (2)).

  The future impact calculation unit 23 calculates the impact at the time of failure of each facility based on information about the area where each facility is located. Specifically, the future influence calculation unit 23 calculates the influence degree when the steel pipe column breaks down (assuming breakage in the present embodiment). For example, as an example of the calculation method (Fig. 6), the impact C (i, T) when a steel pipe column i breaks down in the future prediction time T years is shown as the effect of steel pipe column breakage from two aspects of safety / health and economy. Record the impact of death or injury of related parties within the scope, the impact of death or injury of non-related parties, the cost of restoration, loss of opportunity on the company side, compensation for damage to external objects, compensation for loss of opportunity for communication service users (See the following formula (3)). The parameter data used here is used from the general statistical information database shown in FIG. Future population information, etc. used in calculating future impacts can be obtained from the National Institute of Population and Social Security. For future information that is not available, appropriate assumptions must be made.

  In the parameters used for calculating the influence degree in FIG. 6, “accident time Sa used for calculation of“ car driver's death / injury Cb2 (i, T) ”of“ b. "" Is set according to the average traffic volume (congestion) in the affected area. If there is a lot of traffic, Sa is set longer, taking into account the impact on the following cars. In addition, α used to calculate “f. Compensation (mechanical loss) Cf (i, T)” is a value set based on the amount of compensation being different depending on the line type (general line, dedicated line, etc.) subject to failure. It is.

  The risk value prediction unit 24 uses the failure occurrence probability of each facility calculated by the future failure occurrence probability calculation unit 22 and the impact level of each facility calculated by the future impact calculation unit 23 to determine the risk value in the future. The risk prediction value that is is predicted for each area. For example, the risk value prediction unit 24 uses the product of the failure occurrence probability P (i, t) calculated for each steel pipe column and the degree of influence C (i, T) at the time of failure in the future prediction time T year. The risk value R (i, T) of i is predicted (see the following formula (4)). Further, by summing up the risk values of all the steel pipe columns in the target area S, the total risk value RT (s, T) of the corresponding target area S is calculated (see the following formula (5)). n is the number of steel pipe columns in the target area S.

  The risk allowable value storage unit 25 stores the risk allowable value RA (s, T) of the target area S at the predicted future time T determined in advance in the business plan or the like. For example, the risk tolerance value RA (s, T) for the future may be determined according to the current risk value level. The risk tolerance RA (s, T) should be set so that at least the current risk level is maintained at least in the future if the risk is expected to rise in the future. From the standpoint, risk tolerances are set within a range where future equipment replacement is possible within the budget for maintenance, or failure probabilities that are allowed in advance are determined in order to satisfy qualitative service requirements. Various setting methods can be considered depending on the situation.

  The determination unit 26 determines whether the risk prediction value of each area predicted by the risk value prediction unit 24 is larger than a preset risk allowable value. Specifically, the determination unit 26 determines the risk value total RT (s, T) of all the steel pipe columns in the target area S and the risk allowable value RA (s, T) of the target area S in the future prediction time T year. ) To determine the magnitude relationship.

  If the risk tolerance RA (s, T) is greater than the total risk value RT (s, T), it means that the predicted risk value is within the risk tolerance. The maintenance plan formulation unit 28 may formulate a maintenance plan according to conventional timebase maintenance.

  The renewal target selection unit 27 selects all or some of the facilities arranged in an area where the risk prediction value is determined to be greater than the risk allowable value by the determination unit 26 as a target facility for risk-based maintenance.

  For example, if the total risk value RT (s, T) is greater than the risk tolerance RA (s, T), it means that the predicted risk value exceeds the risk tolerance, so the risk is within the risk tolerance. It is desirable to reduce Therefore, for example, the renewal target selection unit 27 sorts all the steel pipe columns in the target area in descending order of the risk value, selects the equipment renewal target from the steel pipe columns having a large risk value R (i, T), The risk total value RM (s, T) of the remaining steel pipe columns is calculated for each renewal, and the renewal is continued until the calculated RM (s, T) becomes smaller than the risk allowable value RA (s, T). The residual risk value total RM (s, T) is calculated by the following equation (6). m is the number of remaining steel pipe columns. The remaining number of steel pipe columns when the total risk value of the remaining steel pipe columns starts to become smaller than the risk allowable value RA (s, T) is g (T). The total risk value RM (g, s, T) of the remaining steel pipe columns at that time is expressed by the following equation (7). Therefore, the facility renewal rate Rate (T) at the future prediction point T can be obtained by the following equation (8).

  For facilities other than the renewal target based on risk-based maintenance, the maintenance plan formulation unit 28 may formulate a maintenance plan according to conventional time-based maintenance.

  In this way, it is possible to select the scope for which risk-based maintenance should be prioritized, and by determining the rate of equipment renewal Rate (t) in the future, it is possible to predict the maintenance operations that will be required in the future and formulate a maintenance plan. To be able to use it. In addition, risk-based maintenance can be applied sequentially from time-based maintenance.

(Weibull analysis (cumulative hazard type))
The Weibull analysis (cumulative hazard type) will be described below. The cumulative hazard function H (t) is obtained by integrating the failure rate of the Weibull distribution shown in the above equation (1) in the time interval [0, t] (t> 0) as shown in the following equation (9). is there.

  Then, taking the natural logarithm of both sides of the above equation (9), the following equation (10) is obtained.

  Then, with respect to “ln (H (t))” on the left side of the above expression (10) and “ln (t)” on the right side, “ln (t)” is represented on the horizontal axis and by the cumulative hazard analysis based on random truncation. Weibull plotting is performed on a graph having “ln (H (t))” as a vertical axis. Here, x = ln (t) is set and y = ln (H (t)) is set. The slope of the straight line when the Weibull plot is linearly approximated by the method of least squares or the like is the estimated value of the shape parameter m in the above equation (1). Further, a scale parameter (or characteristic life) η is estimated from ln (t) when ln (H (t)) = 0 with respect to the estimated shape parameter m in a straight line obtained by linear approximation. . For cumulative hazard analysis based on random truncation, existing methods such as “Kenji Tanaka,“ Introductory reliability engineer learns for the first time ”, Nikka Giren Publisher, December 25, 2008, pp.102-105” are used. Can be used.

(Equipment maintenance management processing)
FIG. 7 is a flowchart showing equipment maintenance management processing. The failure probability function deriving unit 21 of the facility maintenance management apparatus 200 extracts information on the deterioration state and the age of use from the facility database 11 and the inspection information database 12, and performs a shape parameter m and a scale parameter by Weibull analysis (cumulative hazard type). η is obtained (step S1).

  The future failure occurrence probability calculation unit 22 inputs the years of use t of the facility at the future prediction time T by using a failure occurrence probability function based on the shape parameter m and the scale parameter η, so that the failure occurrence probability P (( i, t) is obtained (step S2).

  The future impact calculation unit 23 extracts information such as the number of populations and traffic volume from the general statistical information database 13, and calculates the impact C (i, T) at the time of steel pipe column failure (step S3).

  The risk value prediction unit 24 uses the failure occurrence probability P (i, t) and the influence degree C (i, T) at the future prediction time T, and the risk value R (i, T) at the future prediction time T by the product thereof. Is predicted (step S4).

  The risk allowable value storage unit 25 extracts the risk allowable value RA (s, T) of the target area S determined in advance (step S5).

  The determination unit 26 determines the magnitude relationship between the total value RT (s, T) of the risk prediction values R (i, T) of all the steel pipe columns in the target area S and the risk allowable value RA (s, T) ( Step S6).

  When the risk allowable value RA (s, T) is larger than the risk total value RT (s, T) (Yes at step S6), the maintenance plan formulation unit 28 formulates a maintenance plan based on the conventional time base maintenance (step S7). ).

  When the total risk value RT (s, T) is larger than the risk allowable value RA (s, T) (No at Step S6), the renewal target selection unit 27 sets the total risk value RT (s, T) to the risk allowable value RA ( The equipment renewal is selected until it becomes smaller than s, T), and the equipment renewal rate is calculated (step S8). In addition, the maintenance plan formulation unit 28 formulates a maintenance plan according to the conventional time base maintenance for facilities other than the renewal target.

  The output device 300 outputs each of the risk-based maintenance and the time-based maintenance, and the calculated equipment update rate (step S9).

(Effect of this embodiment)
As described above, the facility maintenance management apparatus 200 according to the embodiment can select a target range in which risk-based maintenance should be prioritized, and can easily predict a change in maintenance operation in the future that has been difficult to predict in the past. became.

(Modification by embodiment)
In the above embodiment, the risk allowable value storage unit provides the risk allowable value RA (s, T) as the total value in the target area S. However, the risk allowable value P and the influence degree C are provided with allowable values. It is also possible to select the facility renewal target and calculate the facility renewal rate.

(Application example according to the embodiment)
The risk values of all steel pipe columns from 2015 to 2030 were predicted for a certain h area by the method of the present invention. An example is shown in FIG. As shown in Fig. 8, the total risk for the entire h area is expected to increase to more than double from 2015 to 2030. Here, for example, when the risk tolerance value for the future is set to be maintained at the same level as in 2015, it is possible to select the facility renewal target and calculate the facility renewal rate (that is, maintenance operation) in each future year. Fig. 9 shows an example of selection for 2016 for equipment renewal.

  If it demonstrates using the example of FIG. 9, first, the 2016 risk prediction value of all the steel pipe columns in the h area will be rearranged in descending order. Renewal targets were selected one by one from the largest, and the total risk value of the remaining steel pipe columns began to become smaller than the risk tolerance RA (h, 2016) (here, set at the same level as the 2015 risk value). (h, 2016) and the rate of equipment renewal Rate (T). In this adaptation example, the number of equipment renewals was calculated to be nine. The rate of equipment renewal Rate (h, 2016) was calculated to be 0.9%. Moreover, since the new equipment is built in the same place when the selection object is renewed, the number of years of use in that year becomes zero.

  In this way, in response to the results of the facility renewal in 2016, it is possible to select the targets for which risk-based maintenance should be prioritized in the same way in the following years and calculate the facility renewal rate. Figure 10 shows the facility renewal rate calculated until 2030. It becomes possible to predict additional maintenance operations in the future using this prediction result.

(About equipment configuration of equipment maintenance management equipment)
Each component of the equipment maintenance management apparatus shown in FIG. 1 is functionally conceptual, and does not necessarily need to be physically configured as illustrated. That is, the specific form of distribution and integration of the functions of the facility maintenance management apparatus 200 is not limited to the illustrated one, and all or a part of the functions can be functionally or physically processed in an arbitrary unit according to various loads and usage conditions. Can be distributed or integrated.

  In addition, each process performed in the facility maintenance management apparatus 200 may be realized by a CPU (Central Processing Unit) and a program that is analyzed and executed by the CPU. Each process performed in the facility maintenance management apparatus 200 may be realized as hardware by wired logic.

  In addition, among the processes described in the embodiment, all or a part of the processes described as being automatically performed can be manually performed. Alternatively, among the processes described in the embodiments, all or a part of the processes described as being performed manually can be automatically performed by a known method. In addition, the above-described and illustrated processing procedures, control procedures, specific names, and information including various data and parameters can be changed as appropriate unless otherwise specified.

(About the program)
FIG. 11 is a diagram illustrating a computer that executes a management program. The computer 1000 includes a memory 1010 and a CPU 1020, for example. The computer 1000 also includes a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070. In the computer 1000, these units are connected by a bus 1080.

  The memory 1010 includes a ROM (Read Only Memory) 1011 and a RAM (Random Access Memory) 1012. The ROM 1011 stores a boot program such as BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to the hard disk drive 1031. The disk drive interface 1040 is connected to the disk drive 1041. For example, a removable storage medium such as a magnetic disk or an optical disk is inserted into the disk drive 1041. The serial port interface 1050 is connected to a mouse 1051 and a keyboard 1052, for example. The video adapter 1060 is connected to the display 1061, for example.

  The hard disk drive 1031 stores, for example, an OS 1091, an application program 1092, a program module 1093, and program data 1094. That is, a program defined by each process of the facility maintenance management apparatus 200 is stored in, for example, the hard disk drive 1031 as a program module 1093 in which a command executed by the computer 1000 is described. For example, a program module 1093 for executing information processing similar to the functional configuration in the facility maintenance management apparatus 200 is stored in the hard disk drive 1031.

  The setting data used in the processing in the above-described embodiment is stored as program data 1094, for example, in the memory 1010 or the hard disk drive 1031. Then, the CPU 1020 reads the program module 1093 and the program data 1094 stored in the memory 1010 and the hard disk drive 1031 to the RAM 1012 as necessary, and executes them.

  Note that the program module 1093 and the program data 1094 are not limited to being stored in the hard disk drive 1031, but may be stored in, for example, a removable storage medium and read out by the CPU 1020 via the disk drive 1041 or the like. Alternatively, the program module 1093 and the program data 1094 may be stored in another computer connected via a network (LAN (Local Area Network), WAN (Wide Area Network), etc.). The program module 1093 and the program data 1094 may be read by the CPU 1020 via the network interface 1070.

  The above embodiments and modifications thereof are included in the invention disclosed in the claims and equivalents thereof as well as included in the technology disclosed in the present application.

DESCRIPTION OF SYMBOLS 11 Equipment database 12 Inspection information database 13 General statistical information database 21 Failure occurrence probability function derivation part 22 Future failure occurrence probability calculation part 23 Future influence degree calculation part 24 Risk value prediction part 25 Risk tolerance value storage part 26 Judgment part 27 Renewal object selection Department 28 Maintenance Planning Department 200 Equipment Maintenance Management Device 300 Output Device

Claims (5)

A management method executed by a management device,
Probability calculation step of calculating the failure occurrence probability of each facility in the future based on the facility information regarding each facility that is the management target and the inspection information regarding the inspection of each facility,
Based on information on the area where each facility is located, an impact degree calculating step for calculating the impact degree at the time of failure of each equipment, and
A risk prediction value, which is a risk value in the future, is predicted for each area using the failure occurrence probability of each facility calculated by the probability calculation step and the impact level of each facility calculated by the impact calculation step. The prediction process;
A determination step of determining whether the risk prediction value of each area predicted by the prediction step is larger than a preset risk allowable value;
A selection step of selecting all facilities or some facilities arranged in an area where the risk prediction value is determined to be larger than the risk allowable value by the determination step as a target facility for risk-based maintenance,
A management method characterized by including A deriving step of deriving a failure occurrence probability function for predicting an average failure rate in the usage time of the facility from the facility information and the inspection information;
The management method according to claim 1, wherein the probability calculating step calculates the failure occurrence probability using the failure occurrence probability function derived by the derivation step.   The predicting step calculates a product of a failure occurrence probability of each facility calculated by the probability calculating step and an impact level of each facility calculated by the influence degree calculating step as the risk prediction value, The management method according to claim 1 or 2. The predicting step predicts the risk prediction value for each facility using the failure occurrence probability of each facility and the degree of influence of each facility, and uses a total value of risk prediction values of each facility for each area. Predict the risk prediction value for each area,
In the selection step, among the facilities arranged in the area where the risk prediction value is determined to be larger than the risk allowable value by the determination step, the equipment having a large risk prediction value is sequentially selected as the target equipment for risk-based maintenance. The management method according to any one of claims 1 to 3, wherein: A probability calculation unit that calculates the probability of failure of each facility in the future based on facility information on each facility that is a management target and inspection information on inspection of each facility;
Based on the information about the area where each facility is arranged, an impact calculation unit that calculates the impact at the time of failure of each facility;
A risk prediction value, which is a risk value in the future, is predicted for each area using the failure occurrence probability of each facility calculated by the probability calculation unit and the impact level of each facility calculated by the impact calculation unit. A predictor;
A determination unit for determining whether the risk prediction value of each area predicted by the prediction unit is greater than a preset risk allowable value;
A selection unit that selects all or some of the facilities arranged in an area where the risk prediction value is determined to be greater than the risk allowable value by the determination unit as a target device for risk-based maintenance. Management device characterized by.

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