System that supports maintenance-improvement proposal and method for supporting maintenance-improvement proposal

Abstract

Provided are a convenient system that supports maintenance-improvement proposal and a method therefore. A system (P1) that supports maintenance-improvement proposal comprises a processor (3) that, on the basis of: asset knowledge information relating to a failure that may occur in an asset; current maintenance method information indicating a current maintenance method of the asset; maintenance improvement method information indicating a maintenance improvement method for improving a current maintenance method of the asset; and external condition information including, as an external condition, one or both of a support end deadline of a technician who maintains the asset and a supply deadline of a component, creates a time-series maintenance plan pattern so that the external condition is met.

Description

Maintenance improvement proposal support system and maintenance improvement proposal support method

[0001] The present disclosure relates to a maintenance improvement proposal support system and the like.

[0002] As a technology for improving facility maintenance operations, for example, the technology described in Patent Document 1 is known. That is, Patent Document 1 describes that "maintenance improvement measures that are in line with a maintenance improvement policy are extracted that are suited to the characteristics of an asset, and the order in which they are implemented is determined based on the dependencies between the technologies and data requirements between the maintenance improvement measures."

[0003] Japanese Patent Application Laid-Open No. 2020-181230

[0004] The technology described in Patent Document 1 extracts maintenance improvement measures that are suited to the characteristics of assets from among those that are in line with the user's maintenance improvement policy, but there is room for improvement in terms of convenience.

[0005] Therefore, an object of the present disclosure is to provide a highly convenient maintenance improvement proposal support system, etc.

[0006] In order to solve the above-mentioned problems, the maintenance improvement proposal support system according to the present disclosure includes a processing unit that creates a time-series maintenance plan pattern based on asset knowledge information related to failures that may occur in an asset, current maintenance method information indicating the current maintenance method for the asset, maintenance improvement method information indicating a maintenance improvement method for improving the current maintenance method for the asset, and external condition information that includes, as external conditions, one or both of the support end date and parts supply deadline for the technician who maintains the asset, so that the external conditions are satisfied.

[0007] According to the present disclosure, a highly convenient maintenance improvement proposal support system and the like can be provided.

[0008] 1 is a configuration diagram of a maintenance improvement proposal support system according to an embodiment. FIG. 2 is a diagram illustrating a hardware configuration of a maintenance improvement proposal support device provided in the maintenance improvement proposal support system according to an embodiment. FIG. 3 is an explanatory diagram of data stored in a storage unit of the maintenance improvement proposal support system according to an embodiment. FIG. 4 is an explanatory diagram illustrating an example of asset knowledge information in the maintenance improvement proposal support system according to an embodiment. FIG. 5 is an explanatory diagram illustrating an example of current maintenance method information in the maintenance improvement proposal support system according to an embodiment. FIG. 6 is an explanatory diagram illustrating an example of maintenance improvement method information in the maintenance improvement proposal support system according to an embodiment. FIG. 7 is an explanatory diagram illustrating an example of support deadline information in the maintenance improvement proposal support system according to an embodiment. FIG. 8 is an explanatory diagram illustrating an example of parts supply deadline information in the maintenance improvement proposal support system according to an embodiment. FIG. 9 is an explanatory diagram illustrating an example of KPI information in the maintenance improvement proposal support system according to an embodiment. FIG. 10 is an explanatory diagram illustrating an example of budget information in the maintenance improvement proposal support system according to an embodiment. FIG. 11 is an explanatory diagram illustrating an example of replacement priority information in the maintenance improvement proposal support system according to an embodiment. FIG. 12 is an explanatory diagram illustrating an example of replaceable period information in the maintenance improvement proposal support system according to an embodiment. FIG. 13 is an explanatory diagram illustrating an example of deadline response policy information in the maintenance improvement proposal support system according to an embodiment. FIG. 14 is an explanatory diagram illustrating an example of maintenance plan pattern information in the maintenance improvement proposal support system according to an embodiment. FIG. 15 is an explanatory diagram illustrating a transition of the failure probability of a predetermined asset in the maintenance improvement proposal support system according to an embodiment. 1 is a flowchart showing the processing of a processing unit of the maintenance improvement proposal support system according to the embodiment. FIG. 2 is a flowchart showing the processing of a processing unit of the maintenance improvement proposal support system according to the embodiment. FIG. 3 is a display example showing the trends in the cost required for asset maintenance each year and the failure probability in the maintenance improvement proposal support system according to the embodiment. FIG. 4 is a display example comparing the total costs when the current status maintenance method is continued and when maintenance based on a maintenance plan pattern is performed in the maintenance improvement proposal support system according to the embodiment. FIG. 5 is a display example showing the trends in parts procurement costs in preparation for the arrival of a parts supply deadline in the maintenance improvement proposal support system according to the embodiment. FIG. 6 is a display example comparing the trends in the number of replacement units when the current status maintenance method is continued and when maintenance based on a maintenance plan pattern is performed in the maintenance improvement proposal support system according to the embodiment.

[0009] <<Embodiment>> <Configuration of Maintenance Improvement Proposal Support System>> FIG. 1 is a configuration diagram of a maintenance improvement proposal support system P1 according to an embodiment. The maintenance improvement proposal support system P1 shown in FIG. 1 is a system for proposing improvements to asset maintenance work to a user, and is configured to include a maintenance improvement proposal support device 10. Note that the term "asset" refers to a facility. Examples of such "assets" include infrastructure such as power transmission facilities, substation facilities, communication facilities, gas facilities, water facilities, railways, and roads, as well as plants such as power plants, manufacturing plants, water treatment plants, and chemical plants. Other examples of "assets" include industrial machinery, medical facilities, air conditioning facilities, and refrigeration facilities. Furthermore, "maintenance" refers to work that includes not only inspections such as asset inspections, but also treatments such as part replacement and repair.

[0010] 1 is a device that executes a predetermined process based on an input operation by a maintenance improvement proposer M1 and displays the results of the process on a display unit 4. Such a maintenance improvement proposal support device 10 may be configured as a single computer, or may be configured as a plurality of computers connected via a network. For example, the functions of the maintenance improvement proposal support device 10 may be distributed across a plurality of computers such as a cloud server or an edge server.

[0011] 1 is a person who proposes improvements to the maintenance work of a user who operates an asset. Such a maintenance improvement proposer M1 may be, for example, a maintenance business operator or a person belonging to the manufacturer of the asset.

[0012] As shown in Fig. 1, the maintenance improvement proposal support device 10 includes a storage unit 1, an input unit 2, a processing unit 3, and a display unit 4. Predetermined data is stored in the storage unit 1. The data stored in the storage unit 1 will be described later. The input unit 2 is used when a maintenance improvement proposer M1 performs a predetermined input operation, and is connected to the processing unit 3. For example, a keyboard or a mouse is used as the input unit 2.

[0013] The processing unit 3 executes predetermined processing based on the operation of the input unit 2. The processing executed by the processing unit 3 will be described later. The display unit 4 displays the results of the processing by the processing unit 3 in a predetermined manner. For example, a liquid crystal display is used as such a display unit 4. It is also possible to use a touch panel type mobile terminal that combines the functions of the input unit 2 and the display unit 4, such as a smartphone or tablet.

[0014] As shown in FIG. 1 , the processing unit 3 includes an asset knowledge management unit 31, a current maintenance method management unit 32, a maintenance improvement method management unit 33, an external condition management unit 34, a maintenance policy management unit 35, a time-series maintenance plan pattern creation unit 36, a reliability prediction unit 37, and a pattern evaluation unit 38.

[0015] The asset knowledge management unit 31 manages asset knowledge information 11 (see FIG. 4 ), which includes failure modes that can occur in each component of an asset, their occurrence frequency, and the impact and criticality of a failure mode when it occurs. Note that the "component" mentioned above refers to a device or part that is a constituent element of an asset (facility). Furthermore, a "failure mode" refers to the type of failure in a component.

[0016] The current maintenance method management unit 32 manages data related to the current maintenance method of an asset. The "current maintenance method" is the maintenance method of an asset currently adopted by the user. The maintenance improvement method management unit 33 manages data related to the maintenance improvement method of an asset. The "maintenance improvement method" is the maintenance method when the current maintenance method is improved to a specified one.

[0017] The external condition management unit 34 manages external conditions when asset maintenance work is performed. Examples of such external conditions include the end of support for the technician who performs asset maintenance work and the supply deadline for parts used in the maintenance work.

[0018] The maintenance policy management unit 35 manages the maintenance policy of the user. Examples of such a maintenance policy include conditions related to the user's budget and KPIs (Key Performance Indicators) that the user values, as well as conditions related to the reliability of assets, priorities for replacing multiple assets, and conditions for the timing of replacement.

[0019] The time-series maintenance plan pattern creation unit 36 ​​creates a time-series maintenance plan pattern related to asset maintenance work. That is, the time-series maintenance plan pattern creation unit 36 ​​creates a predetermined maintenance plan pattern based on the data managed by the asset knowledge management unit 31, current maintenance method management unit 32, maintenance improvement method management unit 33, and external condition management unit 34.

[0020] The reliability prediction unit 37 predicts reliability indicators such as failure probability when asset maintenance work is performed based on the maintenance plan pattern. The pattern evaluation unit 38 evaluates whether the maintenance plan pattern satisfies the conditions of the user's maintenance policy. The pattern evaluation unit 38 then displays on the display unit 4, among multiple maintenance plan pattern candidates, the one that best suits the user's maintenance policy.

[0021] Fig. 2 is a diagram showing the hardware configuration of the maintenance improvement proposal support device 10. As shown in Fig. 2, the hardware configuration of the maintenance improvement proposal support device 10 includes a processor 10a, a RAM 10b (Random Access Memory), a ROM 10c (Read Only Memory), a HDD 10d (Hard Disk Drive), a communication interface 10e, and an input / output interface 10f, which are connected in a predetermined manner via a bus 10g.

[0022] The processor 10a is hardware that constitutes the processing unit 3 (see FIG. 1) of the maintenance improvement proposal support device 10. The RAM 10b, ROM 10c, and HDD 10d are hardware that constitute the storage unit 1 (see FIG. 1) of the maintenance improvement proposal support device 10. The processor 10a reads out a predetermined program stored in the ROM 10c or HDD 10d, loads it into the RAM 10b, and executes a predetermined process.

[0023] The communication interface 10e performs predetermined communication with the user terminal 20 via the network N1. The input / output interface 10f is an interface for inputting data from the input unit 2 and outputting data to the display unit 4. Note that the hardware configuration shown in Fig. 2 is an example and is not limited to this.

[0024] 3 is an explanatory diagram of data stored in the storage unit 1 of the maintenance improvement proposal support system. As shown in Fig. 3, the storage unit 1 stores asset knowledge information 11, current maintenance method information 12, maintenance improvement method information 13, external condition information 14, maintenance policy information 15, and maintenance plan pattern information 16. Next, each of these pieces of information will be described in order.

[0025] Fig. 4 is an explanatory diagram showing an example of asset knowledge information 11. The asset knowledge information 11 shown in Fig. 4 is information about failures that may occur in assets, and is managed by the asset knowledge management unit 31 (see Fig. 1). Such asset knowledge information 11 is set in advance by a maintenance improvement proposer M1 (see Fig. 1) through an operation via the input unit 2 (see Fig. 1). For example, the asset knowledge information 11 is set based on a Failure Modes, Effects and Criticality Analysis (FMECA).

[0026] In the asset knowledge information 11 shown in FIG. 4, "component," "ID," "failure mode," "occurrence frequency," "impact at occurrence," and "criticality" are associated with each other. Components A to C are constituent elements of a given asset. "ID" shown in FIG. 4 is identification information assigned to the combination of a component and a failure mode. Incidentally, examples of "failure modes" include cracks and breaks in component A, bearing wear, grease deterioration, and bearing sticking in component B, and O-ring deterioration in component C.

[0027] The "occurrence frequency" shown in FIG. 4 is the frequency at which a failure mode occurs, and is set based on the results of past inspections and maintenance. The occurrence frequency of a failure mode is classified into, for example, four levels. Specifically, the highest occurrence frequency of a failure mode is "Frequent," followed by "Occasional," "Infrequent," and "Improbable," in that order. Note that the cases of "Frequent" and "Improbable" are not shown in the table of FIG. 4.

[0028] The "impact at the time of occurrence" shown in Fig. 4 indicates the degree of impact when a predetermined failure mode occurs. The impact at the time of occurrence of a failure mode is classified into, for example, three levels. Specifically, the case where the impact at the time of occurrence of a failure mode is greatest is set to be "Catastrophic," followed by "Degraded" and then "Incipient," in that order.

[0029] The "criticality" shown in FIG. 4 indicates the criticality when a predetermined failure mode occurs in a component. The criticality associated with the occurrence of a failure mode is expressed, for example, as the product of a numerical value of the occurrence frequency and a numerical value of the impact at the time of occurrence. Specifically, in the four-level occurrence frequency, "Frequent" is numerically quantified as 4, "Occasional" as 3, "Infrequent" as 2, and "Improbable" as 1. In addition, in the three-level impact at the time of occurrence, "Catastrophic" is numerically quantified as 3, "Degraded" as 2, and "Incipient" as 1. For example, in the failure mode (ID=1) in which a crack occurs in component A, the occurrence frequency is "Occasional" and the impact at the time of occurrence is "Degraded," so the criticality value is 3×2=6.

[0030] The number of classifications (number of stages) for the frequency of occurrence of a failure mode and the impact when it occurs may be changed as appropriate based on the configuration and characteristics of the asset. The frequency of occurrence does not necessarily need to be classified into stages, and a predetermined value based on past cases may be used. Furthermore, the cost of maintenance or part replacement when a failure mode occurs may be used as appropriate for the impact when it occurs.

[0031] FIG. 5 is an explanatory diagram showing an example of the current maintenance method information 12. The current maintenance method information 12 shown in FIG. 5 is information indicating the current maintenance method of the asset operated by the user, and is managed by the current maintenance method management unit 32 (see FIG. 1). The current maintenance method information 12 is set in advance by the maintenance improvement proposer M1 (see FIG. 1) through the input unit 2 (see FIG. 1). For example, the current maintenance method information 12 may be set based on a prior interview with the user who owns the asset. Furthermore, the current maintenance method information 12 may be set appropriately based on the assumptions of the maintenance improvement proposer M1 (see FIG. 1).

[0032] In the current maintenance method information 12 shown in Fig. 5, "component", "ID", "failure mode", "maintenance strategy", "maintenance frequency", "inspection means", "inspection cost", "treatment means", and "treatment cost" are associated with each other. Note that the "component" and "ID" are the same as those in the asset knowledge information 11 of Fig. 4, and therefore their explanation will be omitted.

[0033] 5 is a maintenance strategy for a specific failure mode of each component. Specifically, the maintenance strategy currently being implemented by the user is registered from among three types: time-based maintenance (TBM), condition-based maintenance (CBM), and breakdown maintenance (BDM).

[0034] The aforementioned time-based maintenance (TBM) is a method of performing maintenance when a predetermined time has passed since the end of the previous maintenance of an asset (or when a predetermined operating time has passed). Condition-based maintenance (CBM) is a method of performing appropriate maintenance or other measures based on the state or operating status of an asset or component. Corrective maintenance (BDM) is a method of performing maintenance or other measures after a failure occurs in an asset.

[0035] The "Maintenance Frequency" shown in FIG. 5 is where the maintenance frequency for each component failure mode is registered. In the example of FIG. 5, time-based maintenance (TBM) and condition-based maintenance (CBM) are shown in number of days. For example, for the failure mode (ID=1) of component A, which is a crack, the maintenance strategy is to inspect the asset every 90 days as condition-based maintenance (CBM), and perform maintenance if a failure occurs. Note that for corrective maintenance (BDM), no specific maintenance is performed until a failure occurs, so the "Maintenance Frequency" column simply states "BDM."

[0036] The "inspection means" shown in FIG. 5 is registered with the means (inspection method) used to inspect whether or not there is a failure mode in each component. Note that "inspection" refers to maintenance work to discover the failure mode. The "inspection cost" shown in FIG. 5 is registered with the cost (expense) of inspecting using a specified inspection means. In the example of FIG. 5, the inspection cost is registered in units of M yen (1 million yen).

[0037] The "treatment means" shown in FIG. 5 is where the treatment means (treatment method) for taking a predetermined treatment for the failure mode of each component is registered. The "treatment" is a maintenance work for eliminating or improving the predetermined failure mode. The "treatment cost" shown in FIG. 5 is where the cost (expense) for taking a treatment using the predetermined treatment means is registered. In the example of FIG. 5, the treatment cost is registered in M ​​yen units (units of 1 million yen).

[0038] For example, for a crack in component A (ID=1), a visual inspection is performed every 90 days using condition-based maintenance (CBM). The inspection cost associated with the visual inspection is 0.005M yen (5,000 yen). If a crack is discovered in component A as a result of the visual inspection, component A is replaced as a remedial measure. The remedial cost associated with replacing component A is 50M yen (50 million yen).

[0039] For example, for grease deterioration in component B (ID=4), an overhaul inspection is performed every 2,190 days (every six years) using time-based maintenance (TBM). The inspection cost associated with the overhaul inspection is 0.5 million yen (500,000 yen). Grease injection is performed as a treatment during the overhaul inspection, and the treatment cost is 0.1 million yen (100,000 yen).

[0040] Fig. 6 is an explanatory diagram showing an example of the maintenance improvement method information 13. The maintenance improvement method information 13 shown in Fig. 6 is information indicating a maintenance improvement method when improving the current maintenance method of an asset, and is managed by the maintenance improvement method management unit 33 (see Fig. 1). Furthermore, the maintenance improvement method information 13 is set in advance by a maintenance improvement proposer M1 (see Fig. 1) through an operation via the input unit 2 (see Fig. 1). In other words, the maintenance improvement proposer M1 (or AI: Artificial Intelligence) sets desirable measures for improving the maintenance work of an asset as the maintenance improvement method information 13.

[0041] 6, the maintenance improvement method information 13 associates "component," "ID," "failure mode," "maintenance strategy," "maintenance frequency," "inspection means," "inspection cost," "treatment means," "treatment cost," and "introduction cost." Note that, since no particular maintenance improvement method is indicated for the crack (ID=1: see FIG. 4) and break (ID=2: see FIG. 4) of component A, the current maintenance method (see FIG. 5) will be continued.

[0042] "Online" in "Maintenance Frequency" in FIG. 6 means that online remote diagnosis is performed. "Introduction Cost" in FIG. 6 is the cost (expense) of newly introducing a specified inspection method. For example, the cost of purchasing a sensor used for online remote diagnosis, as well as a sensor data recording device and a diagnostic device, are registered as "Introduction Cost." Note that the items other than "Introduction Cost" are the same as those in the current maintenance method information 12 (see FIG. 5) described above, and therefore their explanation will be omitted.

[0043] For example, for bearing wear (ID=3) of component B, online remote diagnosis is performed using a vibration meter based on condition-based maintenance (CBM). The cost of implementing online remote diagnosis is 1 million yen (1 million yen). Also, for O-ring deterioration (ID=6) of component C, online remote oil leak diagnosis is performed using image analysis based on condition-based maintenance (CBM). The cost of implementing online remote diagnosis is 5 million yen (5 million yen).

[0044] When creating a maintenance plan pattern for an asset, it is possible to continuously apply the maintenance improvement method information 13 from the first year of the planning year. However, in that case, there is a possibility that a large amount of cost will be required, or that the timing of asset replacement will not be in line with the user's maintenance policy. Therefore, in this embodiment, when creating a maintenance plan pattern, the processing unit 3 (see FIG. 1) selectively incorporates the current maintenance method information 12 (see FIG. 5) or the maintenance improvement method information 13 depending on the asset component, failure mode, and planning year. The processing when creating a maintenance plan pattern will be described later.

[0045] FIG. 7 is an explanatory diagram showing an example of support expiration information 14a. The support expiration information 14a shown in FIG. 7 is information indicating the end of support period for a technician who maintains an asset. The technician is dispatched by the asset manufacturer or the like in response to a request from a user who owns the asset. The support expiration information 14a is included in the external condition information 14 (see FIG. 3) and is managed by the external condition management unit 34 (see FIG. 1). The support expiration information 14a is set in advance by a maintenance improvement proposer M1 (see FIG. 1) via the input unit 2 (see FIG. 1).

[0046] 7, the support expiration information 14a associates assets with expiration dates for technician support. In the example of Fig. 7, the expiration date for technician support for asset α is 2035. The expiration date for technician support for asset β is 2040.

[0047] Fig. 8 is an explanatory diagram showing an example of parts supply deadline information 14b. The parts supply deadline information 14b shown in Fig. 8 is information indicating the supply deadline of parts used in asset maintenance. The parts supply deadline information 14b is included in the external condition information 14 (see Fig. 3) and is managed by the external condition management unit 34 (see Fig. 1). The parts supply deadline information 14b is set in advance by the maintenance improvement proposer M1 (see Fig. 1) through the input unit 2 (see Fig. 1).

[0048] 8, the parts supply deadline information 14b associates "component," "ID," "failure mode," and "parts supply deadline." For example, the parts supply deadline for a crack in component A (ID=1) is set to 2030. Also, the parts supply deadline for bearing wear in component B (ID=3) is set to 2035. No particular parts supply deadline has been set for grease deterioration in component B (ID=4).

[0049] In addition, external condition information 14 (see Figure 3), which includes the support end deadline of the technician who maintains the asset and the parts supply deadline as external conditions, is composed of support deadline information 14a (see Figure 7) and parts supply deadline information 14b (see Figure 8).

[0050] FIG. 9 is an explanatory diagram showing an example of KPI information 15a. The KPI information 15a shown in FIG. 9 is information indicating KPI items that a user wishes to improve. The KPI information 15a is included in the maintenance policy information 15 (see FIG. 3) and managed by the maintenance policy management unit 35 (see FIG. 1). The KPI information 15a is set in advance by a maintenance improvement proposer M1 (see FIG. 1) operating via the input unit 2 (see FIG. 1) based on a prior interview with the user.

[0051] As shown in FIG. 9 , the KPI information 15 a associates a predetermined “ID,” a “KPI,” and an “Improvement Request” item. The “ID” shown in FIG. 9 is identification information assigned to each KPI item. In the example of FIG. 9 , the “KPI” items include availability rate, total cost, cost leveling, replacement number leveling, and reliability. In the “Improvement Request” item, a user operating an asset checks the KPI for which the user wishes to improve. In the example of FIG. 9 , the total cost (ID=2) and reliability (ID=5) are checked, indicating that the user wishes to improve the total cost and reliability related to asset maintenance. In this way, the maintenance policy information 15 (see FIG. 3 ), which indicates the user's maintenance policy when maintaining assets, includes KPI items designated by the user.

[0052] FIG. 10 is an explanatory diagram showing an example of budget information 15b. The budget information 15b shown in FIG. 10 is information showing the user's investment budget for maintenance work on a specific asset by year. The budget information 15b is included in the maintenance policy information 15 (see FIG. 3) and managed by the maintenance policy management unit 35 (see FIG. 1). The budget information 15b is set in advance by a maintenance improvement proposer M1 (see FIG. 1) through an operation via the input unit 2 (see FIG. 1) based on a prior interview with the user.

[0053] 10, the budget information 15b associates "years" such as the first and second years in the future with "investment budgets" for asset maintenance. In the example of Fig. 10, the investment budget for the maintenance work of a specific asset in the first year is 40M yen (40 million yen), but in the sixth year it is 5M yen (5 million yen).

[0054] FIG. 11 is an explanatory diagram showing an example of replacement priority information 15c. The replacement priority information 15c shown in FIG. 11 is information indicating the priority when replacing an asset (replacing it with a new one). The replacement priority information 15c is included in the maintenance policy information 15 (see FIG. 3) and managed by the maintenance policy management unit 35 (see FIG. 1). The replacement priority information 15c is set in advance by a maintenance improvement proposer M1 (see FIG. 1) through an operation via the input unit 2 (see FIG. 1) based on a prior interview with a user.

[0055] Each row in FIG. 11 indicates an asset that is the target when comparing replacement priorities. Each column in FIG. 11 indicates an asset that is the counterpart when comparing replacement priorities. If a specific asset in the row direction has a higher replacement priority than the asset it is compared to in the column direction, a check mark is placed in the box identified by that row and column. More specifically, asset α has a higher replacement priority than assets β and γ. Asset γ has a higher replacement priority than asset β. In other words, the replacement priorities are asset α > asset γ > asset β.

[0056] Fig. 12 is an explanatory diagram showing an example of replacement allowable period information 15d. The replacement allowable period information 15d shown in Fig. 12 is information indicating a period during which each asset can be replaced (exchanged for a new asset). The replacement allowable period information 15d is included in the maintenance policy information 15 (see Fig. 3) and is managed by the maintenance policy management unit 35 (see Fig. 1). The replacement allowable period information 15d is set in advance by a maintenance improvement proposer M1 (see Fig. 1) through an operation via the input unit 2 (see Fig. 1).

[0057] As shown in FIG. 12 , in the replacement possible period information 15d, an "asset," a "start year," and an "end year" are associated with each other. The "start year" is the start year of the period in which a specified asset can be replaced. The "end year" is the end year of the period in which a specified asset can be replaced. For example, for asset α, the start year of the replacement possible period is 2028, and the end year is 2030. This indicates that asset α should be replaced between 2028 and 2030. For asset β, the start year of the replacement possible period is not specifically set, and the end year is 2035. This indicates that asset β should be replaced by 2035.

[0058] In this way, the maintenance policy information 15 (see Figure 3) indicating the user's maintenance policy when maintaining assets includes the replacement timing (see Figure 12) when replacing an asset with a new one, or the priority of replacement relative to other assets (see Figure 11).

[0059] Incidentally, in the replacement priority information 15c (see FIG. 11), the priority of replacement is asset α > asset γ > asset β. Considering such priority of replacement, the replaceable period information 15d in FIG. 12 indicates that asset β needs to be replaced by 2035. Therefore, asset γ, which has a higher priority for replacement than asset β, also needs to be replaced by 2035.

[0060] FIG. 13 is an explanatory diagram showing an example of deadline response policy information 15e. The deadline response policy information 15e shown in FIG. 13 is information indicating a user's response policy regarding the support end deadline (see FIG. 7) and the parts supply deadline (see FIG. 8) of the technician who maintains the asset. The deadline response policy information 15e is included in the maintenance policy information 15 (see FIG. 3) and managed by the maintenance policy management unit 35 (see FIG. 1). Furthermore, the deadline response policy information 15e is set in advance by a maintenance improvement proposer M1 (see FIG. 1) through an operation via the input unit 2 (see FIG. 1) based on a prior interview with the user.

[0061] As shown in FIG. 13 , in the deadline response policy information 15 e, "asset," "technician support," and "parts supply" are associated with each other. The "technician support" column registers the user's response policy in preparation for the end of technician support. The "parts supply" column registers the user's response policy in preparation for the end of supply of parts used in maintenance work. The example of FIG. 13 shows a policy that the user will provide in-house training on maintenance work for asset α in preparation for the end of technician support for asset α. Furthermore, a policy that the user will procure a predetermined amount of parts for asset α by the parts supply deadline in preparation for the end of parts supply for asset α is also shown.

[0062] Furthermore, for asset β, a policy is indicated in which the user will conduct in-house training in preparation for the end of technician support. Also, a policy is indicated in which the user will manufacture parts used in maintenance work for asset β within the user's company (in-house production) in preparation for the end of parts supply. For asset γ, no policy is indicated regarding how to respond to the deadlines for technician support and parts supply. This means that no particular maintenance work will be carried out for asset γ after the expiration of each deadline.

[0063] Fig. 14 is an explanatory diagram showing an example of maintenance plan pattern information 16. The maintenance plan pattern information 16 shown in Fig. 14 is information indicating a time-series maintenance plan pattern related to maintenance work for a specified asset, and is created by the time-series maintenance plan pattern creation unit 36 ​​(see Fig. 1). As shown in Fig. 14, the maintenance plan pattern based on the maintenance plan pattern information 16 is created as a time-series maintenance plan that includes whether or not to replace the asset, whether or not to inspect and take measures for a failure mode of the asset, whether or not to switch the maintenance method, and the quantity of parts to be procured for the asset.

[0064] An "asset" shown in FIG. 14 is a piece of equipment that is the target of maintenance work. A "failure mode" is a type of failure in an asset. Although a failure mode is set for each asset in FIG. 14, this is not limiting. For example, a failure mode may be set for each component that constitutes an asset.

[0065] The "maintenance items" shown in FIG. 14 indicate the maintenance work items for each asset. Examples of such maintenance items include asset replacement, inspection, treatment (maintenance, etc.), parts procurement, and maintenance changeover. A "maintenance changeover" refers to a change in the maintenance work method. Examples of such a "maintenance changeover" include a change from on-site asset inspection to online remote diagnosis, a change in the inspection cycle, and a change in maintenance strategy (e.g., from time-based maintenance (TBM) to condition-based maintenance (CDM)).

[0066] In the "planned year" shown in FIG. 14, for example, the time when the maintenance plan pattern information 16 was generated (present) is used as the base year, and the following years are set as the first year, second year, ..., twentieth year, etc. In the column for each planned year, a predetermined number is set in association with the content of the maintenance work (maintenance item). In the example of FIG. 14, asset α is replaced in the 18th year, so the value "1" is set in the corresponding column. Furthermore, asset α is not replaced in the 1st to 17th years or the 19th and 20th years, so the value "0" is set in the corresponding columns.

[0067] Furthermore, for failure mode FM1 of asset α, a maintenance switchover (for example, online remote diagnosis due to the introduction of IoT) is performed in the first year, so a value of "1" is set in the corresponding field. Note that in each year after the planned year in which the maintenance switchover was performed (first year in the example of FIG. 14), predetermined maintenance will be performed based on the maintenance improvement method information 13 (see FIG. 6). Incidentally, from the second year onwards, the value of "maintenance switchover" is "0", but because the maintenance switchover was already performed in the first year, maintenance will also be performed in each year after the second year based on the maintenance improvement method information 13 (see FIG. 6).

[0068] For "parts procurement," the amount of parts required for n maintenance operations (n ​​is a natural number) is set as the value of "n." For example, for failure mode FM1 of asset α, a predetermined inspection and treatment will be performed in the sixth year, so in preparation for this, the maintenance plan pattern is set so that parts for one maintenance operation will be procured in the fourth year, and parts for two maintenance operations will be procured in the fifth year.

[0069] The maintenance schedule pattern may be displayed in a table format as shown in Fig. 14 on the display unit 4 (see Fig. 1). The maintenance schedule pattern may also be displayed in another format such as a graph. A specific method for creating a maintenance schedule pattern will be described later.

[0070] FIG. 15 is an explanatory diagram showing an example of the transition of the failure probability of a specified asset. The horizontal axis of FIG. 15 represents the planned fiscal year (year) based on the time when the maintenance plan pattern information 16 was created. The vertical axis of FIG. 15 represents the failure probability of the specified asset. The failure probability for each planned fiscal year is calculated by the reliability prediction unit 37 (see FIG. 1). The solid line graph in FIG. 15 shows the transition of the failure probability when part replacement is performed. The dashed line graph shows the transition of the failure probability when part replacement is not performed.

[0071] Assets generally deteriorate over time, and the failure probability also increases with age. However, when parts are replaced or assets are replaced (with new ones), the replacement parts or assets become new, and the failure risk of the entire asset decreases. In the example of Figure 15, the failure probability decreases in the seventh year after the part replacement.

[0072] For example, the reliability prediction unit 37 (see FIG. 1) may calculate a predetermined health score based on the number of years since the asset was introduced, and then calculate the failure probability of the asset based on this health score. When an asset is replaced, the number of years since the asset was introduced is also reset to zero. When some parts of an asset are replaced, the failure probability of the entire asset is calculated appropriately based on the failure probability of each part. The failure probability value is used as a reliability index when predicting the reliability of the asset.

[0073] 16A and 16B are flowcharts showing the processing of the processing unit of the maintenance improvement proposal support system (see also FIG. 1 as appropriate). In the series of processes in FIG. 16A, it is assumed that a maintenance plan pattern is generated for one asset as the target of maintenance work. It is also assumed that the asset knowledge information 11 and current maintenance method information 12 shown in FIG. 3 as well as maintenance improvement method information 13, external condition information 14, and maintenance policy information 15 have already been set and stored in the storage unit 1.

[0074] In step S101, the processing unit 3 creates a maintenance plan pattern using the time-series maintenance plan pattern creation unit 36. The maintenance plan pattern information 16 created initially may be a maintenance plan in which no particular maintenance work is performed each year, or may be a maintenance plan in which the current maintenance method is continued as is. In addition, for example, a predetermined maintenance plan pattern assumed by the maintenance improvement proposer M1 (or AI) may be set initially.

[0075] The components and failure modes included as items in the maintenance plan pattern are set based on the asset knowledge information 11 (see FIG. 4). Regarding asset replacement, an initial maintenance plan pattern may be created so that an asset is replaced with a new one when the expected lifespan is reached. As will be described later, the provisional maintenance plan pattern created in step S101 is updated successively.

[0076] In step S102 of Fig. 16A, the processing unit 3 sets the value of the planned year t in the maintenance plan pattern to 1 (t = 1). For example, if a maintenance plan pattern such as that shown in Fig. 14 is created in step S101, in step S102 the processing unit 3 selects the first year from among the planned years 1 to 20.

[0077] In step S103, the processing unit 3 selects a component. That is, for a specific asset that is the target of maintenance work, the processing unit 3 selects one of multiple components that are constituent elements of the asset. In step S104, the processing unit 3 selects a failure mode. That is, the processing unit 3 selects one of multiple failure modes that can occur in the component selected in step S103.

[0078] In step S105, the processing unit 3 determines whether or not the maintenance has been switched over. That is, the processing unit 3 determines whether or not the maintenance work method for the failure mode (selected in S104) of a predetermined component (selected in S103) has been switched over to the first year of the planning year (t=1 in S102) in the maintenance plan pattern created in step S101.

[0079] For example, assume that a maintenance plan pattern is created in step S101 to continue the current maintenance method. In this case, no maintenance switchover has been performed in the first year of the planning year (t=1). If no maintenance switchover has been performed in step S105 (S105: No), the processing unit 3 proceeds to step S106.

[0080] In step S106, the processing unit 3 performs current maintenance judgment. Here, "current maintenance judgment" means that for the planning year, component, and failure mode selected in steps S102 to S104, the appropriateness of the maintenance plan pattern should be judged based on the current maintenance method information 12 (see FIG. 5).

[0081] 14, it is possible that a maintenance plan pattern is created so that a predetermined maintenance changeover (for example, online remote diagnosis) is performed in the first year (t=1) of the plan fiscal year. In this case, the maintenance changeover will have already been performed from the first year onwards, and the determination result in step S105 will be "maintenance changeover completed."

[0082] If the maintenance switch has been completed in step S105 (S105: Yes), the processing of the processing unit 3 proceeds to step S107. In step S107, the processing unit 3 performs an improvement maintenance judgment. Here, "improvement maintenance judgment" means that the appropriateness of the maintenance plan pattern should be judged based on the maintenance improvement method information 13 (see FIG. 6) for the planning year, component, and failure mode selected in steps S102 to S104.

[0083] After performing the processing of step S106 or S107, the processing of the processing unit 3 proceeds to step S108. In step S108, the processing unit 3 determines whether the maintenance strategy of the maintenance method that was the subject of the determination in step S106 or S107 is time-based maintenance (TBM). As described above, time-based maintenance (TBM) is a maintenance strategy in which inspections and treatments are performed at a predetermined maintenance frequency (maintenance cycle). If the maintenance strategy is time-based maintenance (TBM) in step S108 (S108: Yes), the processing of the processing unit 3 proceeds to step S109.

[0084] In step S109, the processing unit 3 performs inspection and treatment in accordance with a predetermined maintenance frequency. For example, assume that a failure mode of grease deterioration of component B (see ID=5 in FIG. 5) is selected in steps S103 and S104, and a maintenance planning pattern is created to continue the current maintenance method in the first year of the planning fiscal year. In this case, as shown in the current maintenance method information 12 in FIG. 5, an overhaul inspection of component B is performed every 2190 days. Therefore, in step S109, the maintenance planning pattern is appropriately set so that the cycle of overhaul inspection for grease deterioration of component B is 2190 days.

[0085] If the maintenance frequency or the like in the maintenance schedule pattern is inappropriate, the process may return to step S101 and a new maintenance schedule pattern may be created. The same applies to the processes in steps S112, S113, and S114 described below.

[0086] Furthermore, if the maintenance strategy is not time-based maintenance (TBM) in step S108 (S108: No), the processing of the processing unit 3 proceeds to step S110. In step S110, the processing unit 3 determines whether the maintenance strategy of the maintenance method that was the subject of determination in step S106 or S107 is condition-based maintenance (CBM). As described above, condition-based maintenance (CBM) is a maintenance strategy in which maintenance is performed appropriately based on the state and operating status of assets and components. If the maintenance strategy is condition-based maintenance (CBM) in step S110 (S110: Yes), the processing of the processing unit 3 proceeds to step S111.

[0087] In step S111, the processing unit 3 determines whether the maintenance frequency in condition-based maintenance (CBM) is "Online." That is, the processing unit 3 determines whether the maintenance strategy is condition-based maintenance (CBM) with online remote diagnosis. If the maintenance frequency is "Online" in step S111 (S111: Yes), the processing of the processing unit 3 proceeds to step S112.

[0088] In step S112, the processing unit 3 sets an inspection in accordance with a predetermined procedure. That is, the inspection is performed during maintenance or other procedures in accordance with condition-based maintenance (CBM). For example, assume that a failure mode of bearing wear of component B (see ID=3 in FIG. 6) is selected in steps S103 and S104, and a maintenance plan pattern is created to perform a maintenance improvement method in the first year of the planned fiscal year. In this case, as shown in the maintenance improvement method information 13 in FIG. 6, online remote diagnosis using a vibrometer is performed. Therefore, the processing unit 3 sets a maintenance plan pattern to perform online remote diagnosis for bearing wear of component B.

[0089] If the maintenance frequency is not "Online" in step S111 (S111: No), the processing unit 3 proceeds to step S113. In step S113, the processing unit 3 sets an inspection in accordance with a predetermined maintenance frequency (inspection cycle) and performs measures such as maintenance based on the inspection results.

[0090] Furthermore, if the maintenance strategy is not condition-based maintenance (CBM) in step S110 (S110: No), the processing of the processing unit 3 proceeds to step S114. In step S114, the processing unit 3 determines not to perform preventive maintenance. In this case, BDM (reactive maintenance) is set, in which maintenance, etc. is performed after an asset failure occurs. In other words, it is set so that inspection and treatment will not be performed in the planned fiscal year (t=1) set in step S102.

[0091] After performing any one of steps S109 and S112 to S114, the processing of the processing unit 3 proceeds to step S115. In step S115, the processing unit 3 determines whether or not another failure mode exists. That is, the processing unit 3 determines whether or not another failure mode exists for a predetermined component in the first year of the planning fiscal year (t=1 in S102) that is different from the one selected in the first step S104. If another failure mode exists in step S115 (S115: Yes), the processing of the processing unit 3 returns to step S104. Then, in step S104, the processing unit 3 appropriately selects another predetermined failure mode based on the asset knowledge information 11 (see FIG. 4).

[0092] Furthermore, if no other failure modes exist in step S115 (S115: No), the processing of the processing unit 3 proceeds to step S116. In step S116, the processing unit 3 determines whether or not other components exist. That is, the processing unit 3 determines whether or not other components exist that are different from the components selected in the first step S103 for the first year of the planning fiscal year (t=1 in S102). If other components exist in step S116 (S116: Yes), the processing of the processing unit 3 returns to step S103. Then, in step S103, the processing unit 3 appropriately selects other predetermined components based on the asset knowledge information 11 (see FIG. 4).

[0093] Furthermore, if there are no other components in step S116 (S116: No), the processing by the processing unit 3 proceeds to step S117. In step S117, the processing unit 3 determines whether or not t has reached a predetermined number of planned years. The predetermined number of planned years is a preset upper limit value for the planned year when the processing unit 3 creates the maintenance plan pattern information 16. If t has not reached the predetermined number of planned years in step S117 (S117: No), the processing unit 3 proceeds to step S118.

[0094] In step S118, the processing unit 3 increments the value of t, which is the planned year, and returns to the processing of step S103. Also, if t has reached the predetermined number of planned years in step S117 (S117: Yes), the processing of the processing unit 3 proceeds to step S119 in FIG. 16B. In step S119, the processing unit 3 predicts the reliability of the asset using the reliability prediction unit 37. That is, the processing unit 3 calculates reliability indices such as the future failure probability (see FIG. 15 ) when maintenance work is performed in accordance with the maintenance plan pattern, based on the asset knowledge information 11 (see FIG. 4 ), current maintenance method information 12 (see FIG. 5 ), and maintenance improvement method information 13 (see FIG. 6 ).

[0095] Next, in step S120, the processing unit 3 calculates a predetermined evaluation index using the pattern evaluation unit 38. That is, the processing unit 3 calculates an evaluation index such as a KPI based on the maintenance plan pattern and the prediction results of the reliability index. For example, if the total cost of maintenance work is specified as an item of a KPI that the user wishes to improve, the processing unit 3 calculates the evaluation index as follows. First, the processing unit 3 calculates the corrective maintenance cost when corrective maintenance (BDM) is performed on the asset based on the future failure probability of the asset. Then, the processing unit 3 includes the corrective maintenance cost in the total cost required for maintaining the asset. Specifically, the processing unit 3 calculates the total cost by adding the replacement cost, inspection cost, treatment cost, parts procurement cost, maintenance changeover cost, and the corrective maintenance cost.

[0096] Next, in step S121, the processing unit 3 determines whether or not the maintenance schedule pattern satisfies predetermined constraints. The constraints are set in advance based on the external condition information 14 (see FIG. 3) including the support deadline information 14a (see FIG. 7) and the parts supply deadline information 14b (see FIG. 8), as well as the user's maintenance policy information 15 (see FIG. 3).

[0097] It is also possible to discard a maintenance planning pattern in which maintenance is performed in excess of the inventory by introducing a condition that the inventory quantity (t) is 0 as one of the constraints. In a maintenance planning pattern, when parts are procured by the year T when parts supply is stopped, the inventory quantity (t) of parts in year t is calculated based on the following formula (1). The procurement quantity (t) included in formula (1) is the amount of parts procured in year t. Furthermore, the inventory quantity (t-1) is the amount of parts in stock in year (t-1). Furthermore, the usage quantity (t) is the amount of parts used in year t.

[0098] Inventory amount (t) = Procurement amount (t) + Inventory amount (t-1) - Usage amount (t) ... (1)

[0099] Incidentally, for the amount of use (t), when t≦T, the amount of use (t) = 0, and when t>T, the amount of use (t) = treatment (t). In other words, when a predetermined treatment is carried out using a part in year t, the procured part is consumed only once. Also, since the procurement of parts has stopped when t>T, the amount of procurement (t) = 0.

[0100] If the maintenance plan pattern does not satisfy the predetermined constraint conditions such as external conditions in step S121 of Fig. 16B (S121: No), the processing by the processing unit 3 returns to step S101 of Fig. 16A. In this case, a new maintenance plan pattern is created based on a predetermined algorithm such as mathematical optimization (S101). Also, if the maintenance plan pattern satisfies the predetermined constraint conditions such as external conditions in step S121 (S121: Yes), the processing by the processing unit 3 proceeds to step S122.

[0101] In this way, the processing unit 3 creates a time-series maintenance plan pattern based on the asset knowledge information 11, the current maintenance method information 12, the maintenance improvement method information 13, and the external condition information 14 so that the specified external conditions identified in the external condition information 14 are satisfied.

[0102] In step S122, the processing unit 3 determines, via the pattern evaluation unit 38, whether the maintenance plan pattern satisfies the conditions of a predetermined evaluation index. For example, the processing unit 3 identifies the maintenance plan pattern with the best KPI evaluation index from among multiple maintenance plan patterns that satisfy the constraint conditions in step S121. That is, the processing unit 3 creates a maintenance plan pattern that satisfies the conditions of the KPI items specified by the user. For example, if the KPI that the user places importance on is total cost, the processing unit 3 selects, as the best maintenance plan pattern, a maintenance plan pattern that minimizes total cost. This makes it possible to create a maintenance plan pattern that meets the user's desires regarding KPIs.

[0103] Furthermore, the processing unit 3 may create a maintenance plan pattern so that the conditions for replacement timing when replacing an asset with a new one or the priority of replacement relative to other assets are satisfied. This allows a maintenance plan pattern to be created that meets the user's wishes regarding replacement timing and priority of replacement.

[0104] The maintenance policy information 15 (see FIG. 3) includes the user's policy regarding parts supply deadlines (see FIG. 13). If this policy is to procure parts to be used for maintenance until their supply deadlines, the processing unit 3 first refers to the external condition information 14 (see FIG. 3) to identify the parts supply deadlines, although this is omitted in FIGS. 16A and 16B. The processing unit 3 then creates a maintenance plan pattern so that a predetermined number of parts are procured by the parts supply deadlines.

[0105] The maintenance policy information 15 (see FIG. 3) also includes the user's response policy regarding the end of support deadlines for technicians (see FIG. 13). If this response policy is for the user to provide in-house training regarding asset maintenance, the processing unit 3 includes the training costs required for the in-house training in the total cost required for asset maintenance. This allows the total cost required for asset maintenance to be calculated accurately.

[0106] Next, in step S123, the processing unit 3 displays the results on the display unit 4. That is, the processing unit 3 displays the maintenance plan patterns that satisfy the conditions of step S122 on the display unit 4. Note that it is preferable that the processing unit 3 compares and displays on the display unit 4 the values ​​of KPI items designated by the user, the total cost required for asset maintenance, the trend in the future asset failure probability, or the trend in the number of asset replacement units in the future, between the case where the current maintenance method is continued and the case where maintenance based on the maintenance plan pattern is performed. This makes it easier for the user to understand what benefits there are to be gained by performing maintenance in accordance with the maintenance plan pattern.

[0107] Furthermore, information such as the maintenance plan pattern may be transmitted from the maintenance improvement proposal support device (see FIG. 2) to the user terminal 20 (see FIG. 2) via the network N1 (see FIG. 2). This allows the information such as the maintenance plan pattern to be provided to the user. After performing the process of step S123, the processing unit 3 ends the series of processes (END).

[0108] 16A and 16B, the processing unit 3 determines whether or not a maintenance switch has been performed in step S105, but this is not limiting. For example, by setting the value indicating whether or not a maintenance switch has been performed for each year to 0 (setting to no maintenance switch), predicted values ​​of future costs and reliability indexes in the case where the current maintenance method is continued may be calculated.

[0109] FIG. 17 is an example of a display showing the transition of the annual cost required for asset maintenance and the failure probability. The horizontal axis of FIG. 17 represents the planned year when a maintenance plan pattern is created. The vertical axis (solid line) on the left side of FIG. 17 represents the annual maintenance cost required for maintenance when a predetermined maintenance plan pattern is adopted. The vertical axis (dashed line) on the right side of FIG. 17 represents the annual asset failure probability when a predetermined maintenance plan pattern is adopted.

[0110] In the example of Figure 17, the cost trends for each planning year are shown in a bar graph, and the asset failure probability trends are shown in a curve (dashed line). As described above, assets deteriorate over time, and the failure probability increases with age. However, if a prescribed procedure such as maintenance is performed on an asset, the failure probability temporarily decreases. By displaying a graph such as that shown in Figure 17, the user can grasp the trends in maintenance costs and failure probability at a glance. It is also possible to display a comparison of the trends in maintenance costs and failure probability when the user continues using their current maintenance method and when the maintenance method is improved (including when the maintenance method is selectively improved in some planning years).

[0111] Figure 18 is a display example comparing the total costs when the current maintenance method is continued and when maintenance is performed based on a maintenance plan pattern. It should be noted that the KPI that the user values ​​in Figure 18 is the total cost required to maintain the asset. Of the two bar graphs in Figure 18, the bar graph on the left side of the page shows a breakdown of the total costs when the user's current maintenance method is continued. The bar graph on the right side of the page shows a breakdown of the total costs when a predetermined maintenance plan pattern based on a maintenance improvement method is adopted.

[0112] This comparative display allows the user to understand, for example, that the maintenance plan pattern significantly reduces the costs of inspection, treatment, and corrective maintenance. The user can also understand that the maintenance plan pattern requires the introduction and procurement costs of IoT (Internet of Things).

[0113] FIG. 19 is a display example showing the transition of parts procurement costs in preparation for the arrival of a parts supply deadline. The horizontal axis of FIG. 19 represents the planned year in the maintenance planning pattern (see FIG. 14). The vertical axis of FIG. 19 represents the parts procurement cost for each planned year based on the maintenance planning pattern. The example of FIG. 19 shows that a predetermined amount of parts is procured before the parts supply deadline. By procuring parts in advance in this way, assets can be maintained using parts in stock even after parts supply has been stopped.

[0114] FIG. 20 is a display example comparing the transition in the number of replacement units when the status quo maintenance method is continued and when maintenance is performed based on a maintenance plan pattern. Note that in FIG. 20, the KPI that the user places importance on is the leveling of the number of replacement units. The horizontal axis of FIG. 20 is the planned year in the maintenance plan pattern (see FIG. 14). The vertical axis of FIG. 20 is the number of replacement units of assets. Note that the number of replacement units is the number of units when assets are replaced with new ones. In the example of FIG. 20, the transition in the number of replacement units for each planned year in the status quo maintenance method is shown by a solid bar graph. The transition in the number of replacement units for each planned year when a predetermined maintenance plan pattern based on the maintenance improvement method is adopted is shown by a dashed bar graph.

[0115] In the maintenance plan pattern based on the maintenance improvement method, asset life is extended and replacement timing is postponed, thereby leveling out the number of replacement units. By displaying this comparison, the user can see at a glance that the maintenance plan pattern aims to level out the number of replacement units.

[0116] <Effects> According to this embodiment, it is possible to propose a maintenance plan pattern that is in line with the user's maintenance policy, taking into consideration the end of support deadlines for technicians and parts procurement deadlines, while maintaining asset reliability. Furthermore, even in situations where the user owns many aging assets, it is possible to propose a maintenance plan pattern that reduces maintenance costs while maintaining asset reliability and further aims to level out asset replacement. In this way, according to this embodiment, it is possible to provide a highly convenient maintenance improvement proposal support system P1 and maintenance improvement proposal support method.

[0117] <<Modifications>> The maintenance improvement proposal support system P1 and the maintenance improvement proposal support method according to the present disclosure have been described above in the embodiments, but the present disclosure is not limited to these descriptions and various modifications can be made. For example, in the embodiments, a case has been described in which a future failure probability is used as a reliability index for an asset, but this is not limiting. That is, the mean time between failures of an asset may be used as a reliability index. Note that the mean time between failures is the average time interval when asset failures are assumed.

[0118] Furthermore, in the embodiment, a case has been described in which both the support end date and the parts supply date are used as external conditions, but this is not limiting. That is, one of the support end date and the parts supply date may be used as the external condition. Even in this case, the same effects as those of the embodiment can be achieved. Furthermore, a failure probability may be used as a constraint condition used in creating a maintenance schedule pattern. Specifically, a maintenance schedule pattern may be created so that the future failure probability is equal to or less than a predetermined threshold value.

[0119] Furthermore, the processing described in the embodiment can also be solved using, for example, mixed integer programming, with each element of the maintenance plan pattern as a decision variable. Furthermore, AI may be used as appropriate when generating a maintenance plan pattern. For example, a maintenance improvement proposer M1 may provide information such as asset knowledge, maintenance methods, external conditions, and maintenance policies to the generation AI, and obtain a solution from the generation AI via chat.

[0120] Furthermore, the process (maintenance improvement proposal support method) executed by the maintenance improvement proposal support system P1 may be executed as a predetermined program on a computer. The program may be provided via a communication line or may be written to a predetermined recording medium and distributed.

[0121] Furthermore, the present disclosure is not limited to the embodiments and includes various modifications. For example, the embodiments have been described in detail to clearly explain the present disclosure, and the present disclosure is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to add, delete, or replace part of the configuration of the embodiments with other configurations.

[0122] Furthermore, the above-described configurations, functions, processing units, processing means, etc. may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. The above-described configurations, functions, etc. may also be implemented in software, with a processor interpreting and executing a program that implements each function. Information such as the programs, tables, and files that implement each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD.

[0123] In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and do not necessarily show all the control lines and information lines in the product. In reality, it can be assumed that almost all components are interconnected.

[0124] DESCRIPTION OF SYMBOLS 1 Storage unit 2 Input unit 3 Processing unit 4 Display unit 10 Maintenance improvement proposal support device 11 Asset knowledge information 12 Current maintenance method information 13 Maintenance improvement method information 14 External condition information 15 Maintenance policy information 16 Maintenance plan pattern information 31 Asset knowledge management unit 32 Current maintenance method management unit 33 Maintenance improvement method management unit 34 External condition management unit 35 Maintenance policy management unit 36 ​​Time-series maintenance plan pattern creation unit 37 Reliability prediction unit 38 Pattern evaluation unit P1 Maintenance improvement proposal support system

Claims

1. A maintenance improvement proposal support system having a processing unit that creates a time-series maintenance plan pattern so that the external conditions are satisfied based on asset knowledge information regarding failures that may occur in an asset, current maintenance method information indicating the current maintenance method for the asset, maintenance improvement method information indicating a maintenance improvement method for improving the current maintenance method for the asset, and external condition information that includes, as external conditions, one or both of the support end date and parts supply deadline for the technician who maintains the asset.

2. The maintenance improvement proposal support system according to claim 1, wherein the maintenance policy information indicating the user's maintenance policy when maintaining the asset includes a KPI (Key Performance Indicator) item designated by the user, and the processing unit creates the maintenance plan pattern so that the conditions of the KPI item designated by the user are met.

3. The maintenance improvement proposal support system of claim 1, wherein the maintenance policy information indicating the user's maintenance policy when maintaining the asset includes the timing of replacement of the asset with a new one or the priority of replacement relative to other assets, and the processing unit creates the maintenance plan pattern so that the conditions of the replacement timing or the priority are met.

4. The maintenance improvement proposal support system of claim 1, wherein the external condition information includes a supply deadline for the parts, and maintenance policy information indicating the user's maintenance policy when maintaining the assets includes the user's response policy regarding the supply deadline for the parts, and when the response policy is a policy to procure the parts by the supply deadline for the parts, the processing unit refers to the external condition information to identify the supply deadline for the parts, and creates the maintenance plan pattern so that a predetermined amount of the parts will be procured by the supply deadline for the parts.

5. The maintenance improvement proposal support system of claim 1, wherein the external condition information includes the end of support date for the technician, and maintenance policy information indicating the user's maintenance policy when maintaining the asset includes the user's response policy regarding the end of support date for the technician, and if the response policy is a policy for the user to provide in-house training regarding the maintenance of the asset, the processing unit includes the training costs required for the in-house training in the total cost required for maintaining the asset.

6. The maintenance improvement proposal support system described in claim 1, characterized in that the processing unit calculates the cost of corrective maintenance when corrective maintenance is performed on the asset based on the future failure probability of the asset, and includes the corrective maintenance cost in the total cost required to maintain the asset.

7. The maintenance improvement proposal support system according to claim 1, wherein the maintenance plan pattern is a time-series maintenance plan that includes whether or not to replace the asset, whether or not to inspect and take action for the failure mode of the asset, whether or not to switch the maintenance method, and the quantity of parts to be procured for the asset.

8. The maintenance improvement proposal support system according to claim 1, wherein the processing unit causes the display unit to compare and display the values ​​of KPI items designated by the user, the total cost required to maintain the asset, the trend in the future failure probability of the asset, or the trend in the number of replacement units of the asset in the future, between the case where the current maintenance method is continued and the case where maintenance is performed based on the maintenance plan pattern.

9. A maintenance improvement proposal support method including a process for creating a time-series maintenance plan pattern based on asset knowledge information regarding failures that may occur in an asset, current maintenance method information indicating the current maintenance method for the asset, maintenance improvement method information indicating a maintenance improvement method for improving the current maintenance method for the asset, and external condition information including, as external conditions, one or both of the support end date and parts supply deadline for the technician who maintains the asset.

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