Information processing device, program, and evaluation method

Abstract

To perform an evaluation that appropriately reflects a deterioration state of an evaluation object.SOLUTION: According to an embodiment, an information processing device accepts evaluation time, a state level, manufacturing time, and a new-product price of an evaluation object, calculates an elapsed time from the evaluation time and the manufacturing time, finds a remaining useful life of the evaluation object based on first data, the state level, and the elapsed time, finds a normal useful life based on second data, subtracts the remaining useful life from the normal useful life to calculate an effective time, calculates a residual value rate of the evaluation object based on a function included in third data and the effective time, and calculates an evaluation amount of the evaluation object by multiplying the new-product price and the residual value rate.SELECTED DRAWING: Figure 6

Description

[Technical field]

[0001] The present embodiment relates to an information processing device, a program, and an evaluation method for evaluating an evaluation target. [Background technology]

[0002] Patent document 1 (JP 2021-2263 A) discloses an apparatus that calculates an equipment valuation amount for an object to be evaluated, such as equipment (facility), taking into account the recovery of remaining useful life through updates (e.g., renovations or additional investments) and in line with the actual condition of the equipment to be evaluated. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-2263 Summary of the Invention [Problem to be solved by the invention]

[0004] The present embodiment provides an information processing device, a program, and an evaluation method for performing an evaluation that appropriately reflects the degradation state of an evaluation target. [Means for solving the problem]

[0005] According to this embodiment, the information processing device includes a processing device and a storage device that stores data used by the processing device. The storage device stores first data that associates elapsed time, status level, and remaining useful life, second data that includes normal useful life, and third data that includes a function for calculating a residual value rate based on the effective time. The processing device receives an evaluation time of the evaluation object, a status level of the evaluation object, a manufacturing time or a start time of use of the evaluation object, and a new price of the evaluation object, calculates the elapsed time of the evaluation object from the evaluation time and the manufacturing time or the start time of use, calculates the remaining useful life of the evaluation object based on the first data stored in the storage device, the status level of the evaluation object, and the elapsed time of the evaluation object, and calculates the normal useful life of the evaluation object based on the second data stored in the storage device, calculates the effective time of the evaluation object by subtracting the remaining useful life of the evaluation object from the normal useful life of the evaluation object, calculates the residual value rate of the evaluation object based on the function included in the third data stored in the storage device and the effective time of the evaluation object, and calculates the evaluation amount of the evaluation object by multiplying the new price of the evaluation object and the residual value rate of the evaluation object. [Brief description of the drawings]

[0006] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of an information processing device according to a first embodiment. [Diagram 2] FIG. 2 is a view showing an example of data used by the information processing apparatus according to the first embodiment. [Diagram 3] 4 is a view showing an example of data processed by an evaluation unit of the information processing device according to the first embodiment; [Figure 4] 6 is a flowchart showing an example of a process for generating remaining service life data and normal service life data according to the first embodiment. [Diagram 5] 5 is a flowchart showing an example of a process for generating price function data according to the first embodiment. [Figure 6] 5 is a flowchart showing an example of evaluation processing according to the first embodiment. [Figure 7] A diagram showing an example of the distribution of elapsed time and number of retired assets. [Figure 8]An example of the distribution of survival rate versus elapsed time. [Figure 9] FIG. 11 is a diagram showing an example of generation of remaining useful life data according to the second embodiment. [Figure 10] FIG. 13 is a diagram showing an example of generating price function data according to the third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, each embodiment will be described with reference to the drawings. In the following description, the same reference numerals will be used to designate the same functions and components, and the description will be omitted and repeated description will be given only when necessary.

[0008] [First embodiment] In the first embodiment, an information processing device that calculates an evaluation amount that reflects the deterioration state of an evaluation object based on an effective time that reflects the state of the evaluation object and a price function will be described.

[0009] In the following, the effective time will be described using effective years as an example, but other units such as effective months, effective days, effective minutes, and effective seconds may be used instead of effective years. The object of evaluation may be a classifiable asset or an identifiable type of asset. The evaluation targets belong to one of the asset classifications (or types) that are treated statistically, such as metalworking machines, construction and mining machines, food processing machines, agricultural machines, textile machines, lumbering, woodworking and plywood machines, chemical machines, printing, bookbinding and paper processing machines, prime movers, transport machines, pumps and compressors, electrical equipment, private automobiles, freight vehicles, physico-chemical machines and instruments, analyzers, testing machines, weighing machines and measuring instruments, electronic computers and their accessories, copiers, machinery and instruments, and optical machines. These asset classifications are examples and can be freely changed or set. For example, the evaluation targets may be evaluated using classifications smaller than the above asset classifications, such as hydraulic shovels instead of construction and mining machines, conveyors instead of transport machines, and lathes instead of metalworking machines.

[0010] Fig. 1 is a block diagram showing an example of the configuration of an information processing device 1 according to the first embodiment. Various components illustrated in Fig. 1 may be freely combined or freely separated as long as they can achieve the same or similar functions and actions.

[0011] The information processing device 1 operates as an evaluation device that evaluates the asset value of an evaluation target. The information processing device 1 includes, for example, an operation device 2, a display device 3, a storage device 4, and a processing device 5.

[0012] The information processing device 1 accepts, from an operating device 2 operated by the user, the asset classification of the object to be evaluated, the deterioration state of the object to be evaluated (e.g., excellent, good, or fair), the evaluation reference time for evaluating the object to be evaluated (e.g., evaluation reference date), the manufacturing time of the object to be evaluated (e.g., date of manufacture) or the start time of use (e.g., date of start of use), and the new price of the object to be evaluated, all input by the user.

[0013] The information processing device 1 stores remaining durability data A1 to An, normal durability data 8, and value function data 9 in the storage device 4.

[0014] The information processing device 1 calculates, by the processing device 5, an appraisal amount that reflects the deterioration state of the appraisal object, based on the above data input by the user and the above data stored in the storage device 4.

[0015] Then, the information processing device 1 causes the display device 3 to display data including the evaluation amount calculated by the processing device 5.

[0016] The operation device 2 accepts a user's operation and transmits an operation signal indicating the content of the user's operation to the processing device 5. The operation device 2 may be, for example, a keyboard, a touch panel, or a pointing device.

[0017] The display device 3 may be, for example, a liquid crystal display device, an organic EL (Electro Luminescence) device, etc. The display device 3 receives a display signal from the processing device 5 and displays an image corresponding to the display signal.

[0018] The storage device 4 stores various software such as, for example, data input by a user using the operation device 2, data used by the processing device 5, data generated by the processing device 5, and a program 6 executed by the processing device 5. The software stored in the storage device 4 may include, for example, various setting values, statistical data collected in advance, and the like.

[0019] In the first embodiment, the storage device 4 stores, for example, a program 6, input data 7, remaining durability data A1 to An, normal durability data 8, value function data 9, evaluation result data 10, and the like.

[0020] The program 6, when executed by the processing device 5, calculates an evaluation, such as price, reflecting the deterioration state of the object to be evaluated based on input data 7 entered by a user, remaining durability data A1 to An, normal durability data 8, and price function data 9 stored in the storage device 4, and performs processing to generate and output evaluation result data 10.

[0021] The program 6 causes the processing device 5 to realize the functions of an input control unit 11, a data generating unit 12, an evaluation unit 13, and an output control unit .

[0022] The input data 7 is data input by a user, and includes, for example, the asset classification of the asset to be evaluated, a plurality of condition levels, an evaluation reference time, a manufacturing time, and a new product price.

[0023] In the first embodiment, the time of use (operation) may be used instead of the manufacturing time. The condition level may be, for example, one of "excellent," "good," and "fair." The new product price may be the actual new re-purchase price or a reasonable estimated price.

[0024] Each of the remaining useful life data A1 to An corresponds to a plurality of asset classifications. The remaining useful life data A1 is data that manages the remaining useful life corresponding to each of a plurality of condition levels for each elapsed time for the asset classification corresponding to the remaining useful life data A1. For example, the remaining useful life data A1 may correspond to the asset classification "construction and mining machinery". For example, the plurality of condition levels may be "excellent", "good", and "fair". The remaining useful life data A2 to An are data that manage the remaining useful life corresponding to each of a plurality of conditions for each elapsed time for an asset classification different from the remaining useful life data A1.

[0025] The remaining durability data A1 to An may be data in a table format, for example. The remaining durability data A1 to An do not have to be managed separately from each other. That is, for example, at least two of the remaining durability data A1 to An may be combined and managed as one data.

[0026] Normal life data 8 includes normal life times, such as average life spans, for each of a number of asset classes.

[0027] The price function data 9 may be data including an approximation formula with elapsed time as an explanatory variable and residual value rate as an explained variable (objective variable) for each of a plurality of asset classes. The price function data 9 may be data in a table format in which a function and a parameter are associated with each of a plurality of asset classes, for example.

[0028] The evaluation result data 10 includes the evaluation amount of the asset to be evaluated, calculated by the evaluation unit 13 of the processing device 5 .

[0029] Specific examples of the input data 7, remaining durability data A1 to An, normal durability data 8, and price function data 9 will be described later with reference to FIG.

[0030] The processing device 5 may be, for example, a microcomputer or a processor. The processing device 5 executes various processes based on the program 6 and various data stored in the storage device 4. More specifically, the processing device 5 executes the program 6 stored in the storage device 4 to realize the functions of an input control unit 11, a data generating unit 12, an evaluation unit 13, and an output control unit 14.

[0031] The input control unit 11 receives a signal indicating the content input by the user using the operation device 2. The input control unit 11 causes the storage device 4 to store the input data 7 accepted by the operation device 2, for example.

[0032] In addition, the input control unit 11 may receive original data required to generate, for example, the remaining durability data A1 to An, the normal durability data 8, the price function data 9, etc., from the operation device 2, an external information processing device, or an external storage device, and store the same in the storage device 4.

[0033] The data generation unit 12 generates data required for evaluation processing, such as remaining durability data A1 to An, normal durability data 8, price function data 9, etc., based on original data received from the operation device 2, an external information processing device, or an external storage device and stored in the storage device 4, and stores the generated data in the storage device 4.

[0034] Specific examples of the generation of the remaining durability data A1 to An, the normal durability data 8, and the price function data 9 will be described later with reference to FIGS. 4, 5, and 7 to 10. FIG.

[0035] The evaluation unit 13 generates evaluation result data 10 including an evaluation amount reflecting the deterioration state of the evaluation object based on the input data 7, at least one of the remaining durability data A1 to An, normal durability data 8, and price function data 9 stored in the memory device 4, and stores the evaluation result data 10 in the memory device 4.

[0036] The output control unit 14 executes output processing such as displaying various data, such as screen data displayed by the program 6 and evaluation result data 10 stored in the memory device 4, on the display device 3, printing by a printer, or transmitting various data to other devices.

[0037] Fig. 2 is a diagram showing an example of data used by the information processing device 1 according to the first embodiment. Fig. 2 is an example and can be modified as appropriate.

[0038] Input data 7 includes the asset classification "Construction / Mining Machinery" to which the evaluation object (e.g., an asset such as equipment or facilities) entered by the user belongs, the condition level of the evaluation object is "Good", the evaluation reference time "2023 / 6 / 30" which is the reference time for obtaining the evaluation amount, the manufacturing time (or start time of use) of the evaluation object is "2013 / 1 / 1", and the new price (or new acquisition price) of the evaluation object is "20,000,000".

[0039] The remaining useful life data A1 includes, for the asset classification "construction and mining machinery", the relationship between the elapsed time (years) "1", "2" ... and the remaining useful life (life expectancy) for each condition level "excellent", "good", "fair".

[0040] The remaining useful life data A2...An includes, for asset classifications other than the asset classification "construction and mining machinery", the relationship between the elapsed time "1", "2", etc. and the remaining useful life for each condition level "excellent", "good", "fair", similar to the remaining useful life data A1.

[0041] The normal service life data 8 includes data associating various asset classifications such as “construction and mining machinery,” “accessories,” “vehicles,” etc. with the normal service life, which is the average service life (lifespan), such as “16.8,” “14.2,” “12.05,” etc.

[0042] Price function data 9 includes data relating to the type of price function used to calculate the residual value rate based on the effective time for various asset classifications such as "construction and mining machinery"..., and the values ​​○, △ of parameters a and b of the price function.

[0043] The evaluation unit 13 calculates the valuation amount of the evaluation object belonging to the asset class "construction and mining equipment" based on input data 7 including the asset class "construction and mining equipment", remaining useful life data A1 corresponding to the asset class "construction and mining equipment", the normal useful life of "16.8" for the asset class "construction and mining equipment" included in normal useful life data 8, the function type "linear" for the asset class "construction and mining equipment" included in price function data 9, and the values ​​○ and △ of parameters a and b.

[0044] Then, the evaluation unit 13 generates evaluation result data 10 including the calculated evaluation amount.

[0045] Fig. 3 is a diagram showing an example of data processed by the evaluation unit 13 of the information processing device 1 according to the first embodiment. The evaluation unit 13 may execute processing while storing various data shown in Fig. 3 in the storage device 4. The evaluation unit 13 may also read out the various data shown in Fig. 3 from the storage device 4 and use it.

[0046] The evaluation unit 13 subtracts the manufacturing time "2013 / 1 / 1" from the evaluation reference time "2023 / 6 / 30" of the input data 7 to calculate the elapsed time "10 years".

[0047] Based on the elapsed time of "10 years" and the condition level of "good" of the input data 7, the evaluation unit 13 refers to the remaining useful life data A1 corresponding to the asset classification "construction / mining machinery" of the input data 7, and obtains the remaining useful life of "11.6 years" corresponding to the elapsed time of "10 years", the condition level of "good", and the asset classification of "construction / mining machinery".

[0048] The evaluation unit 13 refers to the normal service life data 8 and obtains the normal service life of "16.8 years" corresponding to the asset classification "construction and mining machinery" of the input data 7.

[0049] The evaluation unit 13 subtracts the remaining useful life of "11.6 years" from the normal useful life of "16.8 years" to calculate the effective life of "5.2 years."

[0050] The evaluation unit 13 refers to the price function data 9 based on the asset classification “construction / mining machinery” of the input data 7, and obtains the type of price function “linear” corresponding to the asset classification “construction / mining machinery” and the values ​​of parameters a and b (○, △).

[0051] The evaluation unit 13 calculates a residual value rate of "33.1%" based on the effective time of "5.2 years," the type of function of "linear," and the values ​​of parameters a and b of ○ and △.

[0052] Then, the evaluation unit 13 multiplies the new price "20,000,000 yen" of the input data 7 by the residual value rate "33.1%" to calculate the evaluation amount "6,622,000 yen."

[0053] Fig. 4 is a flowchart showing an example of a process for generating remaining service life data A1 to An and normal service life data 8 according to the first embodiment. In Fig. 4, at least one of S405 and S406 may be executed before or after S402 to S404, or may be executed simultaneously with S402 to S404.

[0054] A specific example of generating the remaining durability data A1 to An and the normal durability data 8 will be described later in the second embodiment.

[0055] In S401, the data generating unit 12 acquires the distribution of the elapsed time and the number of retired assets (number of failures) for each asset class. The distribution of the elapsed time and the number of retired assets includes, for example, the relationship between the elapsed time (e.g., the period of use) and the number of retired assets for each asset class. Note that data compiled and published by a public institution may be used as the distribution of the elapsed time and the number of retired assets for each asset class.

[0056] In S402, the data generating unit 12 generates a life function for each asset class based on the distribution of elapsed time and number of retired assets for each asset class. For example, the data generating unit 12 may generate a life function for each asset class by calculating a cumulative retirement rate (cumulative failure rate) for each elapsed time for each asset class, calculating a survival rate = 1 - cumulative retirement rate for each elapsed time, and approximating a graph with a first axis (e.g., horizontal axis) representing elapsed time and a second axis (e.g., vertical axis) representing the survival rate using a function. From the life function, for example, the remaining useful life for each elapsed time can be calculated.

[0057] In S403, the data generating unit 12 adjusts the lifespan function generated in S402 for each asset classification, and generates a plurality of lifespan functions corresponding to each of a plurality of state levels.

[0058] For example, the data generating unit 12 sets the lifespan function generated in S402 as a first lifespan function corresponding to the condition level "good" for each asset class.

[0059] For example, the data generating unit 12 sets the second lifespan function corresponding to the condition level "excellent" to a function obtained by adjusting the first lifespan function so that the average lifespan becomes "average lifespan of the first lifespan function + standard deviation σ of the first lifespan function." This second lifespan function may be generated by fixing the shape parameters and position parameters included in the first lifespan function so that the shape is similar to that of the first lifespan function, and changing the scale parameter included in the first lifespan function so that the survival rate decays more slowly with respect to elapsed time than the first lifespan function.

[0060] For example, the data generating unit 12 sets the third lifespan function corresponding to the status level "fair" to a function obtained by adjusting the first lifespan function so that the average lifespan is "average lifespan of the first lifespan function - standard deviation σ of the first lifespan function." This third lifespan function may be generated by fixing the shape parameters and position parameters included in the first lifespan function so that the shape is similar to that of the first lifespan function, and changing the scale parameter included in the first lifespan function so that the survival rate attenuates more sharply with respect to elapsed time than the first lifespan function.

[0061] In S404, the data generating unit 12 generates remaining useful life data A1-An for each asset class, which associates the elapsed time with the remaining useful life for each of the condition levels "excellent", "good", and "fair" based on the life functions for the multiple condition levels "excellent", "good", and "fair" generated in S403. Then, the data generating unit 12 stores the remaining useful life data A1-An for each asset class in the storage device 4.

[0062] In S405, the data generating unit 12 calculates the average lifespan of each asset class based on the distribution of the elapsed time and the number of retired assets for each asset class.

[0063] In S406, the data generating unit 12 generates normal durability data 8 in which the average life span is associated with each of the asset classes. Then, the data generating unit 12 stores the normal durability data 8 in the storage device 4.

[0064] 5 is a flowchart showing an example of a process for generating the price function data 9 according to the first embodiment. A specific example of generating the price function data 9 will be described later in the third embodiment.

[0065] In S501, the data generating unit 12 acquires a distribution of elapsed time vs. residual value rate for each asset class. The distribution of elapsed time vs. residual value rate, for example, correlates elapsed time (e.g., period of use) and residual value rate for each asset class. Note that data compiled and published by a public institution may be used as the distribution of elapsed time vs. residual value rate for each asset class.

[0066] In S502, the data generating unit 12 generates a price function for each asset classification based on the distribution of elapsed time vs. residual value rate for each asset classification. For example, the data generating unit 12 may generate a price function for each asset classification by approximating a graph with a first axis (e.g., horizontal axis) representing elapsed time and a second axis (e.g., vertical axis) representing residual value rate, using a function. For example, the data generating unit 12 may determine the type of function (linear function, exponential function, logarithmic function, power function) and the values ​​of parameters a and b as the price function. The type of function and the values ​​of the parameters may be determined using, for example, the least squares method.

[0067] In S503, the data generating unit 12 generates price function data 9 in which a price function and the values ​​of its parameters a and b are associated with each asset class. Then, the data generating unit 12 stores the price function data 9 in the storage device 4.

[0068] Fig. 6 is a flowchart showing an example of the evaluation process according to the first embodiment. In the explanation of Fig. 6, for ease of understanding, the case where various data are as shown in Figs. 2 and 3 will be explained as an example.

[0069] In S601, the evaluation unit 13 receives the input data 7 input by the user from the operation device 2.

[0070] In S602, the evaluation unit 14 calculates the elapsed time "10 years" based on the difference between the evaluation reference time "2023 / 6 / 30" included in the input data 7 and the manufacturing time "2013 / 1 / 1".

[0071] In S603, the evaluation unit 14 refers to the residual value useful life data A1 corresponding to the asset type "construction / mining machinery" included in the input data 7, and obtains a remaining useful life of "11.6 years" corresponding to the condition level "good" included in the input data 7 and the elapsed time "10 years" calculated in S602. The evaluation unit 14 also refers to the normal useful life data 8, and obtains a normal useful life of "16.8 years" corresponding to the asset type "construction / mining machinery" included in the input data 7.

[0072] In S604, the evaluation unit 14 calculates an effective life of "5.2 years" by subtracting the remaining useful life of "11.6 years" from the normal useful life of "16.8 years" obtained in S603.

[0073] In S605, the evaluation unit 14 refers to the price function data 9 and calculates the residual value rate of "33.1%" based on the function "f(x)=ax+b" corresponding to the asset type "construction / mining machinery" included in the input data 7, the values ​​of the parameters a and b (○, △), and the effective time "5.2 years" calculated in S604.

[0074] In S606, the evaluation unit 14 multiplies the new price "20,000,000" included in the input data 7 by the residual value rate "33.1%" to calculate the evaluation amount "6,622,000 yen."

[0075] In S606, the output control unit 14 causes the display device 3 to display the evaluation result data 10 including the evaluation amount "6,622,000 yen", or transmits the evaluation result data 10 to another device.

[0076] The following describes the effects of the information processing device 1 according to the first embodiment.

[0077] As a first comparative example of the valuation method used in the first embodiment, there is an valuation method (cost approach) that estimates the remaining useful life from the normal useful life and the effective life to evaluate the evaluation target.

[0078] Here, the effective life is the actual useful life of an asset determined from its actual deterioration state.

[0079] In the first comparative example, the normal useful life - effective life = remaining useful life. This formula can be rewritten as normal useful life - remaining useful life = effective life.

[0080] In other words, in the first comparative example, if the remaining useful life can be estimated appropriately, the effective life of the object of appraisal can be estimated. However, in the first comparative example, it is difficult to determine the appropriate remaining useful life, for example, by eliminating arbitrariness of the appraiser.

[0081] As a second comparative example, there is a method of classifying the appraisal subject into conditions such as very good (VG), excellent (E), good (G), fair (F), poor (P), scrap (S), etc., and adjusting the appraisal amount. However, in the second comparative example, the adjustment rate must be set arbitrarily based on the appraiser's experience, etc., making it difficult to guarantee objectivity.

[0082] A third comparative example is a variation of the market approach (comparison method). In this third comparative example, the relationship between the age or operating time of the object to be evaluated and the market price is plotted to analyze the correlation. However, in the third comparative example, it is difficult to tally up the market price for each condition of the object to be evaluated, making it difficult to make an evaluation that appropriately reflects the condition of the object to be evaluated.

[0083] The evaluation process executed by the information processing device 1 according to the first embodiment solves the problems in the above first to third comparative examples.

[0084] The information processing device 1 according to the first embodiment calculates the elapsed time for the evaluation object from the inputted manufacturing time or usage start time and the evaluation reference time.

[0085] Next, the information processing device 1 calculates the remaining useful life from the input asset classification, the input condition level (e.g., excellent, good, or fair), the remaining useful life data A1 corresponding to the asset classification, and the calculated elapsed time. The information processing device 1 also calculates the normal useful life corresponding to the input asset classification from the input asset classification and normal useful life data 8.

[0086] Next, the information processing device 1 calculates the normal useful life - the remaining useful life to obtain the effective time.

[0087] Next, the information processing device 1 inputs the effective time into a price function corresponding to the input asset classification, and calculates a residual value rate.

[0088] Then, the information processing device 1 calculates the valuation amount by multiplying the input new product price or a new procurement cost, such as a reasonably estimated new product price, by the residual value rate.

[0089] In the information processing device 1 according to the first embodiment that executes the evaluation process as described above, by using an effective time and price function (residual value rate prediction model using effective time) that reflects the state of the evaluation object, it is possible to obtain an evaluation result that reflects the deterioration state of the evaluation object while eliminating arbitrariness.

[0090] [Second embodiment] In the second embodiment, a method for generating remaining service life data A1 corresponding to the asset classification "construction and mining machinery" will be described. Note that remaining service life data A2 to An corresponding to other asset classifications than the asset classification "construction and mining machinery" can also be generated in a similar manner to the remaining service life data A1 corresponding to the asset classification "construction and mining machinery", and therefore descriptions thereof will be omitted.

[0091] In the second embodiment, the remaining useful life data A1 is generated using the Weibull distribution.

[0092] In the Weibull distribution, the probability density function (failure density) is expressed by the following equation (1).

[0093]

number

[0094] In the above formula (1), t is time. Time t is the variable that appears on the horizontal axis of the Weibull distribution graph that corresponds to the test load.

[0095] m is the shape parameter. The shape parameter m determines the shape of the Weibull distribution.

[0096] η is the scale parameter. The scale parameter η determines the scale of the horizontal axis of a Weibull distribution graph.

[0097] γ is the location parameter. The location parameter γ is related to the location of the peak of the Weibull distribution.

[0098] In the Weibull distribution, the cumulative distribution function (cumulative failure rate) is given by the following equation (2).

[0099]

number

[0100] In the second embodiment, the cumulative retirement rate for each elapsed time of the asset is regarded as the cumulative distribution function of the Weibull distribution, and the survival rate for each elapsed time of the asset is regarded as 1-cumulative distribution function to generate remaining useful data A1.

[0101] FIG. 7 is a diagram showing an example of the distribution of elapsed time versus number of retired assets.

[0102] Figure 7 shows the results of investigating the lives of multiple assets for multiple asset classifications. In Figure 7, the number of retired assets for multiple elapsed times is tallied for multiple asset classifications. The distribution of elapsed time vs. number of retired assets may be determined, for example, from official statistics on asset lifespan, questionnaire surveys, or performance surveys.

[0103] To explain more specifically, for example, for a specific asset classification of "homes," the time (e.g., number of years) until assets belonging to this specific asset classification of "homes" are disposed of is collected through a questionnaire or the like. The number of valid responses to this questionnaire becomes the population parameter for the specific asset classification of "homes." The reported time until disposal is classified according to the elapsed time, for example, "0 years or more but less than 5 years," "5 years or more but less than 10 years," etc., such as "30 years or more but less than 40 years," "40 years or more but less than 50 years," and "50 years or more," and the number of assets disposed of at each elapsed time is tallied.

[0104] For the asset class "residential," the number of assets disposed of with an elapsed time of "0 to less than 5 years" is "75." For the asset class "residential," the number of assets disposed of with an elapsed time of "5 to less than 10 years" is "99." The relationship between other asset classes, elapsed time, and number of disposed assets in Figure 7 is similar.

[0105] FIG. 8 is a diagram showing an example of distribution of elapsed time versus survival rate.

[0106] In Fig. 8, survival rates for multiple elapsed times are displayed for multiple asset classifications based on the distribution of elapsed time and number of retired assets in Fig. 7. The survival rate may also be expressed as reliability.

[0107] For each asset category, the survival rate over time can be calculated by {1-(cumulative retirement rate over time)}, which is the ratio of the cumulative number of retired assets that have been retired up to that time to the base number.

[0108] The population parameter for an asset class is, as described above, the total number of assets whose life spans have been examined for that asset class.

[0109] A method for calculating the numerical values ​​shown in FIG. 8 from the numerical values ​​shown in FIG. 7 will be specifically described.

[0110] In Figure 7 above, the parameter for the asset class "Housing" is the sum of the number of retired assets for all ages of "0 to less than 5 years", "5 to less than 10 years", "30 to less than 40 years", "40 to less than 50 years", and "50 years or more" for the asset class "Housing", "75 + 99 + ... + 503 + 454 + 192". Parameters for other asset classes can be calculated using the same rules.

[0111] In Figure 7 above, for the asset class "Housing," the number of assets disposed of with an elapsed time of "0 years or more but less than 5 years" is "75." In this case, for the asset class "Housing," the cumulative number of disposed assets corresponding to the elapsed time of "0 years or more but less than 5 years" is "75." Therefore, for the asset class "Housing," the survival rate corresponding to the elapsed time of "0 years or more but less than 5 years" is 1 - (75 / parameter for the asset class "Housing") = 0.962.

[0112] In Figure 7 above, for the asset class "Housing," the number of assets disposed of with an elapsed time of "5 to 10 years" is "99." In this case, for the asset class "Housing," the cumulative number of disposed assets corresponding to the elapsed time of "5 to 10 years" is the total number of assets disposed of from "0 to 5 years" to "5 to 10 years," which is "75 + 99." Therefore, for the asset class "Housing," the survival rate corresponding to the elapsed time of "5 to 10 years" is 1-{(75+99) / modulus of asset class "Housing"} = 0.912.

[0113] In Figure 7 above, for the asset class "Housing," the number of assets disposed of with an elapsed time of "30 to less than 40 years" is "503." In this case, for the asset class "Housing," the cumulative number of disposed assets corresponding to the elapsed time of "30 to less than 40 years" is the total number of assets disposed of from "0 to less than 5 years" to "30 to less than 40 years," "75 + 99 + ... + 503." Therefore, for the asset class "Housing," the survival rate corresponding to the elapsed time of "30 to less than 40 years" is 1-{(75 + 99 + ... + 503) / modulus of the asset class "Housing"} = 0.912.

[0114] The survival rates corresponding to the other asset classes and elapsed times in FIG. 8 can also be calculated according to the same rules.

[0115] FIG. 9 is a diagram showing an example of generation of remaining useful data A1 according to the second embodiment.

[0116] For example, the first axis (horizontal axis) is the elapsed time, the second axis (vertical axis) is the survival rate, and the survival rate for each elapsed time of the asset classification "Construction and mining machinery" included in Figure 8 above is plotted on a graph, which is estimated as the life function.

[0117] Weibull distributions such as the Weibull probability density function in equation (1) above and the Weibull cumulative distribution function in equation (2) above are used as functions that describe the lifespan and failure rate of machines. The Weibull cumulative distribution function represents the cumulative failure occurrence rate. The lifespan function is equivalent to (1-cumulative failure rate function). The lifespan function may also be expressed as a reliability function or a survival rate function.

[0118] By applying the concept of life tables to the life function, it is possible to obtain the normal useful life, which is the average lifespan of the asset classification "construction and mining machinery," and generate normal useful life data8.

[0119] In the third embodiment, the estimated life function is associated with a condition level "good" and is set as a first life function.

[0120] A lifespan function (second lifespan function) corresponding to the state level "excellent" is generated by adjusting the first lifespan function. As a specific example, the second lifespan function is generated by adjusting the first lifespan function so that (average value of the second lifespan function=average value of the first lifespan function+standard deviation σ of the first lifespan function), fixing the shape parameter m and position parameter γ of the second lifespan function to those of the first lifespan function, and adjusting the scale parameter η of the second lifespan function so that the decay of the survival rate with respect to elapsed time is more gradual than that of the first lifespan function (in other words, so that the deterioration of the evaluation target does not progress). Here, the position parameter γ may be set to zero.

[0121] A life function (third life function) corresponding to the status level "fair" is also generated by adjusting the first life function. As a specific example, the third life function is generated by adjusting the first life function so that (average value of the third life function=average value of the first life function-standard deviation σ of the first life function), fixing the shape parameter m and position parameter γ of the third life function to those of the first life function, and adjusting the scale parameter η of the third life function so that the decay of the survival rate with respect to elapsed time is steeper than that of the first life function (in other words, so that the deterioration of the evaluation target progresses). Here, the position parameter γ may be set to zero.

[0122] The scale parameter η of the cumulative distribution function of the Weibull distribution determines the degree of expansion or contraction of the cumulative distribution function of the Weibull distribution in the elapsed time direction (horizontal axis direction). By adjusting the scale parameter η and fixing the shape parameter m and position parameter γ, it is possible to expand or compress the graph of the survival rate of the asset class in the elapsed time direction while keeping the shape of the graph of the survival rate of the asset class similar.

[0123] The three lifespan functions obtained as described above do not result in a survival rate of zero even if the elapsed time is extended infinitely, so these three lifespan functions are adjusted so that the survival rate becomes zero at a certain elapsed time.

[0124] In this way, by setting life functions corresponding to the condition levels "excellent," "good," and "fair" for the asset classification "construction and mining machinery," it becomes possible to estimate the remaining useful life according to the condition of the object being evaluated, and to calculate normal useful life - remaining useful life = effective life.

[0125] From the life functions for the condition levels "excellent," "good," and "fair" for the asset classification "construction and mining machinery," remaining useful life data A1 is generated that correlates the elapsed time with the remaining useful life for each condition level of "excellent," "good," and "fair" for the asset classification "construction and mining machinery."

[0126] [Third embodiment] In the third embodiment, a method for generating price function data 9 including a price function corresponding to the asset class "construction and mining machinery" will be described. Note that price functions corresponding to asset classes other than the asset class "construction and mining machinery" can also be generated in a similar manner to the price function corresponding to the asset class "construction and mining machinery", and therefore a description thereof will be omitted.

[0127] FIG. 10 is a diagram showing an example of generation of the price function data 9 according to the third embodiment.

[0128] In Fig. 10, data relating elapsed time and residual value rate for each of a plurality of asset classes is used as the original data. This original data represents the distribution of average residual value rates for each elapsed period for the asset classes. For example, official statistics on residual value rates for asset classes, survey results, or performance survey results may be used as the original data.

[0129] For example, based on Figure 10, the price function for the asset class "Construction and Mining Equipment" is i) Determine the price function f(x) and parameters a and b as an approximation function with elapsed time as the explanatory variable and the residual value rate as the objective variable (dependent variable). ii) The price function f(x) is determined for each asset class over a range of one year or more and less than the normal useful life. iii) If the effective time is less than one year, round it up to one year. iv) When the remaining useful life becomes zero, the scrap residual value is adjusted to zero or a preset scrap residual value rate. It may be determined as follows.

[0130] When approximating the distribution of the average residual value rate for each elapsed period to a price function, the price function can be a linear function (f(x)=ax+b), an exponential function (f(x)=a*exp(b*x)), or a logarithmic function (f(x)=a*log e (b*x)), power function (f(x)=a*x b ) may be selected using, for example, the least squares method, and a and b may be determined as the parameter values.

[0131] The price function f(x) and parameters a, b for the asset class “construction and mining machinery” determined by such means are stored in price function data 9. The price functions f(x) and parameters a, b for other asset classes are determined in a similar manner and stored in price function data 9.

[0132] The embodiments of the present invention are presented as examples and are not intended to limit the scope of the invention. The present embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. The present embodiments and their modifications are included in the scope and spirit of the invention, and are included in the scope of the invention and its equivalents described in the claims. [Explanation of symbols]

[0133] 1...information processing device, 2...operation device, 3...display device, 4...storage device, 5...processing device, 6...program, 7...input data, A1 to An...remaining durability data, 8...normal durability data, 9...price function data, 10...evaluation result data, 11...input control unit, 12...data generation unit, 13...evaluation unit, 14...output control unit

Claims

1. A processing device; A storage device for storing data used by the processing device; Equipped with The storage device includes: First data relating elapsed time, a condition level, and a remaining useful life; Second data including a normal service life; and Third data including a function for calculating a residual value rate based on an effective time; Remember, The processing device includes: receiving an evaluation time of the evaluation object, a condition level of the evaluation object, a manufacturing time or a start time of use of the evaluation object, and a new price of the evaluation object; Calculating an elapsed time of the evaluation object from the evaluation time and the manufacturing time or the use start time; determining a remaining useful life of the evaluation object based on the first data stored in the storage device, the state level of the evaluation object, and the elapsed time of the evaluation object, and determining a normal useful life of the evaluation object based on the second data stored in the storage device; Calculating the effective life of the evaluation object by subtracting the remaining useful life of the evaluation object from the normal useful life of the evaluation object; Calculating a residual value rate of the evaluation object based on the function included in the third data stored in the storage device and the effective time of the evaluation object; Calculating the appraisal value of the object by multiplying the new price of the object by the residual value rate of the object; Information processing device.

2. the state level is any one of a first level, a second level which is less deteriorated than the first level, and a third level which is more deteriorated than the first level, the change in the first level of remaining useful life corresponds to a first lifespan function based on an aggregated survival rate for the evaluation subjects; a change in the remaining useful life of the second level corresponds to a second life function obtained by adjusting the first life function so that deterioration of the evaluation object does not progress beyond the survival rate; a change in the remaining useful life of the third level corresponds to a third life function obtained by adjusting the first life function so that deterioration of the evaluation object proceeds faster than the survival rate; The second lifetime function is obtained by adjusting the first lifetime function so that an average value of the second lifetime function is a value obtained by adding an average value of the first lifetime function and a standard deviation of the first lifetime function; The third life function is obtained by adjusting the first life function so that an average value of the third life function is a value obtained by subtracting a standard deviation of the first life function from an average value of the first life function.

2. The information processing device of claim 1.

3. 3. The information processing device of claim 2, wherein the second lifetime function and the third lifetime function are obtained by fixing a shape parameter and a position parameter included in a cumulative failure rate function on which the first lifetime function is based, and changing a scale parameter.

4. 2. The information processing device of claim 1, wherein the function included in the third data is generated by approximating the relationship between the elapsed time and the residual value rate aggregated for the evaluation object to one of a linear function, an exponential function, a logarithmic function, and a power function.

5. On the computer, A function of receiving an evaluation time of an evaluation object, a condition level of the evaluation object, a manufacturing time or a start time of use of the evaluation object, and a new price of the evaluation object; A function of calculating an elapsed time of the evaluation object from the evaluation time and the manufacturing time or the use start time; a function of calculating a remaining useful life of the evaluation object based on first data stored in a storage device, which correlates elapsed time, a status level, and a remaining useful life, the status level of the evaluation object, and the elapsed time of the evaluation object, and calculating a normal useful life of the evaluation object based on second data stored in the storage device and including a normal useful life; A function of calculating an effective time of the evaluation object by subtracting a remaining useful time of the evaluation object from a normal useful time of the evaluation object; third data stored in the storage device and including a function for calculating a residual value rate based on an effective time; and a function for calculating a residual value rate of the evaluation object based on the effective time of the evaluation object; A function of multiplying the new price of the object to be evaluated by the residual value rate of the object to be evaluated to calculate the evaluated amount of the object to be evaluated; A program to achieve this.

6. receiving, by a processing device, an evaluation time of the evaluation object, a condition level of the evaluation object, a manufacturing time or a start time of use of the evaluation object, and a new product price of the evaluation object; calculating an elapsed time of the evaluation object from the evaluation time and the manufacturing time or the use start time by the processing device; calculating a remaining useful life of the evaluation object based on first data stored in a storage device and correlating elapsed time, a status level, and a remaining useful life, the status level of the evaluation object, and the elapsed time of the evaluation object, and calculating a normal useful life of the evaluation object based on second data stored in the storage device and including a normal useful life; calculating an effective time of the evaluation object by subtracting a remaining useful time of the evaluation object from a normal useful time of the evaluation object by the processing device; Calculating, by the processing device, a residual value rate of the evaluation object based on third data stored in the storage device and including a function for calculating a residual value rate based on an effective time, and the effective time of the evaluation object; calculating an appraised value of the object by multiplying the new price of the object by a residual value rate of the object by the processing device; The evaluation method comprises:

Images