Inspection plan creation assistance device

Abstract

The present invention: calculates a standard value from a cumulative component replacement count and an operation index; calculates a load factor on the basis of the ratio between a standard value and a value calculated for each customer category from the operation index used for calculating the standard value with the cumulative component replacement count; estimates a future operation time for each machine from an operation history; calculates a maintenance history and a maintenance timing for a component for each customer category from the ratio; estimates a maintenance time at which the calculated component maintenance timing is reached from the future operation time and the component maintenance timing; and creates an inspection time on the basis of the estimated maintenance time. Due to this configuration, it is possible to automatically and more accurately create a machine inspection plan which has good inspection efficiency and is created at an appropriate timing considering the effects of a load due to how a customer uses the machine.

Description

Inspection plan creation support device

[0001] The present invention relates to an inspection plan creation support device.

[0002] A known prior art technique for diagnosing and developing maintenance plans for various types of equipment, such as factory production equipment, social infrastructure equipment, incidental equipment for buildings, transportation equipment, and medical equipment, is described in, for example, Patent Document 1.

[0003] Patent Document 1 discloses a composite diagnosis and maintenance plan support system that includes target equipment, a sensor that measures diagnostic basis data, a storage unit that accumulates the maintenance history and sensor measurement data of the target equipment and the sensor, and a diagnosis and maintenance plan support device, and the diagnosis and maintenance plan support device includes: a life model estimation unit that estimates a life model with desired maintenance history and sensor data as variable parameters; a risk diagnosis unit that calculates failure risks and predicted failure risk values ​​of the target equipment and the sensor using the life model; a composite risk calculation unit that calculates composite risks and predicted composite risk values ​​associated with abnormalities in the target equipment and the sensor from the failure risks and predicted failure risk values; and a processing unit that compares at least the predicted failure risk value and the predicted composite risk value with thresholds and formulates a maintenance plan.

[0004] JP 2009-251822 A

[0005] In the above-described conventional technology, a plan is made to carry out inspections at a time when the risk of failure predicted by a lifespan model using the operating time and the number of starts and stops exceeds a threshold.

[0006] However, the above-mentioned conventional technology has the following problems. For example, there is a concern that the accuracy of the prediction results may deteriorate if there are large variations in the way the machine is used or the environment in which it is used. Furthermore, if the prediction accuracy is low, the inspection plan may be predicted to deviate from the actual usage of the machine, which may result in a lack of persuasiveness and may cause problems such as time being required to coordinate the inspection schedule with the customer. Furthermore, since no consideration is given to the need to inspect multiple machines that are geographically separated, the efficiency of the inspection may decrease.

[0007] The present invention has been made in consideration of the above, and aims to provide an inspection plan creation support device that can automatically and more accurately create inspection plans for machines that are timed appropriately and that are efficient in inspection, taking into account the load caused by how the customer uses the machine.

[0008] The present application includes a plurality of means for solving the above-mentioned problems, and one example thereof is an inspection plan creation support device for determining the inspection timing of a machine, the device comprising: an input device for inputting machine identification information including a customer category indicating how the machine to be inspected is used; an operation history in which an operation index including time-series data indicating the operating status of the machine is recorded; and a maintenance history in which the timing of part replacement and the cumulative number of part replacements are recorded; a storage device for storing the information input by the input unit; a calculation device for calculating an inspection plan for the machine based on the information in the storage device; and an output device for outputting the calculation result of the calculation device to a terminal device, The calculation device has a maintenance standard data calculation unit that calculates a standard value from the cumulative number of part replacements and an operation index, and calculates a load factor based on the ratio between the standard value and a value calculated for each customer category from the cumulative number of part replacements and the operation index used to calculate the standard value; and an inspection plan creation unit that estimates future operation time for each machine from the operation history, calculates part maintenance timing for each customer category from the maintenance history and the ratio, estimates the maintenance time that will arrive at the calculated part maintenance timing from the future operation time and the part maintenance timing, and creates an inspection timing based on the estimated maintenance timing.

[0009] According to the present invention, it is possible to automatically and more accurately create an inspection plan for a machine that is timed appropriately and that is efficient in inspection, taking into account the load caused by how the customer uses the machine.

[0010] 1 is a functional block diagram showing an example of the configuration of an inspection plan creation support device. FIG. 2 is a flowchart showing the processing content of the inspection plan creation support device. FIG. 3 is a diagram showing an example of the data structure of operation history. FIG. 4 is a diagram showing an example of the data structure of operation history after a new index has been generated. FIG. 5 is a diagram showing an example of the data structure of machine identification information. FIG. 6 is a diagram showing an example of the data structure of maintenance history. FIG. 7 is a diagram showing an example of the data structure of a maintenance standard data creation table. FIG. 8 is a diagram showing an example of the data structure of maintenance information. FIG. 9 is a diagram showing an example of the data structure of load data. FIG. 10 is a flowchart showing the processing content of an inspection plan creation subroutine. FIG. 11 is a diagram showing an example of a date estimation result. FIG. 12 is a diagram showing an example of the data structure of a maintenance plan. FIG. 13 is a diagram showing an example of a method for creating an inspection plan. FIG. 14 is a diagram showing an example of the data structure of an inspection plan. FIG. 15 is a diagram showing an example of an inspection plan screen. FIG. 16 is a diagram showing an example of the data structure of used car information. FIG. 17 is a diagram showing an example of an inspection timing adjustment screen. FIG. 18 is a diagram showing an example of the data structure of a construction plan.

[0011] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In this embodiment, a work machine such as a hydraulic excavator will be referred to as a "machine" and described as a subject for which an inspection plan is created, but the present invention is not limited to this and can also be applied to other inspection subjects such as other work machines, vehicles, and power supply equipment.

[0012] FIG. 1 is a functional block diagram showing an example of the configuration of an inspection plan creation support device according to this embodiment.

[0013] In FIG. 1, an inspection plan creation support device 1 is connected via a network 102 to a user terminal 103 (terminal device) used by a user and a database 104 in which data is stored.

[0014] The user terminal 103 is an information processing device such as a PC (Personal Computer). A user issues a processing execution instruction to the inspection plan creation support device 1 through the user terminal 103. The user terminal 103 also has a function of displaying information output by the inspection plan creation support device 1 on a screen or the like and presenting it to the user.

[0015] The database 104 is, for example, a map system, a machine operation information system, an ERP (Enterprise Resources Planning) system, or a database or storage device that accumulates data equivalent to these.

[0016] The network 102 communicatively connects the user terminal 103, the database 104, and the inspection plan creation support device 1. The network 102 is, for example, any of a local area network (LAN), a wide area network (WAN), a virtual private network (VPN), and a communication network that uses a general public line such as the Internet as part or all of the network.

[0017] The inspection plan creation support device 1 is an information processing device such as a PC or a server computer, and includes a storage device 11, a calculation device 12, an input device 13 (input interface), and an output device 14 (output interface).

[0018] The storage device 11 stores various types of information, such as maintenance history 110, operation history 111, machine identification information 112, maintenance information 113 (maintenance history), load data 114, maintenance plan 115, inspection plan 116, used car information 117, and construction plan 118.

[0019] The calculation device 12 includes a maintenance standard data calculation unit 121 , a maintenance timing calculation unit 122 , an operating time prediction unit 123 , a maintenance time prediction unit 124 , a replacement time calculation unit 125 , and an inspection plan creation unit 126 .

[0020] Here, the flow of processing executed by the arithmetic unit 12 of the inspection plan creation support device 1 will be described.

[0021] FIG. 2 is a flowchart showing the processing contents of the inspection plan creation support device.

[0022] 2 is started, for example, in response to a start command from a user at the user terminal 103, on the assumption that a predetermined number of maintenance histories 110, operation histories 111, machine identification information 112, used car information 117, and construction plans 118 are recorded in the database 104. Note that steps S100 to S200 in Fig. 2 correspond to the processing related to the maintenance standard data calculation unit 121, and step S210 corresponds to the processing of calling the maintenance timing calculation unit 122, operation time prediction unit 123, maintenance time prediction unit 124, replacement time calculation unit 125, and inspection plan creation unit 126 as subroutines.

[0023] In FIG. 2, the computing device 12 first acquires the operation history 111 (step S100).

[0024] FIG. 3 is a diagram illustrating an example of a data structure of the operation history.

[0025] As shown in FIG. 3 , the operation history 111 stores time-series data on the operating status of the machine, and is composed of the machine number, date, and operation indicators "hour meter, running time, ...." The machine number indicates number information that uniquely identifies the machine. The operation indicators include the hour meter, which is the cumulative engine operating time, and the running time, which is the cumulative running time of the machine, and indicate the operating status of the machine at the time of the "date" column. For example, an hour meter reading of 6.4 on 2022 / 2 / 11 means that the machine has been running for a total of 6.4 hours since it was purchased new.

[0026] Next, one record of each column of the operation indicators in the operation history 111 is read (step S110), and a new indicator is generated by combining the products or ratios of the read indicators (step S120). In step S120, for example, a new indicator "hour meter divided by running time" is generated, which is a combination of the ratio of the hour meter and running time.

[0027] 4 is a diagram showing an example of the data structure of the operation history after the new index is generated. In FIG. 4, the value of "hour meter divided by running time" on February 10, 2020 is N / A because the running time in the denominator is 0 (zero).

[0028] Next, it is determined whether the processing of steps S110 to S120 has been repeated for all records, i.e., whether steps S110 to S120 have been processed for all records (step S130). If the determination result is NO, the processing of steps S110 to S130 is repeated until the determination result becomes YES.

[0029] If the determination result in step S130 is YES, the machine identification information 112 and the maintenance history 110 are subsequently read (step S140).

[0030] Fig. 5 is a diagram showing an example of the data structure of machine identification information, and Fig. 6 is a diagram showing an example of the data structure of maintenance history.

[0031] As shown in Figure 5, the machine identification information 112 stores profile information for each machine, which consists of the machine number, customer, customer category, location, and new machine purchase date. "Customer" is the customer name, customer category is information about how to use the machine, indicating what kind of work the customer will use the machine for, "location" is the coordinates (e.g., latitude and longitude) of the location where the machine is located, and "purchase date" indicates the date the machine was purchased. For example, machine number "001" means that it was purchased by "Company X" on February 10, 2020, that the machine's usage is "civil engineering," and that it is currently located at "x1, y1."

[0032] As shown in FIG. 6, the maintenance history 110 stores parts and prices of past maintenance targets, and is composed of the machine number, date, part, and price.

[0033] Next, the operation history 111, the machine identification information 112, and the maintenance history 110 are combined to generate a maintenance standard data creation table (step S150).

[0034] FIG. 7 is a diagram showing an example of the data structure of the maintenance standard data creation table.

[0035] 7, the maintenance standard data creation table records part replacement history by part, machine number, and number of times, and is composed of the part, machine number, customer category, number of times, and operation index. Note that "number of times" means the cumulative number of parts replacements.

[0036] Next, in order to calculate the standard failure period "Ti,j" when the target part is "i" and the operation index is "j", a simple regression model is created in which the number of times in the maintenance standard data creation table is the objective variable "Ci" and the explanatory variable of the operation index is "xj" (step S160).

[0037] Since there are multiple types of operation indicators (hour meter, running time, etc.), a number of operation indicators are created, such as a simple regression model with the hour meter as an explanatory variable and a simple regression model with the running time as an explanatory variable.

[0038] Next, the optimum model is selected from the candidates created in step S160, and the standard value of the failure period is calculated using the selected model, and output to the maintenance information (step S170).

[0039] That is, in step S170, first, when the regression coefficients of the regression model are calculated using a statistical method for the variables of the simple regression model, a simple regression model is selected that maximizes the absolute value of the "t value" of the t-test statistic, and the operation index and regression coefficient "Ti,j" (standard value of the failure period) corresponding to the explanatory variable "xj" of that model are calculated (step S170). Here, the regression coefficient "Ti,j" can be calculated using the following (Equation 1).

[0040]

[0041] In the above (Equation 1), the subscript "n" corresponds to each record in the maintenance standard data creation table.

[0042] The calculated standard value of the failure period and the operation index are also recorded in the maintenance information 113 .

[0043] FIG. 8 is a diagram illustrating an example of a data structure of the maintenance information.

[0044] In FIG. 8, for example, the standard value of the failure period for "part 1" in the maintenance information 113 is "300 hours," which means that an "hour meter" is used as the operation index.

[0045] Next, assuming that the customer category is "k", in order to calculate the failure period "Ti,k" for the target part "i" for each k, a simple regression model is created for each customer category, with the number of times in the maintenance standard data creation table as the objective variable "Ci,k" and the operation index selected in step S8 as the explanatory variable "x" (step S180).

[0046] Since there are multiple types of customer categories, such as "civil engineering, demolition, ...", a simple regression model for civil engineering, a simple regression model for demolition, and so on, are created for each customer category. Here, the regression coefficient "Ti,k" can be calculated using the following (Equation 2).

[0047]

[0048] In step S180, the load factor is calculated as the ratio of equation 1 to equation 2, "Ti,j / Ti,k", and stored in the load data 114.

[0049] FIG. 9 is a diagram illustrating an example of the data structure of the load data.

[0050] 9, the load data 114 is calculated for each customer category and each part. For example, in the first record, the load factor of "Part 1" in the customer category "civil engineering" is "0.8", which means that the failure period is 1.25 (= 1 / 0.8) times longer than the standard failure period.

[0051] Next, it is determined whether the processing of steps S160 to S190 has been repeated the number of times corresponding to the number of component types, i.e., whether steps S160 to S190 have been processed for all component types (step S200). If the determination result is NO, the processing of steps S160 to S190 is repeated until the determination result becomes YES.

[0052] If the determination result in step S200 is YES, a subroutine for creating an inspection plan is subsequently called (step S210).

[0053] FIG. 10 is a flowchart showing the processing contents of the inspection plan creation subroutine.

[0054] 10 , in the inspection plan creation subroutine (step S210 in FIG. 7 ), first, the operation history 111 is read (step S211), and then, using the date (current date) in the record corresponding to the latest date in the operation history 111 and the value of the operation index, the date on which the operation index will reach a predetermined value is estimated for each unit (step S212). The date estimation (calculation) in step S212 can be performed, for example, using the following (Equation 3).

[0055]

[0056] FIG. 11 is a diagram showing an example of a date estimation result.

[0057] 11 shows the result of estimating the date when the hour meter of unit "001" will reach "3,000 hours" from "2,800 hours" at the current time "2022 / 2 / 1", which is "2,800 hours", as "2,800 hours", and the result is "2022 / 3 / 25". By estimating the date in this way, the date when the operation index will reach a predetermined value can be obtained for each unit.

[0058] Next, the maintenance timing for the part is calculated (step S213).

[0059] In step S213, first, to calculate the timing of maintenance for the parts, parts "part 1, part 2, ..." corresponding to the relevant machine number "001" are extracted from the maintenance information 113, along with the load data 114 corresponding to the customer category "civil engineering" (machine identification information) of the relevant machine. The extracted part is defined as "i," the customer category as "k," and the load factor corresponding to part "i" and customer category "k" as "load factor i, k." The standard value "Ti" of the failure period corresponding to part "i" is extracted from the "period" column of the maintenance information 113. For example, for part 1, "Ti" = "300 h." By substituting the extracted Ti "300 h" and load factor i "0.8" into the following (Equation 4), the failure period Ti,k "375 h" for "part 1" in "civil engineering" can be calculated.

[0060]

[0061] Next, the next maintenance timing Mi,k for part 1, "3,000 hours," is calculated using the calculated failure period Ti,k "375 hours" and the current hour meter reading "2,800 hours" according to the following (Equation 5).

[0062]

[0063] Next, the maintenance timing of the part is calculated (step S214).

[0064] That is, in step S214, the maintenance time when the machine's operation index value reaches the maintenance timing Mi,k "3,000 h" is calculated using the above (Equation 3).

[0065] 11, the maintenance time when the maintenance timing Mi,k reaches "3,000 h" is "3 / 25 / 2022." The calculated maintenance time is recorded in the maintenance plan 115.

[0066] FIG. 12 is a diagram illustrating an example of a data structure of a maintenance plan.

[0067] 12, the maintenance plan 115 is composed of the machine number, customer category, load factor, part, maintenance time, and area. Note that the "area" column stores the execution results of the subsequent process (step S217), so "-" is registered at this point.

[0068] Next, it is determined whether the processing of steps S213 to S214 has been repeated the number of times corresponding to the number of component types, i.e., whether steps S213 to S214 have been processed for all component types (step S215). If the determination result is NO, the processing of steps S213 to S215 is repeated until the determination result becomes YES.

[0069] If the determination result in step 215 is YES, it is next determined whether or not the processing of steps S211 to S215 has been repeated for the number of machines, i.e., whether or not steps S211 to S215 have been processed for all machines (step S216).If the determination result is NO, the processing of steps S211 to S216 is repeated until the determination result becomes YES.

[0070] In this way, by repeating the processes from step S211 to step S216 for each number of machines and each type of part, the maintenance timing for each part in all machines can be obtained.

[0071] If the determination result in step S216 is YES, an area is subsequently generated by clustering machines whose location data are geographically close to each other (step S217).

[0072] In step S217, a clustering method using, for example, k-means is used to group geographically close machines into areas. Here, the machine position is "A", the machine group is "G", the number of groups is "q", and the center coordinate of the group is "u". For example, as initial values, the machine position A is set to the "position" column of the machine identification information 112, and the number of groups q is set to "1 + log ２ The center coordinate u of the group is set by randomly selecting q machines from the "position" column of the machine identification information 112. Next, the machine is assigned to the group with the center coordinate u that is closest to the machine's location, and the center coordinate u of the solution that minimizes the objective function of Equation 6 is obtained. By reassigning the machine to the group with the center coordinate u that is closest to the obtained center coordinate u and repeating this process until the value of the objective function of Equation 6 below no longer changes, it is possible to group geographically close machines into optimal areas. This area information is recorded in the "area" column of the maintenance plan 115.

[0073]

[0074] Next, an inspection plan is created based on the maintenance timing calculated in step S214 and the area of ​​the machine identification information 112 (step S218).

[0075] FIG. 13 is a diagram schematically illustrating an example of an inspection plan creation method.

[0076] In FIG. 13, an example of a method for creating an inspection plan for "Area 1" is described. First, records in which "Area 1" is registered in the area column of the maintenance plan 115 are extracted and piled up by maintenance period. For example, the record for part 1 of machine number "001" in FIG. 12 is piled up as maintenance period "2022 / 3 / 25". Here, if the capacity constraint per day in "Area 1" is set to two machines, the inspection period is determined by collapsing backward from the future toward the present in order to bring forward the amount exceeding the capacity constraint. The determined result is recorded in the inspection plan 116.

[0077] FIG. 14 is a diagram illustrating an example of a data structure of an inspection plan.

[0078] 14, item group A is the column recorded in step 218, and consists of customer, machine number, customer category, part, load factor, maintenance time, area, and inspection time. The inspection and maintenance times for each machine number and part are recorded as "machine number," "part," "maintenance time," "inspection time," and "area," and the accompanying information "customer," "customer category," and "load factor" are obtained from the machine identification information 112 and load data 114 and recorded together.

[0079] FIG. 15 is a diagram showing an example of the inspection plan screen.

[0080] By selecting the display period of the inspection plan on the inspection plan screen 141 shown in FIG. 15 , the "customer," "machine number," "customer category," "load factor," "parts," "maintenance period," "maintenance timing," "location," and "area" information of the machine to be inspected is extracted from the inspection plan 116 and displayed, along with the geographical location of the machine and map information. The map information is obtained, for example, from a database 104 linked to a map system. It can be determined by checking the load factor whether the displayed maintenance period is earlier or later than standard usage. For example, if the load factor exceeds 1, it can be determined that the load is higher than standard usage and therefore the maintenance period is set earlier.

[0081] Next, the life cycle cost is calculated, and the replacement timing is extracted using the break-even point with the used car price as a key (step S219), and the process ends.

[0082] The inspection timing on the inspection plan screen 141 is a recommended value in the inspection plan creation support device 1, and the actual inspection timing requires coordination with the customer. In order to create the cost information required for this coordination with the customer, in step S219, the life cycle cost is calculated and the used car information 117 is read. Specifically, to calculate the life cycle cost, the value of the "maintenance cost" column in the inspection plan 116 is first obtained from the "price" column of the maintenance information 113 and recorded. In addition, the used car information 117 is read, and the turning point where the life cycle cost intersects with the used car price is extracted, and the time of sudden increase in costs just before the turning point is set as the time to recommend machine replacement.

[0083] FIG. 16 is a diagram illustrating an example of a data structure of used car information.

[0084] The used car information 117 shown in FIG. 16 stores price information of used cars according to the number of years that have passed since the new car was purchased.

[0085] FIG. 17 is a diagram showing an example of the inspection timing adjustment screen.

[0086] 17, by selecting a machine number from the menu, a construction plan 142A, an inspection plan 142B, a life cycle cost, and a recommended replacement time 142C for that machine number are displayed. The construction plan 142A can be edited by period (start-end) and event attributes (construction, rainy season, ...), and the results of editing are recorded in the construction plan 118.

[0087] FIG. 18 is a diagram illustrating an example of a data structure of a construction plan.

[0088] 18 includes a period, a unit number, and an event attribute. In the event attribute, matters that the customer takes into consideration when deciding on an inspection date (for example, wanting to inspect before construction or after the end of the rainy season) are registered.

[0089] The inspection plan 142B on the screen in FIG. 17 displays the data registered in the inspection plan 116. Note that the "inspection time (recommended)" on the screen corresponds to the "inspection time" column of the inspection plan 116, and the results of the coordination with the customer are registered in the "confirmed inspection time" column of the inspection plan 116. If the dates of the "inspection time (recommended)" and the "confirmed inspection time" differ, the inspection plan creation support device 1 asks the user for the reason and records it in the "reason for changing the inspection time" column. As another form of the inspection plan creation method, for example, a priority for determining the inspection time (e.g., conducting an inspection before construction) may be extracted for each customer from statistics on the adoption rate of the recorded "reason for changing the inspection time" and "inspection time (recommended)," and an inspection plan that satisfies that priority may be created.

[0090] Furthermore, the life cycle cost and recommended replacement time 142C displays the estimated future life cycle cost of the relevant machine, the market price of used cars, and the recommended replacement time. Since replacement of expensive parts occurs in the latter half of the life cycle, it is possible to propose the replacement time to customers who wish to replace their machines.

[0091] In this embodiment configured as described above, the inspection plan creation support device for determining the inspection timing of a machine comprises an input device for inputting machine identification information including a customer category indicating how the machine to be inspected is used, an operation history in which an operation index including time-series data indicating the operating status of the machine is recorded, and a maintenance history in which the timing of part replacement and the cumulative number of part replacements are recorded, a storage device for storing the information input by the input device, a calculation device for calculating an inspection plan for the machine based on the information in the storage device, and an output device for outputting the calculation results of the calculation device to a terminal device, and the calculation device calculates a standard value from the cumulative number of part replacements and the operation index, and outputs the cumulative number of part replacements and the calculation of the standard value to a terminal device. The system is configured to have a maintenance standard data calculation unit that calculates a load factor based on the ratio between a value calculated for each customer category from the operation index used in the calculation and a standard value, and an inspection plan creation unit that estimates future operation times for each machine from the operation history, calculates part maintenance timing for each customer category from the maintenance history and the ratio, estimates the maintenance time when the calculated part maintenance timing will arrive from the future operation time and the part maintenance timing, and creates an inspection time based on the estimated maintenance time.This makes it possible to automatically and more accurately create inspection plans for machines that are appropriately timed taking into account the impact of load on the machines due to how the customers use them and that have good inspection efficiency.

[0092] <Notes> The present invention is not limited to the above-described embodiments, and includes various modifications and combinations within the scope of the gist thereof. Furthermore, the present invention is not limited to those including all of the configurations described in the above-described embodiments, and also includes those in which some of the configurations are omitted. Furthermore, the above-described configurations, functions, etc. may be realized in part or in whole by designing them as, for example, integrated circuits. Furthermore, the above-described configurations, functions, etc. may be realized in software by a processor interpreting and executing a program that realizes each function.

[0093] 1...inspection plan creation support device, 11...storage device, 12...arithmetic device, 13...input device (input interface), 14...output device (output interface), 102...network, 103...user terminal, 104...database, 110...maintenance history, 111...operation history, 112...machine identification information, 113...maintenance information, 114...load data, 115...maintenance plan, 116...inspection plan, 117...used car information, 118...construction plan, 121...maintenance standard data calculation unit, 122...maintenance timing calculation unit, 123...operation time prediction unit, 124...maintenance timing prediction unit, 125...replacement timing calculation unit, 126...inspection plan creation unit, 141...inspection plan screen, 142...inspection timing adjustment screen, 142A...construction plan, 142B...inspection plan, 142C...recommended replacement timing

Claims

1. An inspection plan creation support device for determining the inspection timing of a machine, comprising: an input device for inputting machine identification information including a customer category indicating how the machine to be inspected is used, an operation history indicating an operation index including time-series data indicating the operating status of the machine, and a maintenance history indicating the time of part replacement and a cumulative number of part replacements; a storage device for storing the information input by the input device; a calculation device for calculating an inspection plan for the machine based on the information in the storage device; and an output device for outputting the calculation result of the calculation device to a terminal device, wherein the calculation device has a maintenance standard data calculation section for calculating a standard value from the cumulative number of part replacements and the operation index, and for calculating a load factor based on the ratio of a value calculated for each customer category from the cumulative number of part replacements and the operation index used to calculate the standard value to the standard value; an inspection plan creation unit that estimates future operation time for each machine from the operation history, calculates part maintenance timing for each customer category from the maintenance history and the ratio, estimates a maintenance time when the calculated part maintenance timing will arrive from the future operation time and the part maintenance timing, and creates an inspection timing based on the estimated maintenance timing.

2. An inspection plan creation support device as described in claim 1, wherein the inspection plan creation unit estimates future operation time for each machine from the operation history, calculates part maintenance timing for each customer category from the maintenance history and the ratio, estimates the maintenance time when the calculated part maintenance timing will arrive from the future operation time and the part maintenance timing, and creates inspection timing based on the estimated maintenance time and area information labeled by geographical proximity from the location data of the machine identification information.

3. An inspection plan creation support device as claimed in claim 1, wherein said maintenance standard data calculation unit calculates a standard value of the failure period from the regression equation with the largest coefficient of determination among a plurality of regression equations created from combinations of said cumulative number of parts replacement and one or more operation indices, and calculates a load factor from the ratio of said standard value to the failure period calculated for each customer category from the cumulative number of parts replacement and the operation indices used to calculate the standard value.

4. An inspection plan creation support device as described in claim 1, wherein the maintenance standard data calculation unit calculates a standard value of the failure period using an average value when all of the determination coefficients of multiple regression equations created from combinations of the cumulative part replacement count and one or more operation indexes are less than a specified value.

5. An inspection plan creation support device according to claim 1, characterized in that the storage device has used car information in which the used car prices for each age of the machine are recorded, and the calculation device has a replacement timing calculation unit that detects a point at which the life cycle cost suddenly decreases backwards from a branch point where the used car prices for each age intersect with the prices in the maintenance history and the life cycle cost obtained by accumulating the estimated maintenance costs for each inspection period, and sets the point as a recommended value for the replacement period.

6. An inspection plan creation support device as described in claim 5, characterized in that the output device outputs at least the customer category, the inspection timing, and the replacement timing as the calculation results of the calculation device to the terminal device and displays them on the same screen of the terminal device.

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