Inspection plan creation support device

Abstract

A standard value is calculated from the cumulative number of parts to be replaced and the work index, a load rate is calculated on the basis of the ratio of a value calculated for each customer category using the cumulative number of parts to be replaced and the work index for calculating the standard value to the standard value, the future work time is estimated for each machine from the work history, and the maintenance time is estimated for each machine from the maintenance history and the ratio. The maintenance timing of the parts is calculated for each customer category, the maintenance timing reaching the calculated maintenance timing of the parts is estimated from the future working time and the maintenance timing of the parts, and the inspection timing is created on the basis of the estimated maintenance timing. As a result, it is possible to automatically and accurately create a machine inspection plan with excellent inspection efficiency at a proper timing at which the influence of the use mode of the machine by the customer on the load is considered.

Description

Technical Field

[0001] This invention relates to a support device for creating inspection plans. Background Technology

[0002] Existing technologies for the diagnosis and maintenance planning of various equipment, such as production equipment in factories, social infrastructure equipment, building accessories, transportation equipment, and medical equipment, include, for example, the technology described in known patent document 1.

[0003] Patent Document 1 discloses a composite diagnostic and maintenance plan support system, which includes a target device, a sensor for measuring diagnostic data, a storage unit for accumulating maintenance history of the target device and the sensor, and sensor measurement data, and a diagnostic and maintenance plan support device. The diagnostic and maintenance plan support device includes a life model prediction unit, a risk diagnosis unit, a composite risk calculation unit, and a processing unit. The life model prediction unit predicts a life model using the expected maintenance history and sensor data as variable parameters. The risk diagnosis unit calculates the failure risk and failure risk prediction value of the target device and the sensor based on the life model. The composite risk calculation unit calculates the composite risk and composite risk prediction value associated with the abnormality of the target device and the sensor based on the failure risk and the failure risk prediction value. The processing unit at least compares the failure risk prediction value, the composite risk prediction value, and a threshold to formulate a maintenance plan.

[0004] Existing technical documents Patent documents Patent Document 1: Japanese Patent Application Publication No. 2009-251822 Summary of the Invention

[0005] The technical problem that the invention aims to solve In the aforementioned prior art, a plan is developed to conduct inspections during periods when the failure risk exceeds a threshold, and this failure risk is predicted by a life model that uses operating time and the number of start-stop cycles.

[0006] However, the aforementioned existing technology has the following problems. For example, there are concerns that the accuracy of the prediction results may decrease when there are significant changes in the machine's usage methods and operating environment. Furthermore, it is believed that low prediction accuracy leads to a lack of persuasiveness in the predicted inspection plan because it deviates from the actual usage of the machine, resulting in time-consuming obstacles when adjusting inspection schedules with customers. In addition, it is argued that inspection efficiency decreases because it does not take into account the geographically dispersed nature of multiple machines requiring routine inspections.

[0007] The present invention was made in view of the above circumstances, and its object is to provide an inspection plan creation support device that can automatically and more accurately create inspection plans for machinery with high inspection efficiency at appropriate times, taking into account the impact of the customer's use of the machinery on the load.

[0008] Technical solutions for solving the problem This application includes several solutions to the aforementioned problems. One example is an inspection plan creation support device that determines the inspection period for machinery. It comprises: an input device for inputting machinery identification information, work history, and maintenance history; the machinery identification information including the customer category indicating the usage of the machinery to be inspected; the work history recording work indicators containing time-series data representing the working status of the machinery; and the maintenance history recording the replacement period for parts and the cumulative number of parts replaced; a storage device for storing the information input by the input device; a calculation device for calculating the inspection plan for the machinery based on the information stored in the storage device; and an output device for outputting the calculated plan to a terminal device. The calculation results of the device include: a maintenance baseline data calculation unit, which calculates a standard value based on the cumulative number of parts replaced and the work indicators, and calculates the load rate based on the ratio of the value calculated by each customer category using the cumulative number of parts replaced and the work indicators used to calculate the standard value to the standard value; and an inspection plan creation unit, which predicts the future working time for each machine based on the work history, calculates the maintenance timing for parts for each customer category based on the maintenance history and the ratio, predicts the maintenance period for reaching the calculated maintenance timing for parts based on the future working time and the maintenance timing for parts, and creates an inspection period based on the predicted maintenance period.

[0009] Invention Effects By employing this invention, inspection plans for machinery can be automatically and with greater accuracy created at appropriate times, taking into account the impact of customer usage patterns on the load. Attached Figure Description

[0010] Figure 1 This is a functional block diagram representing a structural example of an inspection plan that creates support devices.

[0011] Figure 2 This is a flowchart representing the processing content of the inspection plan to create support devices.

[0012] Figure 3 This is a diagram representing an example of a data structure for a work resume.

[0013] Figure 4 This is a diagram illustrating an example of the data structure of a work history after new metrics have been generated.

[0014] Figure 5 This is a diagram illustrating an example of a data structure for representing machine identification information.

[0015] Figure 6 This is a diagram representing an example of a data structure for maintaining a resume.

[0016] Figure 7 This is a diagram illustrating an example of the data structure used to create tables for maintaining baseline data.

[0017] Figure 8 This is a diagram representing an example of a data structure for maintaining information.

[0018] Figure 9 This is a diagram illustrating an example of a data structure for representing load data.

[0019] Figure 10 This is a flowchart representing the processing content of the inspection plan creation subroutine.

[0020] Figure 11 This is an example of a graph representing a predicted date.

[0021] Figure 12 This is a diagram illustrating an example of the data structure for a maintenance plan.

[0022] Figure 13 This is a diagram illustrating an example of a method for creating an inspection plan.

[0023] Figure 14 This is a diagram illustrating an example of the data structure for an inspection plan.

[0024] Figure 15 This is an example of a screen showing a checklist.

[0025] Figure 16 This is a diagram illustrating an example of a data structure representing used car information.

[0026] Figure 17 This is an example of an image showing adjustments made to the screen during an inspection period.

[0027] Figure 18 This is a diagram illustrating an example of a data structure representing a construction plan. Detailed Implementation

[0028] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. Furthermore, in this embodiment, for example, a working machine such as a hydraulic excavator will be referred to as "machinery" and described as the object of the inspection plan creation; however, the present invention is not limited thereto, and can also be applied to other inspection objects such as other working machines, vehicles, and power supply equipment.

[0029] Figure 1 This is a functional block diagram illustrating a structural example of the inspection plan creation support device in this embodiment.

[0030] exist Figure 1In the process, the inspection plan creates a support device 1 that connects to the user terminal 103 (terminal device) used by the user and the database 104 containing the data via network 102.

[0031] User terminal 103 is an information processing device such as a PC (Personal Computer). The user issues execution instructions to the inspection plan creation support device 1 through user terminal 103. Furthermore, user terminal 103 has the function of displaying information output by the inspection plan creation support device 1 on a screen or other surface to prompt the user.

[0032] Database 104 refers to systems such as map systems, machine work information systems, ERP (Enterprise Resources Planning), or databases or storage devices that accumulate data that conforms to such systems.

[0033] Network 102 connects user terminal 103 to database 104 and inspection plan creation support device 1 in a communicative manner. Network 102 is, for example, any of the following communication networks that partially or wholly utilize general public lines such as LAN (Local Area Network), WAN (Wide Area Network), VPN (Virtual Private Network), and the Internet.

[0034] The inspection plan creation support device 1 is an information processing device such as a PC or server computer, which has a storage device 11, a computing device 12, an input device 13 (input interface) and an output device 14 (output interface).

[0035] The storage device 11 stores various information, including maintenance history 110, work history 111, machine identification information 112, maintenance information 113 (maintenance history), load data 114, maintenance plan 115, inspection plan 116, used vehicle information 117, and construction plan 118.

[0036] The computing device 12 includes a maintenance reference data calculation unit 121, a maintenance timing calculation unit 122, a working time prediction unit 123, a maintenance period prediction unit 124, a replacement period calculation unit 125, and an inspection plan creation unit 126.

[0037] Here, the process flow of the processing performed by the computing device 12 of the inspection plan creation support device 1 is explained.

[0038] Figure 2 This is a flowchart representing the processing content of the inspection plan to create support devices.

[0039] Assuming that the database 104 contains a specified number of maintenance records 110 and work records 111, machine identification information 112, used vehicle information 117, and construction plans 118, for example, based on a start command from the user to the user terminal 103, the process begins... Figure 2 The series of processes shown. Additionally, Figure 2 Steps S100 to S200 are equivalent to the processing related to the maintenance reference data calculation unit 121, and step S210 is equivalent to the processing of calling the maintenance timing calculation unit 122, the working time prediction unit 123, the maintenance period prediction unit 124, the replacement period calculation unit 125, and the inspection plan creation unit 126 as subroutines.

[0040] exist Figure 2 In the process, the computing device 12 first obtains the work history 111 (step S100).

[0041] Figure 3 This is a diagram representing an example of a data structure for a work resume.

[0042] like Figure 3 As shown, job history 111 stores time-series data on the working status of machinery, consisting of machine number, date, and work indicators "timer, driving time, ...". The machine number represents the unique identifier of the machinery. Work indicators include the timer (accumulated engine driving time) and the driving time (accumulated machinery driving time), indicating the machinery's working status at the time specified in the "date" column. For example, a timer of 6.4 on 2022 / 2 / 11 indicates that the machine has been driven for a total of 6.4 hours since its purchase.

[0043] Next, the quantity of one record from each column of the work indicators in the work resume 111 is read in (step S110), and a new indicator is generated by combining the products and ratios between the read indicators (step S120). In step S120, as a new indicator, for example, "timer ÷ travel time" is generated, which is a combination of the ratio of timer to travel time.

[0044] Figure 4 This is a diagram illustrating an example of the data structure of a work history after the generation of new metrics. Additionally, in Figure 4 In the example, regarding the value of "timer ÷ travel time" at 2020 / 2 / 10, since the travel time in the denominator is 0 (zero), the output is N / A.

[0045] Next, it is determined whether the processing of steps S110 to S120 has been repeated for all records, that is, whether the processing of steps S110 to S120 (step S130) has been completed for all records. If the determination result is "no", the processing of steps S110 to S130 is repeated until the determination result becomes "yes".

[0046] In addition, if the determination result in step S130 is "yes", then the machine identification information 112 and maintenance history 110 are read in (step S140).

[0047] Figure 5 This is a diagram illustrating an example of a data structure for representing machine identification information. Additionally, Figure 6 This is a diagram representing an example of a data structure for maintaining a resume.

[0048] like Figure 5 As shown, the machine identification information 112 stores the file information for each machine, consisting of the machine number, customer, customer category, location, and purchase date. "Customer" indicates the customer name, "Customer Category" indicates information related to the machine's usage, showing the type of work the customer uses the machine for, "Location" indicates the coordinates (e.g., latitude and longitude) of the location where the machine is placed, and "Purchase Date" indicates the date the machine was purchased. For example, machine number "001" means that "Company X" purchased it on February 10, 2020, the machine's usage is "civil engineering," and it is currently configured at "x1, y1."

[0049] like Figure 6 As shown, the maintenance history 110 stores the parts and prices of past maintenance objects, consisting of the machine number, date, part, and price.

[0050] Next, by combining the work history 111, machine identification information 112, and maintenance history 110, a table for creating maintenance baseline data is generated (step S150).

[0051] Figure 7 This is a diagram illustrating an example of the data structure used to create tables for maintaining baseline data.

[0052] like Figure 7 As shown, the table for creating maintenance baseline data records part replacement history by part, machine number, and number of replacements, consisting of part, machine number, customer category, number of replacements, and work indicators. Additionally, "number of replacements" refers to the cumulative number of part replacements.

[0053] Next, assuming the part to be considered is "i" and the working index is "j", in order to calculate the standard failure cycle "T" i，j Create a simple linear regression model with the number of times the table for maintaining baseline data is created as the objective variable "Ci" and the explanatory variable "xj" as the work indicator (step S160).

[0054] Since there are various work indicators such as "timer, travel time, etc.", the number of work indicators can be created, for example, in a simple linear regression model with the timer as the explanatory variable or a simple linear regression model with the travel time as the explanatory variable.

[0055] Next, the best model is selected from the candidates created in step S160, and the standard value of the failure cycle is calculated using the selected model and output to the maintenance information (step S170).

[0056] That is, in step S170, when using statistical methods to obtain the regression coefficients of the simple linear regression model for the variables, the simple linear regression model with the largest absolute value of the "t-value" of the t-test statistic is selected, and the working index and regression coefficient "T" corresponding to the explanatory variable "xj" of the model are calculated. i，j (Standard value of the failure cycle) (Step S170). Here, the regression coefficient "T" can be calculated using the following (Equation 1). i，j ".

[0057] (Mathematical Formula 1) ... (Equation 1) Additionally, in Equation 1 above, the superscript "n" corresponds to each record in the table used to create the baseline data.

[0058] In addition, the calculated standard value of the fault cycle and the working indicators are recorded in the maintenance information 113.

[0059] Figure 8 This is a diagram representing an example of a data structure for maintaining information.

[0060] exist Figure 8 In the maintenance information 113, for example, the standard value of the fault cycle in “part 1” is “300h”, which means that the “timer” is used as a working indicator.

[0061] Next, with the customer category set to "k", in order to calculate the failure cycle "T" of the part "i" set as the object for each k, i，k The number of times a table is created to maintain baseline data for each customer category is set as the target variable "C". i，k The simple linear regression model is obtained by setting the work index selected in step S8 as the explanatory variable "x" (step S180).

[0062] Since there are multiple customer categories such as "civil engineering, demolition, etc.", we can create a number of customer categories, for example, by using a simple linear regression model with civil engineering as the object and a simple linear regression model with demolition as the object. Here, the regression coefficient "T" can be calculated using the following (Equation 2). i，k ".

[0063] (Mathematical Formula 2) ... (Equation 2) Additionally, in step S180, the ratio "T" of Equation 1 to Equation 2 is used. i，j ÷T i，k Calculate the load rate and store it in load data 114.

[0064] Figure 9 This is a diagram illustrating an example of a data structure for representing load data.

[0065] like Figure 9 As shown, load data 114 is calculated for each customer category and each part. For example, in the first record, the load rate "0.8" for "Part 1" in the customer category "Civil Engineering" means that it is 1.25 times the standard failure cycle (=1÷0.8).

[0066] Next, it is determined whether the processing of steps S160 to S190 has been repeated in terms of the number of part types, that is, whether the processing of steps S160 to S190 has been completed for all part types (step S200). If the determination result is "no", the processing of steps S160 to S190 is repeated until the determination result becomes "yes".

[0067] Additionally, if the determination result in step 200 is "yes", a subroutine is then called to create an inspection plan (step S210).

[0068] Figure 10 This is a flowchart representing the processing content of the inspection plan creation subroutine.

[0069] exist Figure 10 In the process of creating a subroutine for the inspection plan ( Figure 7 In step S210), the work history 111 is first read in (step S211). Then, using the date (current date) in the record corresponding to the latest date of the work history 111 and the value of the work indicator, the date when the work indicator reaches the specified value is estimated by the machine (step S212). For example, the date in step S212 can be estimated (calculated) using the following (Equation 3).

[0070] (Mathematical Formula 3) ... (Formula) 3 Figure 11 This is an example of a graph representing a predicted date.

[0071] exist Figure 11 In the text, "2022 / 02 / 1" indicates the date "2800h" of the timer in machine "001" will reach "3000h", resulting in "2022 / 03 / 25". By predicting the date in this way, the date when each machine's work targets reach the specified values ​​can be determined.

[0072] Next, the maintenance timing for the part is calculated (step S213).

[0073] In step S213, firstly, in order to calculate the maintenance timing of the parts, the parts "Part 1, Part 2, ..." corresponding to machine number "001" and the load data 114 corresponding to the customer category "Civil Engineering" (mechanical identification information) of the machine number are extracted from the maintenance information 113. The extracted parts are defined as "i", the customer category is defined as "k", and the load rate corresponding to part "i" and customer category "k" is defined as "load rate". i，k The standard value "Ti" of the fault cycle corresponding to part "i" is extracted from the "Cycle" column of maintenance information 113. For example, in the case of part 1, "Ti" = "300h". By substituting the extracted Ti "300h" and load rate i "0.8" into the following (Equation 4), the fault cycle T of "part 1" in "Civil Engineering" can be calculated. i，k "375h".

[0074] (Mathematical expression 4) ... (Equation 4) Next, the calculated failure period T is used. i，k The next maintenance timing M in part 1 is calculated based on "375h" and the current timer "2800h" according to the following formula (5). i，k "3000h".

[0075] (Mathematical formula 5) ... (Equation 5) Next, the maintenance period for the part is calculated (step S214).

[0076] That is, in step S214, the working index value of the machine is calculated using the above (Equation 3) to determine when maintenance is needed. i，k The maintenance period is 3000 hours.

[0077] like Figure 11 As shown, maintenance is required at time M. i，k The maintenance period for "3000h" is "2022 / 3 / 25". The calculated maintenance period is recorded in maintenance plan 115.

[0078] Figure 12 This is a diagram illustrating an example of the data structure for a maintenance plan.

[0079] like Figure 12 As shown, maintenance plan 115 consists of machine number, customer category, load rate, parts, maintenance period, and region. In addition, the "region" column stores the execution result of the subsequent processing (step S217), so "-" is recorded at that point in time.

[0080] Next, it is determined whether the processing of steps S213 to S214 has been repeated in terms of the number of part types, that is, whether the processing of steps S213 to S214 has been completed for all part types (step S215). If the determination result is "no", the processing of steps S213 to S215 is repeated until the determination result becomes "yes".

[0081] In addition, if the determination result in step 215 is "yes", then it is determined whether the processing of steps S211 to S215 has been repeated in terms of the number of machines, that is, whether the processing of steps S211 to S215 has been completed 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".

[0082] In this way, by repeatedly performing steps S211 to S216 for each machine and each part, the maintenance period for each part in all machines can be obtained.

[0083] In addition, if the determination result in step 216 is "yes", then the region is generated by mechanically grouping the location data into geographically close groups through clustering (step S217).

[0084] In step S217, to group geographically close machines by region, a k-means clustering method is used, for example. Here, the machine's location is set to "A", the machine's group to "G", the number of groups to "q", and the group's center coordinate to "u" as initial values. For example, the machine's location A is set as the "Location" column of machine identification information 112, the number of groups q is set to "1 + log2 (number of machines)", and q machines are arbitrarily selected from the "Location" column of machine identification information 112 to set the group's center coordinate u. Next, the group with the nearest center coordinate u to the machine's location is assigned, and the center coordinate u of the objective function of Equation 6 is obtained as the minimum solution. The machines are reassigned to the nearest group with the obtained center coordinate u, and this process is repeated until the value of the objective function (Equation 6) no longer changes, thereby enabling the grouping of geographically close machines according to each optimal region. This region information is recorded in the "Region" column of maintenance plan 115.

[0085] (Mathematical expression 6) ... (Formula 6) Next, based on the maintenance period calculated in step S214 and the area of ​​machine identification information 112, an inspection plan is created (step S218).

[0086] Figure 13This is a diagram illustrating an example of a method for creating an inspection plan.

[0087] exist Figure 13 The following explanation uses the creation method of the inspection plan in "Area 1" as an example. First, extract the records registered in the "Area 1" column of maintenance plan 115, and stack them according to each maintenance period. For example, Figure 12 The record for part 1 of machine number "001" is for maintenance on "2022 / 3 / 25". Here, with the daily capacity limit in "Area 1" set to 2 units, in order to bring forward any units exceeding the capacity limit and balance them in a reverse direction from the future to the present, the inspection period is determined. The decision is recorded in inspection plan 116.

[0088] Figure 14 This is a diagram illustrating an example of the data structure for an inspection plan.

[0089] exist Figure 14 In the inspection plan 116 shown, project group A is the column recorded in step 218, consisting of customer, machine number, customer category, part, load rate, maintenance period, area, and inspection period. Regarding the inspection period and maintenance period by machine number and by part, "machine number", "part", "maintenance period", "inspection period", and "area" are recorded, and "customer", "customer category", and "load rate" are obtained as supplementary information based on machine identification information 112 and load data 114 and recorded together.

[0090] Figure 15 This is an example of a screen showing a checklist.

[0091] exist Figure 15 In the inspection plan screen 141 shown, by selecting the display period of the inspection plan, the inspection plan 116 extracts and displays information on the machine to be inspected, including "customer," "machine number," "customer category," "load rate," "parts," "maintenance period," "location," and "region." It also displays the machine's geographical location and map information. The map information is obtained, for example, from a database 104 linked to a map system. By confirming the load rate, it can be determined whether the displayed maintenance period is earlier or later than the standard operating procedure. For example, if the load rate exceeds 1, the load is higher than the standard operating procedure, thus indicating that the maintenance period should be set earlier.

[0092] Next, the lifecycle cost is calculated, and the replacement period is extracted by taking the bifurcation point with the used car price as the key (step S219), and the process ends.

[0093] The inspection period on the inspection plan screen 141 is a suggested value from the inspection plan creation support device 1; the actual inspection period needs to be adjusted with the customer. To create the cost information required for this adjustment with the customer, the lifecycle cost is calculated and the used vehicle information 117 is read in step S219. Specifically, firstly, to calculate the lifecycle cost, the value of the "Maintenance Cost" column in the inspection plan 116 is obtained from the "Price" column of the maintenance information 113 and recorded. Secondly, the used vehicle information 117 is read, the bifurcation point where the lifecycle cost intersects with the used vehicle price is extracted, and the period of sharp cost increase just before reaching the bifurcation point is set as the recommended period for the machinery trade-in program.

[0094] Figure 16 This is a diagram illustrating an example of a data structure representing used car information.

[0095] exist Figure 16 The used car information 117 shown stores the price information of the used car corresponding to the number of years since it was purchased as a new car.

[0096] Figure 17 This is an example of an image showing adjustments made to the screen during an inspection period.

[0097] exist Figure 17 On the inspection period adjustment screen 142 shown, select machine number from the menu to display the construction plan 142A, inspection plan 142B, life cycle cost, and recommended replacement period 142C for that machine. Construction plan 142A allows editing of the period (start-end) and event attributes (construction, rainy season, ...), and the edited results are recorded in construction plan 118.

[0098] Figure 18 This is a diagram illustrating an example of a data structure representing a construction plan.

[0099] Figure 18 The construction plan 118 shown consists of a period, a machine number, and an event attribute. The event attribute records the factors the customer considers when deciding on the inspection date (e.g., wanting to conduct the inspection before construction, after the rainy season, etc.).

[0100] Figure 17The inspection plan 142B displayed on the screen shows the data registered in the inspection plan 116. Additionally, the "Inspection Period (Recommended)" on the screen corresponds to the "Inspection Period" column of the inspection plan 116, and the adjusted results are registered in the "Confirmed Inspection Period" column of the inspection plan 116. If the dates of the "Inspection Period (Recommended)" and the "Confirmed Inspection Period" differ, the inspection plan creation support device 1 asks the user for their reasons, which are recorded in the "Reason for Inspection Period Change" column. As another form of inspection plan creation method, it can be configured as follows: Prioritize the inspection period (e.g., pre-construction inspection implementation) based on statistics of the adoption rate of the recorded "Reason for Inspection Period Change" and the "Inspection Period (Recommended)", extract this priority for each customer, and create an inspection plan to meet that priority.

[0101] Additionally, the Lifetime Cost and Recommended Replacement Period section 142C displays the projected future lifetime cost of the unit, the market price of a used vehicle, and the recommended replacement period. Since significant parts replacements occur in the latter half of the lifespan, a replacement period can be suggested to customers wishing to trade in their old unit for a new one.

[0102] In this embodiment configured as described above, the inspection plan creation support device for determining the inspection period of machinery is configured to include: an input device that inputs machinery identification information, work history, and maintenance history, wherein the machinery identification information includes a customer category indicating the usage mode of the machinery to be inspected, the work history records work indicators including time-series data indicating the working status of the machinery, and the maintenance history records the replacement period of parts and the cumulative number of parts replaced; a storage device that stores the information input by the input device; a calculation device that calculates the inspection plan of the machinery based on the information stored in the storage device; and an output device that outputs the calculation results of the calculation device to a terminal device, wherein the calculation device has: maintenance reference data calculation... The output department calculates a standard value based on the cumulative number of parts replaced and work indicators. It calculates the load rate based on the ratio of the value calculated for each customer category using the cumulative number of parts replaced and the work indicators used to calculate the standard value to the standard value. The inspection plan creation department predicts future working hours for each machine based on work history. Based on maintenance history and ratios, it calculates the maintenance timing for parts for each customer category. Based on future working hours and the maintenance timing for parts, it predicts the maintenance period when the calculated maintenance timing for parts will arrive. Based on the predicted maintenance period, it creates an inspection period. Therefore, it can automatically and more accurately create inspection plans for machines with high inspection efficiency at appropriate times, taking into account the impact of customers' use of the machines on the load.

[0103] (Note) Furthermore, the present invention is not limited to the embodiments described above, and includes various modifications and combinations without departing from its spirit. Additionally, the present invention is not limited to having all the structures described in the above embodiments, but also includes structures obtained by removing a portion of those structures. Furthermore, the aforementioned structures and functions can be implemented using, for example, integrated circuit designs, with some or all of the structures and functions described above. Moreover, regarding the aforementioned structures and functions, a processor can interpret and execute programs for implementing each function, thereby implementing the aforementioned structures and functions using software.

[0104] Explanation of reference numerals in the attached figures 1. Inspection plan creation support device; 11. Storage device; 12. Computing device; 13. Input device (input interface); 14. Output device (output interface); 102. Network; 103. User terminal; 104. Database; 110. Maintenance history; 111. Work history; 112. Machine identification information; 113. Maintenance information; 114. Load data; 115. Maintenance plan; 116. Inspection plan; 117. Used vehicle information; 118. Construction plan; 121. Maintenance baseline data calculation unit; 122. Maintenance timing calculation unit; 123. Working time prediction unit; 124. Maintenance period prediction unit; 125. Replacement period calculation unit; 126. Inspection plan creation unit; 141. Inspection plan screen; 142. Inspection period adjustment screen; 142A. Construction plan; 142B. Inspection plan; 142C. Suggested replacement period.

Claims

1. An inspection plan creation support device that determines the inspection period of machinery, characterized in that, The inspection plan creation support device includes: The input device inputs machine identification information, work history, and maintenance history. The machine identification information includes customer categories indicating the usage of the machine being inspected. The work history records work indicators containing time-series data representing the working status of the machine. The maintenance history records the replacement period of parts and the cumulative number of parts replaced. A storage device that stores information input by the input device; A computing device that calculates an inspection plan for the machine based on the information in the storage device; as well as An output device that outputs the computation results of the computing device to a terminal device. The computing device has: The maintenance baseline data calculation unit calculates a standard value based on the cumulative number of parts replaced and work indicators, and calculates the load rate based on the ratio of the value calculated for each customer category using the cumulative number of parts replaced and the work indicators used to calculate the standard value to the standard value; and The inspection planning department estimates future working hours for each machine based on the work history, calculates the maintenance timing for each part based on the maintenance history and the ratio, estimates the maintenance period for the parts based on the future working hours and the calculated maintenance timing, and creates an inspection period based on the estimated maintenance period.

2. The inspection plan creation support device according to claim 1, characterized in that, The inspection planning department estimates future working hours for each machine based on the work history. Based on the maintenance history and the ratio, the maintenance timing for parts is calculated for each customer category. Based on the future working hours and the maintenance timing of the parts, the estimated maintenance period for reaching the calculated maintenance time of the parts is predicted. Inspection periods are created based on the estimated maintenance period and the area information marked according to geographical proximity based on the location data of the machine identification information.

3. The inspection plan creation support device according to claim 1, characterized in that, The maintenance baseline data calculation unit, in the process of creating multiple regression equations based on the cumulative number of parts replaced and a combination of more than one work indicator, calculates the standard value of the failure cycle based on the regression equation with the largest coefficient of determination. The load rate is calculated based on the ratio of the failure cycle to the standard value, where the failure cycle is calculated for each customer category using the cumulative number of parts replaced and the work indicators used to calculate the standard value.

4. The inspection plan creation support device according to claim 1, characterized in that, The maintenance baseline data calculation unit calculates a standard value for the failure cycle using the average value when all the determination coefficients of multiple regressions created based on the cumulative number of parts replaced and a combination of more than one work indicator are less than a specified value.

5. The inspection plan creation support device according to claim 1, characterized in that, The storage device contains used car information, which includes the used car price of the machine according to its age. The computing device has a replacement period calculation unit, which calculates the life cycle cost based on the price of the maintenance history, the estimated value of the maintenance cost accumulated for each inspection period, and the bifurcation point of the difference between the used car prices according to the vehicle age. The replacement period calculation unit detects the point where the life cycle cost drops sharply and sets the point as the recommended value for the replacement period.

6. The inspection plan creation support device according to claim 5, characterized in that, The output device outputs at least the customer category, the inspection period, and the replacement period 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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