JP7218140B2 - Simulation device and simulation method - Google Patents

Description

The present invention relates to a technique for simulating maintenance work for equipment and facilities.

As a method for calculating the cost of maintenance work, there is a technique for calculating future equipment inspection costs. For example, in Patent Document 1, the performed inspection cost is recorded, and based on the recorded performed inspection cost, the total cost of the inspection cost in an arbitrary period for each equipment configuration item, multiple inspections in an arbitrary period We have calculated the average cost of each implementation and the total cost of all equipment components for any period of time.

In addition, there is a technique for supporting the determination of workers to be dispatched to equipment. For example, in Patent Literature 2, the arrival time to a device to be dispatched and the cost required for dispatching are calculated.

JP 2006-244006 A JP 2017-16239 A

In Patent Document 1, the cost of future maintenance work is calculated based on the cost of past implementation, but since it is not assumed that a new solution will be applied and the cost will change, the future will be about the same as the past. Effective only where costs are anticipated.
Further, in Patent Document 2, the arrival time and cost of a worker dispatched to the equipment are calculated to support the decision of the worker, but the application effect of the solution is not assumed.

When evaluating the value of a solution that a customer has introduced, the actual performance of the solution that has been introduced may vary depending on the customer's maintenance resources, changes in maintenance information such as the number of target devices, failure frequency, and failure sign detection lead time. Customer value can change. In such a case, the technology described in the above patent document only calculates the cost of maintenance work from the past work costs and personnel costs, so the performance of the introduced solution does not affect the customer's maintenance. The impact on business could not be accurately evaluated.

SUMMARY OF THE INVENTION An object of the present invention is to provide a technology capable of accurately evaluating the impact of the performance of a solution introduced by a customer on the customer's maintenance work.

Preferably, the simulation apparatus according to the present invention is based on maintenance demand data that records maintenance results of equipment to be maintained, and solution performance data that indicates the performance of a solution to be applied to the equipment. a first simulation processing unit that generates estimated maintenance demand data that estimates maintenance and signs of maintenance; and estimated maintenance of the equipment based on the estimated maintenance demand data and maintenance parameters related to costs required for maintenance of the equipment. Actual maintenance demand for the equipment based on estimated maintenance work data obtained by estimating the demand for maintenance work for the equipment in consideration of maintenance costs with respect to demand, the maintenance demand data, and the maintenance parameters. , a second simulation processing unit that generates maintenance work data calculated by calculating the demand for maintenance work of the equipment in consideration of the cost of maintenance; a maintenance cost calculation unit that calculates a maintenance cost after application of the solution and a maintenance cost before application of the solution for the maintenance work data, and calculates a difference between the two. be.

The present invention can also be grasped as a simulation method performed by the simulation apparatus.

According to one aspect of the present invention, it is possible to accurately evaluate the influence of the performance of the solution introduced by the customer on the customer's maintenance work.

Block diagram showing the configuration of the simulation device Flowchart showing the processing procedure when the simulation device is applied to the effect calculation of the predictive detection solution Diagram showing an example of maintenance demand data Diagram showing a configuration example of solution performance data Diagram showing an example of estimated maintenance demand data Diagram showing an example of maintenance task parameters Diagram showing an example of estimated maintenance work data Diagram showing an example of maintenance work data Diagram showing an example of facility information

Hereinafter, embodiments will be described in detail with reference to the drawings. However, the present invention should not be construed as being limited to the description of the embodiments shown below. Those skilled in the art will easily understand that the specific configuration can be changed without departing from the idea or gist of the present invention.

In the configuration of the invention described below, the same reference numerals may be used in common for the same parts or parts having similar functions between different drawings, and redundant description may be omitted.

The notations such as “first”, “second”, and “third” in this specification and the like are attached to identify the constituent elements, and do not necessarily limit the number or order. Also, numbers for identifying components are used for each context, and numbers used in one context do not necessarily indicate the same configuration in other contexts. Also, it does not preclude a component identified by a certain number from having the function of a component identified by another number.

The position, size, shape, range, etc. of each configuration shown in the drawings, etc. may not represent the actual position, size, shape, range, etc., in order to facilitate understanding of the invention. Therefore, the present invention is not necessarily limited to the positions, sizes, shapes, ranges, etc. disclosed in the drawings and the like.

Elements presented herein in the singular shall include the plural unless the context clearly dictates otherwise.

First, a maintenance work simulation system according to this embodiment will be described with reference to FIG. FIG. 1 is a diagram showing an example of a maintenance work simulation system 1000 in this embodiment.

A maintenance work simulation system 1000 has a simulation device 100 and a user terminal 300, which are communicably connected via a network 200 such as the Internet or a telephone line network. The simulation device 100 and the user terminal 300 are configured from a general computer as hardware, and are provided with respective units such as a CPU (Central Processing Unit), memory, external storage device, and communication device. These respective units that constitute the simulation device 100 and the user terminal 300 are electrically connected by an internal communication line such as an internal bus.

As shown in FIG. 1, the simulation device 100 includes a simulation execution management unit 101, a communication processing unit 102, a data storage unit 103, a solution simulation processing unit 104 (first simulation processing unit), and a maintenance work simulation processing unit. It includes a unit 105 (second simulation processing unit) and a maintenance cost calculation processing unit 106 (maintenance cost calculation unit). The simulation execution management unit 101, the communication processing unit 102, the solution simulation processing unit 104, the maintenance work simulation processing unit 105, and the maintenance cost calculation processing unit 106 load programs stored in an external storage device (not shown) into memory. The functions of these units are realized by executing them as The memory is composed of, for example, a data readable and writable RAM (Random Access Memory), and the above various programs are loaded by the CPU. The external storage device includes, for example, a storage medium such as a ROM (Read Only Memory), a storage device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and stores various programs necessary for the processing of the simulation apparatus 100. Remember.

The data storage unit 103 is composed of a storage device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and stores various data used in this system. Specific processing performed by each of the above units of the simulation apparatus 100 will be described later.

The various programs described above may be downloaded from the network to the external storage device via the communication processing unit 102, loaded onto the memory, and executed by the CPU. In addition, information can be directly loaded onto a memory from a portable computer-readable storage medium such as a CD (Compact Disk) or DVD (Digital Versatile Disk) via a reading device that reads and writes information from the storage medium. It may be executed by a CPU. Furthermore, the various programs described above may be provided or distributed by being recorded on the storage medium in the form of files in a computer-installable or executable format. Furthermore, the various programs described above may be stored on a computer connected to a communication network and provided or distributed by being downloaded via the network.

The user terminal 300 comprises a GUI processing section 301 and a communication processing section 302 . Similar to the simulation apparatus 100, the GUI processing unit 301 and the communication processing unit 302 are implemented by the CPU loading programs stored in an external storage device (not shown) into a memory and executing them. be. The description of each unit such as the memory, the external storage device, the communication device, etc., and the embodiment of the above program are the same as those of the simulation device 100, so the description thereof will be omitted here. Specific processing performed by each unit of the user terminal 300 will be described later.

In the simulation apparatus 100 , the simulation execution management unit 101 manages simulation execution according to instructions transmitted from the user terminal 300 via the communication processing unit 102 . More specifically, the simulation execution management unit 101 acquires necessary data from the data storage unit 103 storing various data necessary for simulation. Then, the simulation execution management unit 101 stores the newly generated data in the data storage unit 103, and provides the solution performance and maintenance data to the solution simulation processing unit 104, which simulates the effect of the solution according to the solution performance. Output demand data. Furthermore, the simulation execution management unit 101 acquires the estimated maintenance demand output from the solution simulation processing unit 104, and supplies the maintenance demand data and maintenance work parameters to the maintenance work simulation processing unit 105 that simulates maintenance work processing. to output Furthermore, the simulation execution management unit 101 acquires the maintenance work parameters output from the maintenance work simulation processing unit 105, and supplies the estimated maintenance work data to the maintenance cost calculation processing unit 106, which calculates maintenance costs from the maintenance work parameters. to output Furthermore, the simulation execution management unit 101 manages execution of processing for obtaining maintenance costs output from the maintenance cost calculation processing unit 106 .

Next, processing of the simulation device 100 will be described with reference to FIG. FIG. 2 is a diagram showing an overview of the flow of maintenance work simulation processing according to the first embodiment.

When starting the simulation process, the GUI unit 301 of the user terminal 300 reads maintenance demand data input from an input device such as a keyboard (not shown) and transmits the data to the simulation device 100 (Step 1). Maintenance demand data will be described later with reference to FIG.

Upon receiving the maintenance demand data from the user terminal 300, the simulation execution management unit 101 stores the maintenance demand data in the data storage unit (Step 2).

FIG. 3 shows an example of maintenance demand data 301. As shown in FIG. The maintenance demand data 301 is log data that chronologically records actual maintenance results for a predetermined period (for example, one month) for each piece of equipment to be maintained.

FIG. 3 shows an example in which the maintenance results of equipment A and equipment B for 30 days are recorded in chronological order. The days marked with "X" in the figure indicate the days when the failure actually occurred. In FIG. 3, for example, equipment A has a failure on the 5th, 20th, and 25th of a month, and equipment B has a failure on the 10th, 22nd, and 29th of the same month. It shows what happened. The number of facilities stored as maintenance demand data, the above-mentioned predetermined period, the degree of detail of maintenance such as date and time (for example, AM / PM classification and time of day) are determined by the solution performance described later. In addition, it is determined as necessary.

Next, the simulation execution management unit 101 acquires solution performance data from the data storage unit 103 (Step 3). The solution performance data is information stored in advance in the data storage unit 103, and is an index such as a numerical value that indicates the expected effect when the solution is applied to the maintenance results recorded in the maintenance demand data. is the information indicated by

FIG. 4 shows an example of solution performance data 401. As shown in FIG. The solution performance data 401 is data indicating the standard performance of the solution introduced by the customer. FIG. 4 is an example of performance data of a failure sign detection solution for detecting facility failures and their signs as a solution. The above failures and signs of failure are examples of equipment maintenance and signs of maintenance.

As shown in FIG. 4 , solution performance data 401 includes failure rate 4001 , detection rate 4002 , false alarm rate 4003 and maximum detection lead time 4004 .

The failure rate 4001 is a numerical value indicating the probability of equipment failure occurring during a predetermined operating period, and is 10% in this example.

The detection rate 4002 is a numerical value indicating the probability that a failure symptom occurring in the equipment can be detected by the failure symptom detection solution, and is 67% in this example.

The false alarm rate 4003 is a numerical value indicating the probability of false detection among the failure signs detected by the failure sign detection solution, and is 33% in this example.

The maximum detection lead time 4004 is an index for determining failure sign detection success when a failure occurs within the period set by the maximum detection lead time after the sign detection by the failure sign detection solution. In this example, 3 days is set, and if the failure occurs within 3 days after the sign is detected, the detection is successful.

Next, the simulation execution management unit 101 issues a solution simulation execution instruction to the solution simulation processing unit 104 (Step 4). The solution performance data 401 is attached to the execution instruction.

The solution simulation processing unit 104 reads the maintenance demand data 301 stored in Step 2 from the data storage unit 103 . Further, the solution simulation processing unit 104 inputs the solution performance data 401 and the maintenance demand data 301 according to the execution instruction, performs solution simulation processing, and generates estimated maintenance demand data as a simulation result (Step 5). Solution simulation processing will be described later.

FIG. 5 shows an example of estimated maintenance demand data 501. As shown in FIG. Estimated maintenance demand data 501 is data obtained by estimating failures and signs of failures of equipment to be maintained based on the standard performance of the failure sign detection solution introduced by the customer based on the actual failure record of the equipment to be maintained. is. FIG. 5 is a diagram showing estimated maintenance demand data generated based on solution performance indicated by solution performance data 401 of FIG. 4 for maintenance demand data 301 of FIG.

The processing of Step 5 will be described below according to the data in FIGS. 5, 4, and 3. FIG.
Actual failures during the period of data indicated by maintenance demand data 301 are 5th, 20th and 25th for facility A, and 10th, 22nd and 29th for facility B. Therefore, there were 6 failures in the predetermined period for these facilities to be maintained. Also, the detection rate 4002 stored in the solution performance data 401 is 67%. Therefore, the solution simulation processing unit 104 estimates that the sign detection has been successful for 6×0.67≈4 failures. In the example of FIG. 5, of the six failures, it is estimated that a total of four failures were successfully detected on the 20th and 25th for equipment A, and on the 10th and 29th for equipment B ( SA1, SA2, SB1, SB2). As a result, the solution simulation processing unit 104 estimates that the failure of the remaining facility A on the 5th day and the failure of the facility B on the 22nd day failed to be detected and the failure occurred as they were (FA, FB). The facility and day on which predictive sign detection is estimated to have succeeded and the facility and day on which predictive sign detection has failed are randomly selected according to the detection rate.

In addition, since the false alarm rate 4003 stored in the solution performance data 401 is 33%, the solution simulation processing unit 104 calculates the number of false alarms as 4 (4 times of successful predictor detection) x 0.33/(1- 0.33)≈2 times. In FIG. 5, the solution simulation processing unit 104 estimated that the false alarm occurred on the 7th for the facility A and on the 15th for the facility B (IA, IB). The facility and day on which the false alarm is presumed to have occurred are randomly selected according to the false alarm rate.

Furthermore, the maximum detection lead time 4004 stored in the solution performance data 401 is 3 days. Therefore, the solution simulation processing unit 104 sets the number of days of predictive detection within three days for each of the successful case and the false alarm case as predictive detection. In FIG. 5, the lead time for the false alarm on the 7th of the equipment A is estimated to be 1 day, the predictive sign detection on the 20th is estimated to be 3 days, and the predictive sign detection on the 25th is estimated to be 2 days. In addition, the lead time was estimated to be 3 days for the sign detection on the 10th, 2 days for the false alarm on the 15th, and 2 days for the sign detection on the 29th. The length of the lead time is set randomly within a range of three days, which is the maximum number of days.

Thus, the solution simulation processing unit 104 applies the solution performance data 401 shown in FIG. 4 to the maintenance demand data 301 shown in FIG. 3 to generate the estimated maintenance demand data 501 shown in FIG. .

Next, the simulation execution management unit 101 instructs the maintenance work simulation processing unit 105 to execute the estimated maintenance work simulation (Step 6).

The maintenance work simulation processing unit 105 inputs the estimated maintenance demand data 501 and the maintenance work parameters according to the estimated maintenance work simulation execution instruction, executes maintenance work simulation processing, and outputs estimated maintenance work data ( Step 7).

FIG. 6 shows an example of maintenance work parameters 601 . The maintenance work parameter 601 is data that defines information related to costs (work costs and personnel costs) required for maintenance of equipment to be maintained. As shown in FIG. 6, maintenance work parameters 601 include maintenance center location 6001, number of maintenance workers 6002, maintenance worker holidays 6003, standard maintenance preparation time 6004, standard maintenance work time 6005, worker movement cost 6006, and failure repair cost. 6007, preventive maintenance cost 6008, and false alarm response cost 6009.

The maintenance center position 6001 is data indicating the position of the center, which is the base for maintenance work, and is, for example, information in which the position is designated by coordinates such as latitude and longitude.

The number of maintenance workers 6002 is the number of maintenance workers deployed in the maintenance center, which is two in this example.

The maintenance worker holiday 6003 indicates information about the maintenance worker's holiday, which is two days off a week in this example.

The standard maintenance preparation time 6004 indicates the standard required time until the worker is assigned after the maintenance demand is fixed, and is one day in this example.

A standard maintenance work time 6005 indicates a standard work time required for maintenance work, which is 4 hours in this example.

The worker movement cost 6006 is the cost incurred when the worker moves, and is 1000 yen/km in this example.

The failure repair cost 6007 is the cost of repairing equipment when it breaks down, and is 100,000 yen in this example.

The preventive maintenance cost 6008 is the cost for preventive maintenance of the equipment, and is 20000 yen in this example.

The false alarm response cost 6009 is the cost incurred when responding to the false alarm, and is 10,000 yen in this example.

FIG. 7 shows an example of estimated maintenance work data 701. As shown in FIG. The estimated maintenance work data 701 is data obtained by estimating the demand for maintenance work of the facility in consideration of the cost required for maintenance with respect to the estimated maintenance demand for the facility. The estimated maintenance work data 701 is generated based on the maintenance work parameters 601 shown in FIG. 6 for the estimated maintenance demand data shown in FIG.

The processing of Step 7 will be described below according to the data in FIGS. 7, 6, and 5. FIG.
As shown in the maintenance work parameter 601, the number of maintenance workers who perform the maintenance work for the equipment is two. Therefore, the maintenance work simulation processing unit 105 uses the work data 7001 for setting the maintenance work for the first worker 1 and the work data 7001 for setting the maintenance work for the second worker 2 as the estimated maintenance work data 701. Business data 7002 is generated.

Also, in the maintenance work parameter 601, workers have two days off per week. Therefore, the maintenance work simulation processing unit 105 defines the 1st, 2nd, 8th, 9th, 15th, 16th, 22nd, 23rd, 29th, 30th (H11 to H20) days as holidays for worker 1. 10 days in total), and the 3rd, 4th, 10th, 11th, 17th, 18th, 24th and 25th days (8 days in total from H21 to H28) are set as holidays for worker 2.

Then, the maintenance work simulation processing unit 105 sets maintenance work for each failure, failure sign, and false alarm as follows.
For example, the maintenance work simulation processing unit 105 sets that the worker 1 is in charge of the failure detected on the 5th, prepares on the 5th, and repairs on the 6th. In addition, since the schedule and equipment in charge of worker 1 were set, worker 2 was in charge of false alarm A (IA in FIG. 5) regarding equipment A on the 7th, and prepared on the 7th. Set to handle false alarms.

On the other hand, as for the facility B, as shown in FIG. 5, the failure sign detected from the 8th represents the sign of the failure occurring on the 10th. As described above, the worker 2 is already dealing with the false alarm of the equipment A on the 8th, and the 10th and 11th are holidays (H23, H24). Therefore, the maintenance work simulation processing unit 105 sets the worker 1 to be in charge of the equipment B. FIG. In addition, as shown in FIG. 7, the maintenance work simulation processing unit 105 sets the 8th and 9th as holidays (H13, H14) for the worker 1, so the next day, the 10th, is prepared. , set the next day as repair. In this case, the worker 1 entered the preparation PA2 on the 10th, but since a failure occurred on the 10th, repairs will be made on the 11th.

In addition, the maintenance work simulation processing unit 105 sets that the worker 2 is in charge of the false alarm on the 14th of the facility B, prepares on the 14th, and responds to the false alarm on the 15th. Furthermore, the failure sign detected from the 18th of the equipment A is a sign of a failure that will occur on the 19th. The maintenance work simulation processing unit 105 sets a schedule in which worker 1 is in charge of the sign, preparations are made on the 18th, and preventive maintenance work is performed on the 19th.

In addition, the maintenance work simulation processing unit 105 sets a schedule in which the worker 2 is in charge of the failure of the equipment B on the 22nd, preparations are made on the 22nd, and repair work is performed on the 23rd.

Further, the failure sign detected from the 24th of the equipment A is a sign of the failure that will occur on the 25th. The maintenance work simulation processing unit 105 sets a schedule in which worker 1 is in charge of the sign, preparations are made on the 24th, and preventive maintenance work is performed on the 25th.

Furthermore, the failure sign detected from the 28th of the facility B is a sign of a failure that will occur on the 29th. The maintenance work simulation processing unit 105 sets a schedule in which the operator 2 is in charge of the sign, prepares on the 28th, and performs preventive maintenance work on the 29th.

In this way, the response of the worker for each piece of equipment to each failure, failure sign, and false alarm is generated as estimated maintenance work data 701 .

Here, according to the estimated maintenance demand generated by the solution simulation (FIG. 5), failure signs are detected one day or more before the standard maintenance preparation time, so preventive maintenance is judged to be successful. For example, for the preventive maintenance performed by the worker 1 shown in FIG. 7, the maintenance work simulation processing unit 105 schedules the preparation on the 18th, one day before the preventive maintenance work is performed on the 19th. Therefore, based on the preparation, the maintenance work simulation processing unit 105 determines that the preventive maintenance on the 19th will be successful. The preventive maintenance performed by worker 1 on the 25th and the preventive maintenance performed by worker 2 on the 29th can be similarly considered.

However, in the maintenance work simulation, the maintenance work simulation processing unit 105 detects a sign of the failure of equipment B on the 10th (FIG. 3) with a detection lead time of 3 days as shown in FIG. Since it takes 4 days for the maintenance lead time from the detection (8th day in FIG. 5) to the implementation of the maintenance work (11th day in FIG. 7), it is determined that the preventive maintenance has failed. In this case, although a sign of failure was detected by the simulation, the schedule for dispatching a worker was too late, so the actual repair schedule was changed to the 10th day of the failure in FIG. This means that preventive maintenance is estimated to be impossible.

In addition, the maintenance work simulation processing unit 105 detected a sign of the failure of equipment A on the 20th (Fig. 3) with a detection lead time of 3 days, but the failure was detected on the schedule before the detection date (20th in Fig. 5). Since preventive maintenance is performed during the period of 18th and 19th (maintenance lead time of 2 days) in FIG. 7, it is determined that preventive maintenance is successful.

Furthermore, the maintenance work simulation processing unit 105 detected a sign of the failure of equipment A on the 25th (Fig. 3) with a detection lead time of 2 days. The name of the day is that the sign was detected with a detection lead time of 2 days, but during the period of the 24th and 25th in FIG. Since preventive maintenance is performed, it is judged that preventive maintenance was successful.

In this manner, by executing the maintenance work simulation in the present embodiment, calculating the maintenance lead time for each failure symptom, and determining the success or failure of preventive maintenance, it is possible to accurately evaluate the effect of the solution.

Next, the simulation execution management unit 101 instructs the maintenance work simulation processing unit 105 to execute a maintenance work simulation for the maintenance demand data 301 saved in Step 2 (Step 8).

The maintenance work simulation processing unit 105 inputs the maintenance demand data 301 and the maintenance work parameters according to the maintenance work simulation execution instruction, executes maintenance work simulation processing, and outputs maintenance work data (Step 9). The maintenance business data corresponds to the business data when the solution is not applied.

FIG. 8 shows an example of maintenance work data 801. As shown in FIG. The maintenance work data 801 is data obtained by calculating the demand for the maintenance work of the facility in consideration of the cost required for the maintenance with respect to the performance of the maintenance demand for the facility. The maintenance work data 801 is generated based on the maintenance work parameters 601 shown in FIG. 6 for the maintenance demand data 301 shown in FIG. The processing of Step 9 will be described below according to the data in FIGS. 8, 6 and 3. FIG.

As shown in the maintenance work parameter 601, the number of maintenance workers who perform the maintenance work for the equipment is two. Therefore, the maintenance work simulation processing unit 105 uses work data 8001 for setting the maintenance work for the first worker 1 and work data 8001 for setting the maintenance work for the second worker 2 as the maintenance work data 801 . Data 8002 is generated.

Since workers have two days off per week, holidays for worker 1 and worker 2 are set as in the case of FIG.
Maintenance work is set for each failure as follows.

For example, the maintenance work simulation processing unit 105 sets the worker 1 to be in charge of the failure of the facility A on the 5th, prepare on the 5th, and perform repair work on the 6th.

Further, the maintenance work simulation processing unit 105 sets the worker 1 to be in charge of the failure of the facility B on the 10th, prepare on the 10th, and perform repair work on the 11th.

Further, the maintenance work simulation processing unit 105 sets the worker 1 to be in charge of the failure of the equipment A on the 20th, prepare on the 20th, and perform repair work on the 21st.

In addition, the maintenance work simulation processing unit 105 sets the worker 2 to take charge of the failure of the equipment B on the 22nd, prepare on the 22nd, and perform repair work on the 23rd.

Furthermore, the maintenance work simulation processing unit 105 sets the worker 1 to be in charge of the failure of the equipment A on the 25th, prepare on the 25th, and perform repair work on the 26th.

In addition, the maintenance work simulation processing unit 105 sets the worker 2 to take charge of the failure of the facility B on the 29th, prepare on the 29th, and perform repair work on the 30th.

In this way, the response of the worker who performed the maintenance work for each failure that actually occurred in the past is generated as maintenance work data.

Next, the simulation execution management unit 101 instructs the maintenance cost calculation processing unit 106 to calculate the maintenance cost (Step 10).

The maintenance cost calculation processing unit 106 inputs the estimated maintenance work data (FIG. 7), the maintenance work data (FIG. 8), and the maintenance work parameters (FIG. 6) according to the maintenance cost calculation instruction, A post-solution maintenance cost for the maintenance work data and a pre-solution maintenance cost for the maintenance work data are calculated, and the difference between the two is calculated (Step 11).

6, the estimated maintenance work data 701 in FIG. 7, and the maintenance work data 801 in FIG. Calculation processing will be explained.

In the estimated maintenance work data 701, failure repair is performed three times, preventive maintenance is performed three times, and false alarm response is performed twice. Therefore, the maintenance cost calculation processing unit 106 refers to the maintenance work parameter 601, and the repair cost is 100,000 yen x 3 times = 300,000 yen, the preventive maintenance cost is 20,000 yen x 3 times = 60,000 yen, and the false alarm response cost is: 10,000 yen×2=20,000 yen is calculated for each, and finally, 380,000 yen, which is the sum of each cost, is calculated as the total cost required for the maintenance work.

On the other hand, in the maintenance work data 801, failure repair is performed six times. Therefore, the maintenance cost calculation processing unit 106 refers to the maintenance work parameter 601 and calculates 100,000 yen×6 times=600,000 yen as the repair cost as the total cost required for the maintenance work.

Therefore, the maintenance cost calculation processing unit 106 calculates 600,000 yen as the maintenance cost before applying the solution and 380,000 yen as the maintenance cost after applying the solution, and calculates the difference between the two as 600,000 yen - 380,000 yen = 220,000 yen. Thus, as a result of introducing the solution, a cost reduction effect of 220,000 yen can be assumed during the 30-day simulation period.

As described above, according to the present embodiment, it is possible to precisely simulate a maintenance work to which a solution is applied, and to obtain a simulation log of an estimated maintenance work that accurately reflects the effect of the solution.

In the maintenance cost calculation of this embodiment, the maintenance cost was calculated by setting a uniform work cost for each of failure repair, preventive maintenance, and false alarm response. to the maintenance site. Specifically, it is possible to record the equipment information to be maintained in the data storage unit 103, and to calculate the moving cost 6006 of the target worker by multiplying the distance.

FIG. 9 shows an example of equipment information. FIG. 9 is an example of equipment information about equipment A and equipment B. In FIG. As shown in FIG. 9, the facility information includes a facility ID 9001 that is an identification code of the facility, a facility model number 9002 that indicates the model of the facility, a facility position 9003 that indicates the installation location of the facility, and an operating date that indicates the date and time when the facility started operating. A start date and time 9004 and a last maintenance date and time 9005 indicating the date and time when maintenance work was last performed on the equipment are included.

The distance between the maintenance center and the equipment can be calculated from the maintenance center position 6001 of the maintenance work parameter and the equipment position 9003 of the equipment information. A straight line distance may be used for simplicity, or a route or distance between two points may be calculated using a GIS (Map Information System) or the like.

In addition, differences in maintenance costs due to differences in equipment failure details, maintenance details such as parts replacement and cleaning, and differences in maintenance costs due to hours of maintenance such as holidays and late nights may be reflected.

Next, the simulation execution management section transmits the maintenance cost and the maintenance cost difference to the GUI processing section (Step 12).

Next, the GUI processing unit displays the maintenance cost and the maintenance cost difference to show the user the effect of introducing the solution (Step 13).

As described above, the maintenance work simulation system of this embodiment calculates the maintenance cost when the solution is applied to the maintenance demand data, and the difference between the maintenance cost when the solution does not function is sent to the user terminal. By displaying it, the effect of the solution can be grasped quantitatively.

Furthermore, in the above description, the failure sign detection solution was taken as an example of the solution, but it can be applied to other solutions. For example, it can be applied to solutions that are particularly effective in preventing failures, such as a solution that diagnoses the remaining life of equipment and a condition-based maintenance solution that monitors equipment and performs maintenance when it reaches a predetermined state.

Moreover, although the example in which the actual maintenance demand is input as the first input data has been described, virtual data may also be used. For example, it is possible to input data in which failures are randomly generated at a predetermined failure rate in equipment for which a certain operation plan is set.

In that case, as input data, information defining the operation plan and failure rate of each facility is entered as equipment information, and processing to generate virtual maintenance demand data is added, and instead of maintenance demand data, virtual maintenance demand data is used to perform the processing from Step 2 onward.

Furthermore, in this case, in the above description, a system for simulating maintenance work was taken as an example, but the system may also be a system for planning maintenance work in response to expected maintenance demand in the future.

In addition, the present invention is not limited to the above-described embodiments, and includes various modifications. For example, the above-described embodiments are detailed descriptions for easy understanding of the present invention, and are not necessarily limited to those having all the configurations. Moreover, it is possible to add, delete, or replace a part of the configuration of the embodiment with another configuration. Further, part or all of the above configurations, functions, processing units, processing means, etc. may be implemented by hardware, for example. Also, the above configurations, functions, etc. may be realized by software by a processor interpreting and executing a program for realizing each function. Information such as programs, tables, and files that implement each function can be stored in a recording device such as a memory, a hard disk, an SSD (Solid State Drive), or a recording medium such as an IC card, an SD card, or a DVD.

DESCRIPTION OF SYMBOLS 100... Simulation apparatus, 101... Simulation execution management part, 102... Communication process part, 103... Data storage part, 104... Solution simulation process part, 105... Maintenance work simulation process part, 106... Maintenance cost calculation process part, 200... Network , 300... User terminal device, 301... GUI unit, 302... Communication processing unit.

Claims (2)

Maintenance demand data that records the maintenance results of equipment to be maintained and solution performance data that indicates the performance of a solution applied to the equipment , and the probability that the solution can detect a sign of failure occurring in the equipment. the solution performance data, including the detection rate indicated by the solution, the false alarm rate indicating the probability of false detection among the failure signs detected by the solution, and the maximum lead time, which is the period used as an index for determining that the detection of the failure signs is successful. a first simulation processing unit that generates estimated maintenance demand data that estimates success or failure of detection of the failure sign and false alarm of the failure sign based on;
said estimated maintenance demand data; and said maintenance parameters relating to at least the cost of handling failures of said equipment, preventive maintenance of said equipment, and dealing with said false alarms, said maintenance parameters including holidays of workers who perform maintenance of said equipment Presumed maintenance that determines the success or failure of preventive maintenance of the equipment by determining whether the maintenance lead time from the detection of the failure sign to the implementation of maintenance work satisfies the maximum lead time for each failure sign based on generating maintenance work data storing responses of workers who performed maintenance work to respective failures that actually occurred in the past, based on the work data, the maintenance demand data, and the maintenance parameters ; a second simulation processing unit that evaluates the solution by determining that preventive maintenance of the facility by the solution is successful ;
A maintenance cost for calculating a maintenance cost after application of the solution to the estimated maintenance work data and a maintenance cost before applying the solution to the estimated maintenance work data, based on the maintenance parameters, and calculating a difference between the two. a calculation unit;
A simulation device comprising: A first simulation processing unit provides maintenance demand data that records the maintenance results of equipment to be maintained and solution performance data that indicates the performance of a solution applied to the equipment, and the solution is generated in the equipment by the solution. False alarm rate indicating the probability that the failure sign detected by the solution is a false detection, and the maximum lead, which is the period in which the detection of the failure sign is judged to be successful. generating estimated maintenance demand data estimating successful or unsuccessful detection of the failure sign and false alarm of the failure sign , based on the solution performance data including time ;
A second simulation processing unit is provided with the estimated maintenance demand data and at least maintenance parameters related to the cost of handling failures of the equipment, preventive maintenance of the equipment, and dealing with false alarms, and the number of workers performing maintenance of the equipment. Preventing the equipment by determining whether the maintenance lead time from detection of the failure sign to execution of maintenance work satisfies the maximum lead time for each failure sign , based on the maintenance parameters including holidays. Based on the estimated maintenance work data for judging the success or failure of maintenance , the maintenance demand data, and the maintenance parameters , the response of the worker who performed the maintenance work for each failure that actually occurred in the past is stored. generating maintenance operations data and evaluating the solution by determining that preventive maintenance of the equipment by the solution was successful ;
A maintenance cost calculation unit calculates, based on the maintenance parameters, a maintenance cost after applying the solution to the estimated maintenance work data evaluated and a maintenance cost before applying the solution to the maintenance work data. calculate the difference,
A simulation method characterized by:

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