Equipment maintenance system and equipment maintenance method

Abstract

To compare the costs of both on-site maintenance services and remote maintenance services and to propose a maintenance method having a good cost-effectiveness.SOLUTION: An equipment maintenance system for determining a method for handling a failure of equipment is provided with: an asset knowledge database in which a failure and a failure cause of the equipment are registered; an input unit for accepting failure information output from the equipment; a failure cause estimating unit for referring to the asset knowledge database based on the failure information accepted by the input unit and estimating the failure cause; a handling method determining unit for determining a handling method for handling the failure cause estimated by the failure cause estimating unit; a cost estimating unit for estimating a cost, required for the handling method determined by the handling method determining unit, separately for a dispatch handling in which an engineer is dispatched to the scene where the equipment is installed and a remote handling in which an engineer handles the failure remotely; and an output unit for outputting the handling method having the smallest cost among the costs estimated by the cost estimating unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an equipment maintenance system and an equipment maintenance method. [Background technology]

[0002] When equipment such as industrial multifunction copiers breaks down, the maintenance department must listen to the symptoms from the user and infer possible causes of the breakdown. Based on these inferences, it must decide whether to visit the user's site where the equipment is installed or to handle the problem remotely.

[0003] There are two ways to deal with failures: remote maintenance or maintenance by visiting the user site. Patent Document 1 discloses a maintenance support system that includes a maintenance support tree information management unit that manages maintenance support tree information including candidates for remedial work required for recovery from the malfunction phenomenon and information on diagnostic work for identifying them, an optimal work calculation unit that calculates the starting point of work that minimizes the expected value of work cost or work time using the number of past cases, the experience of maintenance workers, diagnostic work, and cost information for each work in the maintenance support tree, and a diagnostic work execution unit that updates the maintenance support tree information by reflecting the results of the executed work in order to repeat the calculation of the optimal work until the recovery of the maintenance target device is completed. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2013-29881 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, the maintenance support system of Patent Document 1 does not take into consideration the travel time and transportation costs required for a business trip when performing maintenance service.

[0006] The object of the present invention is to use the results of failure cause estimation to quantify the costs associated with travel time and transportation expenses required for on-site visits, compare the costs of both on-site and remote maintenance services, and propose a maintenance method with good cost performance. [Means for solving the problem]

[0007] The object of the present invention can be achieved by an equipment maintenance system that determines a response method for responding to an equipment failure, the equipment maintenance system including an asset knowledge database in which equipment failures and their causes are registered, an input unit that accepts failure information output from the equipment, a failure cause estimation unit that refers to the asset knowledge database based on the failure information accepted by the input unit and estimates the cause of the failure by referring to the asset knowledge database, a response method determination unit that determines a response method for responding to the cause of the failure estimated by the failure cause estimation unit, a cost estimating unit that estimates the costs required for the response method determined by the response method determination unit by dividing it into on-site response costs, in which an engineer visits the site where the equipment is installed and responds, and remote response costs, and an output unit that outputs the response method with the lowest cost among the costs estimated by the cost estimating unit. Effect of the Invention

[0008] According to the present invention, it is possible to quantify the travel time and transportation costs required for business trips and to propose a maintenance method with good cost performance. [Brief description of the drawings]

[0009] [Figure 1] Example of a system configuration diagram according to an embodiment of the present invention [Diagram 2] Examples of maintenance knowledge data in an embodiment of the present invention [Diagram 3] Examples of failure probabilities in embodiments of the present invention [Figure 4] Example of child node abnormality probability when parent node is abnormal in the embodiment of the present invention [Diagram 5] Example of child node abnormality probability when parent node is normal in the embodiment of the present invention [Figure 6]Example of a maintenance knowledge Bayesian network in an embodiment of the present invention [Figure 7] 1 is a flowchart showing a process for generating a maintenance knowledge Bayesian network according to an embodiment of the present invention; [Figure 8] 1 is a flowchart showing a process for estimating a cause of a failure according to an embodiment of the present invention; [Figure 9] Example of failure cause estimation result in the embodiment of the present invention [Figure 10] Example of a fault cause response method table in the embodiment of the present invention [Figure 11] Example of a travel expense table in an embodiment of the present invention [Figure 12] Example of response method data in the embodiment of the present invention [Figure 13] Example of business trip response data in the embodiment of the present invention [Figure 14] Examples of remote correspondence data according to an embodiment of the present invention [Figure 15] 1 is a flowchart showing a process for supporting decision-making regarding on-site and remote response methods according to an embodiment of the present invention; [Figure 15a] 13 is an example of step S34 in the flowchart of the decision support process for a business trip / remote response method according to the embodiment of the present invention. [Figure 15b] 13 is an example of step S35 in the flowchart of the decision support process for a business trip / remote response method according to the embodiment of the present invention. [Figure 15c] 13 is an example of step S36 in the flowchart of the decision support process for a business trip / remote response method according to the embodiment of the present invention. [Figure 15d] 13 is an example of step S37 in the flowchart of the decision support process for a business trip / remote response method according to an embodiment of the present invention. [Figure 16] Example of an input screen in an embodiment of the present invention [Figure 17] Example of a result screen in an embodiment of the present invention DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In each drawing for explaining the embodiments, the same components are given the same names and reference numerals as much as possible, and repeated description will be omitted.

[0011] The present invention is not limited to the following examples, and includes various modifications and equivalent configurations within the spirit of the appended claims. For example, the above-mentioned examples have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations.

[0012] In addition, the processing units described in the embodiments may be realized in hardware, for example by designing some or all of them as integrated circuits, or may be realized in software by a processor interpreting and executing a program that realizes each function.

[0013] The tables, areas, etc. described in the embodiments may be a database (DB) or may be data stored in the main memory. Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0014] 1 is an example of a system configuration diagram in an embodiment of the present invention. The facility maintenance system is configured with a computer equipped with a CPU (Central Processing Unit) 2, a memory 3, and an external storage device 4. In this embodiment, a configuration using a stand-alone computer will be described, but it may be realized by a server on a cloud, or each function may be divided and realized by multiple computers.

[0015] The memory 3 includes a weighting coefficient determination unit 7 which determines whether to give priority to monetary cost or time cost in the calculation, a response method determination unit 8 which determines whether maintenance should be performed remotely or on-site by a visit, a failure cause estimation unit 9 which estimates the cause of a failure based on the input equipment status and alarms, a cost estimation unit 19 which estimates the cost of responding to a failure, and an input / output unit 10 which performs input / output processing.

[0016] The external storage device 4 is provided with a failure mode response method database 11 and an asset knowledge database 12. The failure mode response method database 11 stores a failure cause response TBL (table) 13 that determines whether a failure should be responded to remotely or by on-site, and a trip cost TBL (table) that describes the time and cost required for a trip.

[0017] The asset knowledge database 12 stores maintenance knowledge data 15 that associates functional failures and alarms with the failure modes that are the causes of those failures, failure probabilities 16 that store the probability of the occurrence of the cause of failure, child node abnormality probabilities 17 when the parent node is abnormal, and child node abnormality probabilities 18 when the parent node is normal.

[0018] The asset knowledge database is a database that accumulates maintenance knowledge. Maintenance knowledge is information extracted from, for example, a maintenance manual, FMEA, etc., and includes, for example, the maintenance knowledge data shown in Figure 2 and the probability setting data shown in Figures 3 to 5. 2 is an example of maintenance knowledge data in an embodiment of the present invention. A functional failure indicates a failure phenomenon such as cooling water not circulating or part 2 cannot be stored. A failure mode indicates the cause of the failure. A check item is a field for storing items to be checked, such as sensor data of the equipment, environment, equipment, components, etc.

[0019] The check items are used to identify phenomena that may occur when a failure mode occurs, and to check whether the phenomenon has occurred. There is not necessarily one check item that corresponds to one failure mode. Alarm information that often occurs when the functional failure occurs is also included.

[0020] 3 shows an example of failure probability in an embodiment of the present invention. This is data for setting the probability when creating a Bayesian network. It includes failure mode 41, which stores the name of the failure mode, state 42, and probability 43. Based on this table, the occurrence probability of each failure mode can be known.

[0021] The state 42 stores Y or N, which respectively indicate the occurrence and non-occurrence of a failure mode. These states are set so that the sum of the probabilities of Y and N related to the same failure mode is 1.

[0022] Probability 43 stores the probability of the failure mode state. Here, the failure mode probability may be set to fixed values ​​such as 50% for the failure mode states Y and N. However, this is not limited to this, and calculations can also be made from past history, etc. The probability of the failure mode occurrence state is, for example, a value obtained by dividing the number of occurrences of the failure mode in the failure history by the total number of occurrences.

[0023] 4 shows an example of the child node anomaly probability when a parent node is abnormal in an embodiment of the present invention. It includes a parent node 51, a state 52, a child node 53, a child node state 54, and a probability 55. When a certain failure mode occurs, the check items show the probability of anomaly and normality.

[0024] The name of the failure mode is stored in the parent node 51. In this table, Y is stored in the state 52, which means the occurrence state of the failure mode.

[0025] A check item is stored in child node 53, in this case the name of the sensor. A check item state is stored in child node status 54, in this case the state of the sensor. A probability of the check item state is stored in probability 55, in this case the probability of the sensor state. The sum of the abnormal and normal probabilities for the same check item state is set to 1.0 (100%).

[0026] It is possible to set fixed values ​​such as 100% when abnormal and 0% when normal, or it may be calculated from past failure history.

[0027] 5 is an example of a child node abnormality probability when a parent node is normal in an embodiment of the present invention. It includes a child node 61, a child node state 62, and a probability 63.

[0028] A check item is stored in child node 61, and here, the check item is stored. A child node status 62 stores the check content, and here, the status of the check item is stored. A probability 63 stores the probability of the check item status, and here, the probability of the sensor status is stored. The sum of the abnormal and normal probabilities related to the same check item status is set to 1.0 (100%).

[0029] It may be set to a fixed value such as 0% when abnormal and 100% when normal, or it may be calculated from the past failure history.

[0030] When an abnormality occurs in equipment, the failure cause estimation unit in Figure 1 generates a maintenance knowledge Bayesian network for cause estimation, inputs check items into the maintenance knowledge Bayesian network according to information on the symptoms of the failure input by maintenance personnel or users, and calculates the occurrence probability of each failure mode. <Maintenance knowledge Bayesian network generation process> 7 is a flowchart showing an example of a process for generating a maintenance knowledge Bayesian network in an embodiment of the present invention. This process is executed by the failure cause estimation unit 9 using the maintenance knowledge data 15 stored in the asset knowledge database 12.

[0031] 6 is an example of a maintenance knowledge Bayesian network in an embodiment of the present invention. This maintenance knowledge Bayesian network is a Bayesian network with a four-layer structure of alarms, functional failures, failure modes, and check items, and arrows indicate causal relationships.

[0032] The start of the arrow is the parent node and indicates the cause. The tip of the arrow is the child node and indicates the result. The maintenance knowledge Bayesian network generation process flow is as follows:

[0033] First, the maintenance knowledge data 15 is obtained from the asset knowledge database (S11). Next, a Bayesian network node is created from each cell of the maintenance knowledge data 15 (S12). At this time, cells with the same content are generated as one node.

[0034] Then, a parent-child link is established between each node according to the causal relationship described in the maintenance knowledge data 15 (S13).

[0035] Links are established between the nodes in parent-child relationships according to the causal relationships described in the maintenance knowledge data 15. Specifically, Arrows are set to create causal relationships (parent-child relationships) such as "Failure mode" → "Functional failure", "Failure mode" → "Check item", and "Functional failure" → "Alarm".

[0036] The probability of failure 16 is set as the prior probability for the set arrow (S14). Next, a posterior probability is set for the arrow indicating the parent-child relationship using the probability of child node abnormality when the parent node is abnormal 17 and the probability of child node abnormality when the parent node is normal 18 (S15).

[0037] Figure 8 is an example of a flowchart of a fault cause estimation process in an embodiment of the present invention. Information on the symptoms of the fault is received from a maintenance worker or a user via the input unit (S21). Using an input screen such as that shown in Figure 16, "abnormal" or "normal" is entered into the check items according to the equipment fault information provided by the maintenance worker or the equipment user.

[0038] "Abnormal" means that something is occurring, and "Normal" means that something is not occurring. If it is unknown whether something is occurring or not, do not enter anything. For example, if there is information that "oil is leaking," enter "Abnormal" in the check item "Oil is leaking." Do not enter anything in other check items.

[0039] A maintenance knowledge Bayesian network is generated based on the input information (S22). The occurrence probability of each failure mode is calculated using the maintenance knowledge Bayesian network, and the estimation results are output to the response method determination unit 8. The failure cause estimation results are as shown in Figure 10. An occurrence probability 93 corresponding to each failure mode 92 is calculated.

[0040] Next, the decision support process for the on-site / remote response method will be described.

[0041] 10 shows an example of a fault cause handling method table in the embodiment of the present invention, which stores fault causes 101 and fault handling methods 102.

[0042] 11 is an example of a travel expense table in an embodiment of the present invention. It stores, in association with each other, the customer 103 who owns the equipment, the travel time 104 to and from the location where the equipment is installed, and the transportation cost 105 for the travel.

[0043] 12 is an example of response method data in an embodiment of the present invention. For each failure mode 107, a response method 108 indicating whether the failure requires on-site response or can be handled remotely, and a probability 109 indicating the possibility of the failure mode are stored in association with each other.

[0044] 13 is an example of business trip response data in an embodiment of the present invention. This is a table that collects only those data in FIG. 12 for which the response method is business trip.

[0045] 14 is an example of remote response data in an embodiment of the present invention. This is a table that collects only those remote response methods from the data in FIG.

[0046] FIG. 15 illustrates an example of a flowchart of a decision support process for a business trip / remote response method in an embodiment of the present invention that uses these tables to propose a response method with high cost performance.

[0047] The failure cause response method and user information input from the failure mode response method database from the maintenance worker terminal 5 are received via the input / output unit 10. At this time, weighting information indicating which of monetary cost and time cost is to be given more importance may also be received. A failure cause estimation result is obtained using a Bayesian network based on the received user information (S31).

[0048] A countermeasure is determined based on each failure cause by referring to the failure cause countermeasure TBL 13, and countermeasure determination information 91 is created in which the countermeasure is associated with the probability of the estimated failure cause (S32).

[0049] For each failure mode 107 in the response method determination information 91, the response methods 108 are divided into remote failures and on-site failures (S33).

[0050] The cost estimating unit 19 calculates a monetary cost M remote for a failure whose response method is remote (S34), and calculates a time cost T remote for each failure whose response method is remote (S35).

[0051] The M remote calculation process performed in S34 is shown in Figure 15a. First, M remote and Mkj are initialized to 0 (S001). The number of rows k of remote compatible data is received and this process is repeated k times. The probability of the failure mode of each row shown in Figure 14 is set to "P remote k" (S002). Each time process S002 is performed, the number of rows j of on-site compatible data is received and the process is cycled. The probability of the failure mode of each row shown in Figure 13 is set to "P on-site j" and the monetary cost is calculated according to the following formula (1) (S003).

[0052] T traffic is the travel time shown in Figure 11, and M traffic is the transportation cost. M labor cost is the hourly labor cost of the maintenance worker who performs the maintenance work.

[0053] Mkj= P business trip j × (1-P remote k) × (T transportation × M personnel expenses · H + M transportation expenses) (1) Each time step S003 is performed, Mkj is added to M distance (S004). Step S003 is performed k×j times to obtain M distance (S005).

[0054] The calculation process of T remote performed in S35 is shown in Figure 15b. First, T remote and Tkj are initialized to 0 (S011). The number of rows k of remote compatible data is received and this process is repeated k times. The probability of the failure mode of each row shown in Figure 14 is set to "P remote k" (S012). Each time process S002 is performed, the number of rows j of on-site compatible data is received and the process is repeated. The probability of the failure mode of each row shown in Figure 13 is set to "P on-site j" and the time cost is calculated according to the following formula (1) (S013).

[0055] Tkj=P business trip j×(1-P remote k)×T traffic (2) Each time step S003 is performed, Tkj is added to T-remote (S014). Step S003 is performed k×j times to obtain M-remote (S015).

[0056] Based on the weighting information, the monetary cost and the time cost are multiplied by their respective weights and summed up to obtain the cost remote, which is the cost of remote support (S36). The weighting information may be set to a predetermined weight, or the monetary cost and the time cost may be weighted equally.

[0057] Next, the cost estimating unit 19 calculates M trips, which is the total monetary cost even if the response method is a trip to the breakdown (S37), and calculates T trips, which is the total time cost (S38). Similarly, it calculates the cost trip based on the weighting information (S39).

[0058] The calculation process for M business trips performed in S36 is shown in Figure 15c. First, M business trips and Mkj are initialized to 0 (S021). The number of rows k of remote support data is received and this process is repeated k times. The probability of the failure mode of each row shown in Figure 14 is set to "P remote k" (S022). Each time process S022 is performed, the number of rows j of business trip support data is received and the process is cycled. The probability of the failure mode of each row shown in Figure 13 is set to "P business trip j" and the monetary cost is calculated according to the following formula (1) (S023). Mkj = P remote k × (1-P business trip j) × [(T remote + T transportation) × M personnel expenses · H + M transportation expenses] (3) Each time step S023 is performed, Mkj is added to M trips (S024). Step S023 is performed k×j times to obtain M trips (S025).

[0059] The calculation process for T business trips performed in S37 is shown in Figure 15d. First, T business trips, Tkj, are set and initialized (S031). The number of rows k of remote support data is received and this process is repeated k times. The probability of the failure mode of each row shown in Figure 14 is set to "P remote k" (S032). Each time process S032 is performed, the number of rows j of business trip support data is received and the process is repeated. The probability of the failure mode of each row shown in Figure 13 is set to "P business trip j" and the time cost is calculated according to the following formula (1) (S033). Tkj=P business trip j×(1-P remote k)×T traffic (4) Each time step S033 is performed, Tkj is added to T trip (S034). Step S033 is performed k×j times to obtain T trip (S035).

[0060] The cost estimation unit 19 compares the cost of remote response with the cost of on-site response (S40), and if the cost of remote response is higher, the output unit outputs information such as result display message 170 shown in FIG. 17 "Since the cost of on-site response method is lower, a on-site response is recommended" to the maintenance staff (S41), and if the cost of on-site response is higher, the output unit outputs result display message 170 such as information such as "Since the cost of remote response method is lower, remote response is recommended" to the maintenance staff (S42).

[0061] Finally, "M remote", "T remote", "M business trip", and "T business trip" are output to the cost calculation result 172 (S43).

[0062] 17 is an example of a result display screen in an embodiment of the present invention. Cost calculation result 172 shows the costs for four cases. The four cases are a combination of the response method (on-site / remote) for the failure mode that actually occurred and the response method (on-site / remote) adopted at the discretion of the maintenance staff.

[0063] Among the four cases, there are two ideal cases: when a failure mode occurs that requires an on-site response and a maintenance technician responds by on-site response, and when a failure mode occurs that requires a remote response and a maintenance technician responds by remote response. The ideal cases mean that no unnecessary costs or time are incurred.

[0064] For example, if failure mode F1 occurs that requires an on-site response, and a maintenance technician performs a response based on the symptoms, the only costs and time required for the on-site response are incurred, which is an ideal case.

[0065] If failure mode F1 occurs, which requires an on-site response, and the maintenance staff determines based on the symptoms that the response method is to respond remotely, the equipment may not be restored easily through remote response, and an on-site response may be required, which incurs not only the cost and time of the on-site response but also the cost of the remote response, so this is not an ideal case.

[0066] The cost calculation result 172 shows the cost of four cases, the cost and time required compared to the ideal case as (1) to (4). (1) and (4) are ideal cases, and deviations from the ideal are displayed as cost: 0 yen and work time: 0 hours. In (2), the deviation from the ideal is shown as cost: “M remote” yen and work time: “T remote” hours. In (3), the deviation from the ideal is displayed as Cost: “M business trip” yen, Working time: “T business trip” hours. “M remote”, “T remote”, “M business trip”, and “T business trip” are values ​​calculated from the flowchart shown in Figure 15. [Explanation of symbols]

[0067] 1 Facility maintenance system, 2 CPU, 3 Memory, 4 External storage device, 5 Maintenance staff terminal, 6 Maintenance staff, 7 Weight coefficient determination unit, 8 Response method determination unit, 9 Failure cause estimation unit, 10 Input / output unit, 11 Failure mode response method database, 12 Asset knowledge database, 13 Failure cause response TBL, 14 Travel cost TBL, 15 Maintenance knowledge data, 16 Failure probability, 17 Child node abnormality probability when parent node is abnormal, 18 Child node abnormality probability when parent node is normal, 19 Cost estimation unit

Claims

1. An equipment maintenance system that determines a method for responding to a failure of equipment, An asset knowledge database that registers equipment failures and their causes; An input unit that receives fault information output from the equipment; a failure cause estimation unit that refers to an asset knowledge database based on the failure information received by the input unit and estimates a cause of the failure; a response method determination unit that determines a response method for responding to the failure cause estimated by the failure cause estimation unit; a cost estimating unit that estimates the costs required for the response method determined by the response method determining unit into a cost for an on-site response in which an engineer visits the site where the equipment is installed and a cost for a remote response in which the engineer performs the response remotely; The facility maintenance system includes an output unit that proposes the less expensive response method between the on-site response cost and the remote response cost estimated by the cost estimation unit.

2. 2. The facility maintenance system according to claim 1, wherein the cost estimating unit estimates the cost of a site visit by taking into account the transportation costs and time required for an engineer to travel to the site.

3. The asset knowledge database stores the occurrence probability of a failure cause corresponding to the failure information, 3. The facility maintenance system according to claim 2, wherein the failure cause estimation unit estimates the failure cause based on the occurrence probability of the failure cause.

4. the input unit receives cost information indicating whether to prioritize cost or time for responding to a failure; 3. The equipment maintenance system according to claim 2, further comprising a weighting coefficient calculation unit that determines a weighting coefficient indicating whether to prioritize cost or time when the cost estimating unit estimates costs based on the cost information.

5. The failure cause estimation unit estimates a plurality of failure causes, The cost estimating unit estimates the cost for each cause of failure, calculates the total of on-site response costs and the total of remote response costs, The facility maintenance system according to claim 2 , wherein the output unit outputs the smaller of the total on-site response cost and the total remote response cost.

6. An equipment maintenance method for determining a method for dealing with a failure of equipment, comprising: The input unit receives fault information output from the equipment, The failure cause estimation unit estimates the cause of the failure based on the failure information received by the input unit by referring to an asset knowledge database in which equipment failures and their causes are registered, A countermeasure determination unit determines a countermeasure for dealing with the cause of the failure estimated by the failure cause estimation unit; The cost estimation unit estimates the costs required for the response method determined by the response method determination unit into an on-site response cost for an engineer to visit the site where the equipment is installed and a remote response cost for an engineer to respond remotely, An equipment maintenance method in which an output section proposes the less expensive response method between the on-site response cost and the remote response cost estimated by a cost estimating section.

Images