JP2020181230A - Maintenance improvement support system - Google Patents

Abstract

To provide a maintenance improvement support system capable of presenting an appropriate maintenance improvement introduction plan according to an improvement policy of a user from a maintenance improvement measure prepared in advance based on knowledge of target asset.SOLUTION: A maintenance improvement support system includes an asset knowledge management unit that manages asset knowledge which is knowledge of a failure and a maintenance of an asset, a maintenance improvement measure database that records maintenance improvement measures defined in advance, a maintenance improvement policy selection unit that receives setting of a maintenance improvement policy by a user, a maintenance improvement measure list generation unit that generates a list of the maintenance improvement measures to be implemented from the asset knowledge and the maintenance improvement measures of the asset and the maintenance improvement policy of the user, and an optimal implement plan generation unit that generates a plan that has high efficiency of maintenance improvement and an order easy to introduce from the list of the maintenance improvement measures to be implemented. The maintenance improvement support system extracts the maintenance improvement measure that matches a characteristic of the asset from the maintenance improvement measures in line with the maintenance improvement policy of the user, and determines an execution order thereof from a dependency of a technique and data request among the maintenance improvement measures.SELECTED DRAWING: Figure 1

Description

The present invention relates to a maintenance improvement support system that operates according to device information and user requirements.

In many areas such as infrastructure, railroads, industrial equipment, and medical equipment, it is necessary to maintain the prescribed performance by continuously implementing maintenance after the introduction of assets. In maintenance, the state of the target asset is collected, a diagnosis that analyzes the presence or absence of abnormalities and problems is applied, and then appropriate maintenance work is applied.

With the development of information technology in recent years, the state of assets is collected by sensors and the current state of assets is grasped, or IT technology such as diagnostic technology that predicts the future state is used to maintain appropriate contents. By implementing at the timing, it is possible to improve reliability while reducing maintenance costs. In addition, there is an example in which the efficiency of maintenance is greatly improved by combining maintenance optimization techniques such as reliability-centered maintenance with IT technology.

However, in order to do so, it is necessary to introduce a maintenance process that is different from the conventional one in cooperation with diagnosis, and the number of personnel, necessary skills, equipment, and other maintenance work resources will change significantly accordingly. In addition, the introduction of new IT technology requires system updates, and depending on the sensor and analysis method, IT resources such as large-capacity storage and large computing power are required. In addition, it is necessary to accumulate data and know-how in order to master the new technology.

In addition, for example, in order to introduce predictive diagnosis technology that predicts future equipment failures from data, it is necessary to incorporate a data collection and visualization system as a preliminary step to accumulate data collection and analysis know-how. Alternatively, if a data collection / visualization system is introduced in advance, it will lead to various measures such as failure location identification support and decision-making support as well as predictive diagnosis, and there is an appropriate order for technology introduction.

Under these circumstances, it is necessary to introduce possible combinations of maintenance and improvement measures in an appropriate order, rather than introducing the measures that one wants to implement one after another in maintenance and improvement activities.

As a method for examining maintenance improvement measures, there is one listed in Patent Document 1. Patent Document 1 is a system that supports the formulation of improvement measures for individual failure modes from the failure mode analysis of the target asset.

Patent No. 4237610

There are practical difficulties in determining the implementation details and introduction order of maintenance improvement measures. First, the content of the improvement measures needs to cover as many important items as possible with respect to the various conservation items required for the assets to be preserved. In addition, maintenance improvement measures are usually specialists such as maintenance consultants and do not have knowledge of what kind of measures are possible.

In deciding the maintenance improvement measures or the order of introduction of IT technology to realize them, it is necessary to introduce from the technical prerequisites. For example, if a condition monitoring system is not introduced and data is not accumulated, predictive diagnosis by data analysis is impossible. Furthermore, it is desirable to introduce improvement measures that include core technologies that lead to more improvement measures.

It is necessary to reflect the policy of the maintenance practitioner as to what kind of maintenance issues are considered important. Whether maintenance cost, responsiveness, or safety depends on the wishes of the maintenance operator. In addition, the scale of investment that can be made at one time and how long it will take to make improvements also depend on the operator.

From the above, the present invention provides a maintenance improvement support system that can present an appropriate maintenance improvement introduction plan from the user's improvement policy from the maintenance improvement measures prepared in advance based on the knowledge of the target asset. With the goal.

From the above, in the present invention, "an asset knowledge management unit that manages asset knowledge, which is knowledge of asset failure and maintenance, a maintenance improvement measure database that records pre-defined maintenance improvement measures, and maintenance improvement by the user. Maintenance improvement policy selection department that accepts policy settings, maintenance improvement measure list generation department that generates a list of maintenance improvement measures to be implemented from asset knowledge of assets, maintenance improvement measures and user maintenance improvement policies, and maintenance to be implemented From the list of improvement measures, it is equipped with an optimal implementation plan generation unit that generates plans in an order that is highly efficient and easy to introduce, and from maintenance improvement measures in line with the user's maintenance improvement policy, assets It is a maintenance improvement support system characterized by extracting those that match the characteristics and determining the implementation order from the dependency of technology and data requirements between maintenance improvement measures. "

By using the present invention, it is possible to promote the introduction of maintenance improvement measures according to the situation of assets and users, and it is possible to improve the efficiency of maintenance work.

The figure which shows the structural example of the maintenance improvement support system which concerns on embodiment of this invention. The figure which shows the example of the asset knowledge D1 in the case of maintenance of a wind turbine as an example. The figure which shows the example of the maintenance improvement measure D2 accumulated in the maintenance improvement measure database 60 in the case of maintenance of a wind turbine as an example. The figure which shows the example of the KPI list. The figure which shows the example of the element technology list. The figure which shows the example of the maintenance improvement policy input screen 90 displayed on the display part in the input / output part HMI. The figure which shows the processing flow in maintenance improvement measure list generation part 40. The figure which shows the score calculation result example. The figure which shows the example of the measure selection display screen 91 displayed on the display part in the input / output part HMI. The figure which shows the processing flow in the optimum implementation plan generation part 50. The figure which shows the dependency of technology / data and the state of policy implementation. The figure which shows the example of maintenance improvement implementation, investment plan creation screen 92 displayed on the display part in the input / output part HMI.

Hereinafter, examples of the present invention will be described with reference to the drawings.

FIG. 1 is a diagram showing a configuration example of a maintenance improvement support system according to an embodiment of the present invention. The maintenance improvement support system 100 shown in FIG. 1 is realized by a computer device, and is generally configured by connecting an input / output unit, a database, a calculation unit, and the like on a transmission bus 20.

FIG. 1 is a diagram schematically showing the functions of the maintenance improvement support system 100 configured by these, and shows the asset knowledge management unit 10 that manages the knowledge of the target asset and a collection of maintenance improvement measures defined in advance. The recorded maintenance improvement measure database 60, the maintenance improvement policy selection unit 30 that accepts the setting of the maintenance improvement policy by the user, the asset knowledge of other specification assets, the maintenance improvement measure, and the maintenance improvement measure to be implemented from the user's maintenance improvement policy. Optimal implementation plan generation unit 50 that generates a plan with high maintenance improvement implementation efficiency and easy introduction from the maintenance improvement measure list generation unit 40 that generates a list and the list of maintenance improvement measures to be implemented. It can be said that the system is provided with an input / output unit HMI that inputs / outputs information to / from the user M.

The user M in the present invention is an administrator or maintenance operator who plans and plans maintenance, or an operator who manages assets, and appropriately gives instructions and data to the maintenance improvement support system 100 via the input / output unit HMI. It is input and provided by the maintenance improvement support system 100 via the input / output unit HMI by displaying appropriate information.

The present invention is not limited to a specific asset, sensor technology, and analysis technology, but the following description will be given by taking the maintenance of a wind turbine as an example. For wind turbines, the utilization rate affects the amount of power generated, so it is necessary to reduce downtime due to maintenance as much as possible. In addition, since it is necessary to move to a remote installation point and climb the tower for actual inspection and maintenance, remote monitoring is applied to various parts such as spindle bearings, generators, brakes, and pitch control. Can be considered. It is also conceivable to apply predictive diagnosis that predicts a failure in advance based on the risk of an accident at the time of failure. In addition, when considering the monitoring of wind farms and remote wind turbines, it is conceivable that a large number of wind turbines will be inspected and maintained at the same time.

Hereinafter, the configuration and function of each part in the maintenance improvement support system 100 will be described in detail according to the processing flow. In addition, in order for the maintenance improvement support system 100 to function, it is necessary that necessary information is held in the maintenance improvement support system 100 in advance. Therefore, asset knowledge D1 and maintenance improvement measure D2 are prepared in advance.

Of these, the asset knowledge D1 is prepared in advance by the asset knowledge management unit 10. For this reason, the user M first inputs the knowledge about the structure and failure mode of the target asset or the knowledge from the installed state monitoring system to the asset knowledge management unit 10 through the input / output unit HMI, and then enters the maintenance improvement support system. It is stored as asset knowledge D1 in an appropriate storage area within 100.

FIG. 2 shows an example of asset knowledge D1 in the case where maintenance of a wind turbine is taken as an example. For example, in the example of FIG. 2, asset knowledge is formed for each item of the component D11 constituting the wind turbine, the failure mode D13 to which the ID (D12) is assigned, the impact on occurrence D14, the inspection means D15, and the maintenance means D16. ..

Specifically, for example, in the asset knowledge D1, blades, shafts, bearings, speed increaser gears, etc. are exemplified as the component D11 constituting the wind turbine, and the blade crack (ID = 1) is set for the blade as the failure mode D13. Blade breakage (ID = 2), misalignment with respect to shaft (ID = 3), bearing scratches / wear (ID = 4), grease deterioration (ID = 5) and bearing sticking (ID = 6) with respect to bearing bearings As the effect D14 at the time of occurrence, for example, the stop time when a blade crack (ID = 1) occurs is 8 hours, the damage is 1 M yen, and the stop time when a blade breakage (ID = 2) occurs. It is described that the damage is 72 hours and the damage is 15M yen. Furthermore, methods such as periodic inspection, remote diagnosis, and overhaul inspection are individually described as inspection means D15 for each failure mode, and component replacement, oil and grease injection, etc. are individually described as maintenance means D16 when repairing this failure. It is described in.

As shown in this example, the asset knowledge includes information such as component D11 of the target asset, failure mode D13, and influence D14 when a failure occurs, and is currently based on the knowledge of failure mode analysis for each failure mode. The available inspection means D15, the current maintenance procedure D16, and the time and cost required for it should also be recorded.

In addition, regarding maintenance improvement measure D2, which is other advance preparation information indispensable for the maintenance improvement support system 100 to function, the maintenance improvement measure database 60 is typically created in advance by experts in maintenance and maintenance IT. Multiple maintenance improvement measures D2 are stored.

FIG. 3 shows an example of the maintenance improvement measure D2 accumulated in the maintenance improvement measure database 60 in the case where the maintenance of the wind turbine is taken as an example. Here, maintenance improvement measure D2 refers to measure D2a, which is the content of business improvement such as improvement of operating rate and work volume leveling, improvement KPI (D2b) such as issues and KPIs to be improved by implementation, and IT technology and cost required for improvement. This is a summary of the elemental technologies D2c.

In FIG. 3, for example, in the case where failure prevention is performed as a measure D2a with an ID of 2, the operating rate and component cost should be considered as an improvement KPI (D2b), and condition monitoring and predictive diagnosis should be performed as an elemental technology D2c. Is remembered in association with. Further, in FIG. 3, for example, in the case where the workload leveling is performed as the measure D2a having the ID of 4, the labor cost should be considered as the improvement KPI (D2b), and the schedule optimization and the predictive diagnosis should be performed as the elemental technology D2c. Is remembered in association with.

Further, in the maintenance improvement measure database 60, the KPI list Lb of FIG. 4 and the elemental technology list Lc of FIG. 5 are recorded in relation to the maintenance improvement measure D2. Here, the KPI list Lb is a comprehensive list of the items described in the improved KPI (D2b) of FIG. 3, and individual items such as maintenance response, operating rate, parts cost, labor cost, recording quality, and reliability. It is a list described in a format in which items are given IDs. The elemental technology list Lc is a comprehensive list of the items described in the elemental technology D2c of FIG. 3, and individual items such as condition monitoring, predictive diagnosis, failure location identification, and schedule optimization are assigned IDs. A list written in a format.

However, especially for the elemental technology list Lc, the elemental technologies (condition monitoring, predictive diagnosis, failure location identification, schedule optimization, etc.) necessary to realize maintenance improvement measures include introduction cost, prerequisite technology, prerequisite data, and generation. It should be saved with the data. Here, the prerequisite technology and the prerequisite data are technologies and data that need to be already available to the maintenance organization when they introduce the elemental technologies. The prerequisite data also records how long or how much it needs to be accumulated. In the example, the required number of years is recorded.

In addition, as the data (premise data, generated data) in the elemental technology list Lc, fixed data given from the setting or past driving experience, and time-series variable data given sequentially from various sensors. Contains sensor data.

The maintenance improvement support system 100 functions as follows on the premise that the above-mentioned various data are prepared in advance.

First, the user M inputs the points to be emphasized in the improvement implementation to the maintenance improvement policy selection unit 40 through the input / output unit HMI.

FIG. 6 shows an example of the maintenance improvement policy input screen 90 displayed on the display unit in the input / output unit HMI. As an example of the maintenance improvement policy input screen 90, the KPI improvement target input unit 90a, the investment plan amount input unit 90b, and the confirmation button 90c are displayed here. In the illustrated example, the contents of the KPI list Lb of FIG. 4 are displayed as KPIs together with the ID in the KPI improvement target input unit 90a, and a check mark is added to the column for improvement request to select. Here, hopes for improvement in availability and component costs are selected. The investment plan amount for each year is input to the investment plan amount input unit 90b, and after inputting each data, the confirmation button 90c is instructed to incorporate the maintenance improvement policy D3.

As shown in the example of FIG. 6, the user M first inputs a maintenance business indicator (KPI) aiming at improvement. As more specific examples of KPIs, various things such as asset utilization rate, reliability, penalty payment amount, failure investigation success rate, and repeated failure rate can be considered. In FIG. 6, the KPI is input by selecting from the KPI list of FIG. 4, but instead of the KPI, the improvement desired item is input in a sentence, and the similarity between the KPI and the KPI is automatically obtained in FIG. The KPI corresponding to the list of may be selected.

In addition, the user M inputs an annual investment amount that can be spent on implementing maintenance improvement. After inputting each of the above information, the user M presses the input result confirmation button 90c. As a result, the maintenance improvement policy D3 is generated, and the processing of the maintenance improvement measure list generation unit 40 is started.

The maintenance improvement measure list generation unit 40 extracts the maintenance improvement measures to be implemented based on the maintenance improvement policy D3 input by the user M, the asset knowledge D1 prepared in advance, and the contents of the maintenance improvement measure D2.

FIG. 7 shows the processing flow in the maintenance improvement measure list generation unit 40. According to the processing flow of FIG. 7, after the start condition is satisfied in the processing step S100, the asset knowledge D1 is first acquired from the asset knowledge management unit 10 in the processing step S110. Here, the asset knowledge D1 includes the contents shown in FIG.

Next, in the processing step S120, the maintenance improvement policy D3 input by the user M is acquired from the maintenance improvement policy selection unit 30, and further, in the processing step S130, the maintenance improvement measure D2 is acquired from the maintenance improvement measure database 60. Here, the maintenance improvement policy D3 includes the contents shown in FIG. 6, and the maintenance improvement measure D2 includes the contents shown in FIGS. 3, 4, and 5.

Next, in the processing step S140, the KPI (in the case of the case of FIG. 6, the operating rate of ID 2 and the component cost of ID 3) that the user M requested to improve in the maintenance improvement policy D3, and the maintenance improvement measure D2. Match with.

According to this matching, in the maintenance improvement measure D2 of FIG. 3 referred to based on the operating rate selected in FIG. 6, failure prevention (ID = 2) and maintenance plan optimization (ID = 5) were hit, and FIG. In the maintenance improvement measure D2 of FIG. 3 referred to based on the component cost selected in 6, failure prevention (ID = 2) and diagnosis support (ID = 3) are hit.

In the processing step S140, the agreement between the KPIs in each maintenance improvement measure D2 and the maintenance improvement policy D3 selected by the user M is checked, and the number of matches is used as the score of the maintenance improvement measure D2. If the maintenance improvement measure D2 has a weight value indicating the relationship to each KPI instead of the number, the total weight of the matching KPIs may be used as the score. In this case, the relationship between the KPI and the maintenance improvement measure D2 in the maintenance improvement policy D3 selected by the user M can be evaluated in more detail. The score obtained in the processing step S140 in this way is the KPI score KS.

Next, in the processing step S150, it is verified which failure (failure mode D14) included in the asset knowledge D1 (FIG. 2) can be improved by each maintenance improvement measure D2 (FIG. 3). In the technical score setting in the processing step S150, a technical score TS is added to the improvement measure D2 depending on which of the asset knowledge D1 can be improved by the elemental technology D2c of each improvement measure D2 in FIG.

As a result, for example, which is the failure mode of FIG. 2 that can most effectively improve the failure when the condition monitoring, which is the elemental technology of FIG. 5, is applied, and the failure can be most effectively improved when the predictive diagnosis is applied. We will clarify which failure mode is shown in FIG.

In the case of the previous case, each of the matched improvement measures D2 is maintenance plan optimization (ID = 5), failure prevention (ID = 2), diagnostic support (ID = 3) in FIG. 3, and further in FIG. According to the maintenance improvement measure D2, the elemental technology D2c for maintenance plan optimization (ID = 5) is reliability-centered preservation and maintenance plan evaluation, and the elemental technology D2c for failure prevention (ID = 2) is status monitoring and precursors. It is a diagnosis, and the elemental technology D2c of the diagnosis support (ID = 3) is to identify the failure location.

Here, for each elemental technology D2c of the improvement measure D2, the technical field (elemental technology list Lc) shown in FIG. 5, the inspection method included in the data of the asset knowledge D1 shown in FIG. 2, and the influence at the time of failure occurrence are described. The technical score TS is set in comparison.

In the case of the previous case, in the elemental technology list Lc shown in FIG. 5, reliability center preservation (ID = 5), maintenance plan evaluation (ID = 6), condition monitoring (ID = 1), and predictive diagnosis (ID = 2). ) And the failure location identification (ID = 3) will be compared with the inspection method included in the data of the asset knowledge D1 shown in FIG. 2 and the influence at the time of failure occurrence.

The technical score TS used in the processing step S150 is, for example, the equation (1). In the equation (1), the viewpoints of evaluation are S, T, I, and M, and the weighting coefficients α, β, γ, and δ are multiplied and added up.
[Number 1]
TS (i, j) = Σ (α × Sj + β / Tj + γ × Ij + δ × Mj) (1)
In equation (1), i is the ID of the improvement measure, j is the ID of the asset knowledge data, and Σ is the sum of all the technical elements of each improvement measure. α, β, γ, and δ are constants. For example, α = 1, β = 6 [month], and γ = δ = 5 [hour].

As described above, the equation (1) defines the technical score TS from the four evaluation viewpoints S, T, I, and M, but in carrying out the present invention, some of these are used or other. It may be one with elements added.

Of these, the evaluation viewpoint S focuses on the presence or absence of the sensor. In comparing the data in FIGS. 5 and 2, first, regarding the sensors in the technical field (elemental technology list Lc), if the sensors can be used for the inspection method, the score TS is given a point S as having commonality. to add. Regarding the point S, the weight α is considered to be 1 when the sensor is available and 0 in other cases. According to the description of the items of the prerequisite data and the generated data of the elemental technology list Lc shown in FIG. 5, the condition monitoring (ID = 1) and the predictive diagnosis (ID = 2) are recorded using the sensor. , The sensor is not used in other items.

Further, in the equation (1), the viewpoints T, I, and M of the evaluation are the periodic inspection cycle T, the investigation time I, and the treatment time M, respectively. Therefore, it is preferable that each time of the periodic inspection cycle T, the investigation time I, and the treatment time M is included in the time such as the stop time of the occurrence time influence D14 in the asset knowledge data D1 of FIG.

Thus, when executing the technical field plan as the operation of the maintenance improvement system, the asset knowledge data D1 related to the time related to maintenance is taken into consideration. For example, if the periodic inspection cycle T is short, or if the survey time I or treatment time M is long, it is considered difficult to formulate a maintenance implementation plan, so the technical score TS is set to a high score.

On the other hand, when the technical field survey is carried out as the operation of the maintenance improvement system, the one with a long survey time is given a high score, and the one with a long treatment time is given a high technical score TS.

As a result, the elemental technology D2c selected for each failure mode item exemplified in the failure mode D13 of FIG. 2 (in the previous example, reliability center storage (ID = 5), maintenance plan evaluation (ID = 6)). , Status monitoring (ID = 1), predictive diagnosis (ID = 2), failure location identification (ID = 3)) is given a technical score TS.

At this time, if the technical score TS (i, j) is larger than the threshold value ε, C (i) = Σ (TS (i, j)> ε) is set as the coverage of the asset knowledge of the measure i. This shows how many asset knowledge the measure contributes to. In addition, the sum of the scores of the individual knowledge data items is taken to obtain the score TS (i) = ΣTS (i, j) for the improvement measures. Where Σ is the sum of j.

FIG. 8 shows an example of the score calculated so far. The score is exemplified as the score TS (i) and the coverage C (i) relating to the improvement measure for each item of the measure D2a in the maintenance improvement measure D2 of FIG. Note that this score is for, for example, a blade crack of i = 1.

In the processing flow of FIG. 7, in the processing step S160 for generating the measure list, a candidate list of measures to be implemented is generated using the calculated score (KPI score KS or technical score TS). Here, the measure candidate list is sorted in the order of the technical score TS (i). This is presented to the user M through the input / output unit HMI in the desired measure selection process 170, and the user M actually selects the measure desired to be introduced.

FIG. 9 shows an example of the measure selection display screen. FIG. 9 shows an example of the measure selection display screen 91 displayed on the display unit in the input / output unit HMI. As an example of the measure selection display screen 91, here, the measure selection display unit 91a, the return unit 91b (return key to the maintenance improvement policy input screen 90), and the confirmation button 91c are displayed. In the illustrated example, the improvement measures in FIG. 8 are displayed in the order of high score of the technology score TS (i), the improvement KPIs and the elemental technologies are displayed together with the IDs on the measure selection display unit 91a, and a check mark is added to the column of desired implementation. It means to select by doing. It can be said that this symbol is obtained by rearranging the maintenance improvement measure data D2 of FIG. 3 in the order of high score of the technical score TS (i).

Here, after the measure of the measure selection display unit 91a is selected by the check, if the user M confirms with the confirmation button 91c, the selection result is output as a measure candidate list and the maintenance improvement measure list generation unit 40 processes. Complete. When the return unit 91b selects to return to the maintenance improvement policy input screen 90, the maintenance improvement policy selection unit 30 starts over. Here, it is assumed that the user M selects and confirms only the failure prevention.

In the processing flow of FIG. 7, the processing step S170 is the measure selection process (adding a check mark) using the measure selection display unit 91a, and the process step S180 determines whether the return unit 91b or the confirmation button 91c is selected. Represents.

Returning to FIG. 1, the optimum implementation plan generation unit 50 receives the prototype candidate list that is the output of the maintenance improvement measure list generation unit 40, and generates a plan for executing this in an appropriate order. Here, the investment plan amount included in the maintenance improvement policy (Fig. 6) input by the user M and the dependency relationship between the elemental technologies included in the list of elemental technologies D2a in the maintenance improvement measure database D2 (Fig. 3) are used. By doing so, user M formulates an optimal measure introduction plan to realize maintenance improvement.

The above-mentioned planning in the maintenance improvement measure list generation unit 40 is based on the following idea. For example, in order to prevent failures in maintenance improvement measures by predicting the occurrence of failures in advance using sensor records or to realize predictive diagnosis of elemental technologies, as shown in FIG. It is necessary to introduce condition monitoring technology in advance to collect sensor records regarding the target asset and failure mode. In addition, even if condition monitoring is introduced, it is actually impossible to execute predictive diagnosis until the data to be analyzed is accumulated, so there is no point in introducing it at the same time. First, condition monitoring is introduced, and predictive diagnosis is performed after a certain period of time. Only after it becomes possible can prevent failure prevention using predictive diagnosis be introduced. The same can be said for predictive diagnosis, improvement of maintenance records for measures, and recording terminals for elemental technologies. Further, even if the user M thinks that only the prevention of failure is necessary, it is indispensable to include the condition monitoring / analysis first, and it is necessary to include the condition monitoring / analysis in the implementation plan.

FIG. 10 shows the processing flow of the optimum implementation plan generation unit 50. According to the processing flow of FIG. 10, after the start condition is satisfied in the processing step S200, in the processing step S210, the user M first executes the implementation from the maintenance improvement measure list generation unit 40 based on the score evaluated by the system. Obtain the list 41 of the desired conservation and improvement measures. This list is, for example, the content shown in FIG.

On the other hand, in the subsequent processing from the processing step S220 to the processing step S260, the data D2 (maintenance improvement measure) of FIGS. 3, 4, and 5 recorded in the maintenance improvement measure database 60 is appropriately referred to. The reading process is performed, and the result is recorded and saved as appropriate.

First, in the processing step S220, failure prevention is a measure D2a determined with reference to FIG. 3 from the operation rate and component cost selected by the KPI improvement target input 90a in FIG. 6 in the previous example. , Diagnosis support, maintenance plan optimization), check if there is any deficiency of the prerequisite technology described in the elemental technology list Lc of FIG.

Here, it is confirmed whether or not there is a deficiency in the prerequisite technology of the elemental technology list Lc corresponding to the elemental technology D2c of each measure D2a recorded in the maintenance improvement measure D2. In this example, since condition monitoring is required as a prerequisite technology for predictive diagnosis, which is an elemental technology D2c used for preventing failure of D2a's measures desired by the user M, this is additionally recorded as a necessary technology.

In the processing step S230, it is confirmed whether or not all the data of the elemental technology list Lc is insufficient in accordance with the elemental technology D2c of each measure D2a recorded in the maintenance improvement measure database 60 for the measure desired by the user M. In the elemental technology list Lc, sensor recording and failure recording are required as prerequisite data for predictive diagnosis, and these are recorded.

In the processing step S250, in order to secure the essential element technologies and data confirmed in the technology dependency check and the data dependency check, the maintenance improvement measure D2 including them is searched from the maintenance improvement measure database 60. In this example, first, the condition monitoring / analysis of the measure D2a of FIG. 3 having the condition monitoring (ID = 1) of FIG. 5 as an element technology is extracted. In addition, since condition monitoring generates sensor records of essential data, the request for this data is eliminated. Regarding the failure record of essential data, the recording terminal, which is an elemental technology for generating the essential data, is first extracted, and the maintenance record improvement, which is an improvement measure including this elemental technology, is extracted.

With the processing up to this point, it can be seen that in order to implement the failure prevention measures desired by the user M, it is sufficient to carry out status monitoring and failure record improvement as a premise. It is possible to satisfy User M's wishes with this, but if condition monitoring, failure record improvement, and failure prevention are introduced, a combination of those elemental technologies and generated data, or the addition of a small number of technologies / data. Therefore, there is a possibility that another maintenance improvement measure can be implemented to expand the maintenance improvement effect.

In the processing step S260, it is searched for other feasible measures by using the elemental technologies and generated data of the measures extracted so far or by adding a small number of elemental technologies. In this process, a measure using the elemental technology based on the selected measure and the generated data is extracted, and the process step S220 and the process step S230 are repeated.

Here, it can be seen that diagnostic support and work volume leveling can be realized by introducing failure location identification support and schedule optimization of each elemental technology. As for the additional measures, the more elemental technologies and data required, the more difficult it is to implement. Therefore, the score is set as -R for the measures as the total number R of the necessary elemental technologies and data.

In addition, we will recursively search for measures that will be realized on the premise of additional measures. However, the elemental technologies and generated data added by the additional measures are counted as insufficient technologies / data R, distinguishing them from the existing ones. This process extracts measures with R smaller than a certain value, or extracts until no feasible measures are found. Here, the threshold value is R <= 2.

FIG. 11 shows the dependency of technology / data and the state of implementation of measures extracted by the processing up to this point. To briefly explain a part of the correlation in FIG. 11, the condition monitoring analysis, which is a measure, performs condition monitoring as an elemental technology and sensor recording as generated data. In addition, as a measure to improve maintenance records, a recording terminal is used as an elemental technology, and failure records and maintenance records are performed as generated data. In addition, the same measures, elemental technologies, and generated data are related to the subsequent failure prevention, diagnostic support, and work volume leveling.

In addition, condition monitoring, which is an elemental technology, can be developed, used, and applied to predictive diagnosis in failure prevention, failure location identification in diagnosis support, and schedule optimization in work volume leveling. In addition, the sensor record as generated data can be applied to predictive diagnosis, which is an elemental technology for failure prevention, as long as there is a one-year accumulation result, and also data for schedule optimization, which is an elemental technology for workload leveling. Applicable. Other generated data can also be used as data judgment material in other elemental technologies after the same long-term experience and data accumulation. In the illustration, the data with a short data accumulation period is positioned at the upper part of FIG. 11 and shown.

Next, in the processing step S270, the implementation schedule is adjusted according to the investment plan amount of FIG. 6 set by the user M in the maintenance improvement policy selection unit 30. Here, from the technology / data dependency extracted as FIG. 11, first, in terms of the dependency, the measures (upper side of FIG. 11) including the underlying elemental technology / generated data are implemented in order, but the essential data is essential. According to the accumulated years, the time from the introduction of the elemental technology accompanying the implementation of the measure to the realization of the next stage elemental technology depending on the generated data and the implementation of the new measure is set. FIG. 11 also shows this number of years.

As a result, the order and minimum number of years required to technically implement each measure were determined, but in reality, the amount of money that User M can invest is limited year by year, so it is in line with that. It is necessary to adjust the implementation timing of measures.

At this time, the implementation timing of each measure is adjusted according to the investable amount of each year of the user M. The actual cost of the measure is the total cost of the elemental technologies that make up the measure, which have not been introduced at the measure implementation stage. At this time, if the two measures cannot be implemented in the same year, priority is given to the measures desired by User M and the measures that provide the prerequisite technology and data for those measures. Other than that, priority is given to those with a high score of measures. However, if there is a measure that can be implemented within the surplus investable amount, that may be prioritized.

FIG. 12 shows an example of the maintenance improvement implementation and investment plan creation screen 92 displayed on the display unit of the input / output unit HMI. As an example of the maintenance improvement implementation and investment plan creation screen 92, here, a time-series investment amount display unit 92a, a legend display unit 92c, a confirmation button 90d, and a part of the time-series investment amount display unit 92a are formed. A judgment-requiring display unit 92b for requesting a problem in time and a necessary judgment is displayed. In the illustrated example, the horizontal axis of the time-series investment amount display unit 92a shows that the budget will be set in the order of maintenance record improvement, condition monitoring analysis, and failure prevention diagnosis support as capital investment projects for each fiscal year. , The vertical axis shows the investment amount and its achievement status in a comparable manner. In the legend display unit 92c, the promises on the display in the time-series investment amount display unit 92a and the like are described.

In addition, according to the comparative display of the investment amount on the vertical axis shown and the achievement status, it has been approved that additional investment is required for the existing condition monitoring analysis for reasons such as performance improvement or expansion of application range. In addition, a plan to advance the installation plan for diagnostic support on the horizontal axis shown is presented. These parts are displayed as the judgment-required display unit 92b, and the final decision is made by the confirmation button 90d including the judgment result here.

A specific example of the judgment-required display unit 92b will be described below. In the equipment planning, it is predicted that it may not be possible to implement the measures with the investable amount set by the user M in the first place. This can occur when the prerequisite technologies for elemental technologies are not in place and essential measures must be implemented in advance. In this case, if it is an essential measure, it will be instructed to make an additional investment. In FIG. 12, since it is impossible to introduce condition monitoring with the investment amount set by the user M, additional investment is urged. In addition, additional investment may make it possible to implement measures at an early stage. In this case, for example, by presenting to the user M the required investment amount in the case of the shortest feasible next period, the necessity of additional investment can be determined. Further, at this time, by displaying the score of each measure and using it as reference information, it is easy to judge the necessity of the user M.

User M finally decides the optimal implementation plan for maintenance improvement measures by checking the necessity of implementing the measures, adjusting the start time, and adjusting the investment amount while checking Fig. 12 on the screen through the input / output unit HMI. To do. The determination result is presented to the user M through the input / output unit HMI.

By the above processing, the maintenance improvement support system in the present invention can be realized.

100: Maintenance improvement support system M: User 10: Asset knowledge management unit 60: Maintenance improvement measure database 30: Maintenance improvement policy selection unit 40: Maintenance improvement measure list generation unit 50: Optimal implementation plan generation unit HMI: Input / output unit

Claims (9)

Asset knowledge management department that manages asset knowledge, which is knowledge of asset failure and maintenance, maintenance improvement measure database that records pre-defined maintenance improvement measures, and maintenance improvement policy selection that accepts user maintenance improvement policy settings From the maintenance improvement measure list generation department that generates a list of maintenance improvement measures to be implemented from the asset knowledge of the asset, the maintenance improvement measures, and the user's maintenance improvement policy, and the list of maintenance improvement measures to be implemented. Equipped with an optimal implementation plan generation unit that generates plans in an order that is highly efficient and easy to introduce, from maintenance improvement measures in line with the user's maintenance improvement policy, those that match the characteristics of the asset A maintenance improvement support system characterized in that it is extracted and its implementation order is determined from the dependency of technology and data requirements between maintenance improvement measures. The maintenance improvement support system according to claim 1.
The maintenance improvement measure list generation unit is a candidate for implementation of the maintenance KPI that the user desires to improve, based on the matching with the maintenance KPI associated with the maintenance improvement measure stored in the maintenance improvement measure database in advance. A maintenance improvement support system characterized by selecting improvement measures. The maintenance improvement support system according to claim 1.
The maintenance improvement measure list generation unit determines the suitability of the maintenance improvement measure that is a candidate for implementation to the target asset characteristics from the asset knowledge input to the asset knowledge management unit to failure inspection means, maintenance means, and impact. A maintenance improvement support system characterized in that the improvement effect of the above is large, or the number of failures improved by the measures is large, and the improvement measures with high suitability are selected. The maintenance improvement support system according to claim 1.
In the optimum implementation plan generation unit, the elemental technologies and the prerequisite data necessary for realizing the maintenance improvement measures that are candidates for implementation are satisfied as much as possible with the elemental technologies and the generated data related to the introduced measures. A maintenance and improvement support system characterized by formulating the implementation order of maintenance and improvement measures in such an order. The maintenance improvement support system according to claim 4.
In addition to the maintenance improvement measures that the user wants to introduce, the optimal implementation plan generation unit is a measure that can be realized based on the elemental technologies and generated data that are introduced by the maintenance improvement measures that are already included in the implementation plan. If there is, a maintenance improvement support system characterized by realizing a wider range of maintenance improvement by presenting it to the user as an additional maintenance improvement measure. The maintenance improvement support system according to claim 5.
The optimal implementation plan generation unit gives priority to presenting to the user the additional maintenance and improvement measures that are not satisfied by the existing maintenance and improvement measures among the elemental technologies and data that are the prerequisites for the implementation. A maintenance improvement support system characterized by efficiently expanding the scope of maintenance improvement. The maintenance improvement support system according to claim 1.
The optimum implementation plan generation unit is a maintenance improvement support system characterized in that the user sets an investment amount for maintenance improvement and determines whether or not the improvement measure that is a candidate for implementation can be implemented with the investment amount of the user. .. The maintenance improvement support system according to claim 7.
The optimum implementation plan generation unit is a maintenance improvement support system characterized by encouraging the user to change the investment plan when the measures to realize the maintenance improvement desired by the user cannot be implemented with the investment amount planned by the user. .. The maintenance improvement support system according to claim 4 or 7.
The optimal implementation plan generation unit can implement a wider range of maintenance improvements by modifying the user's investment plan by calculating the next period in which maintenance improvement measures can be implemented and the costs required for realization. A maintenance improvement support system characterized by encouraging users to change their investment plans when maintenance improvement can be realized earlier.

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