Equipment efficiency deterioration diagnosis device, training device, monitoring device, and energy saving activity support system

Abstract

An equipment efficiency deterioration diagnosis device (30) comprises: an acquisition unit (31) that acquires, in advance, the power amount of target equipment and a plurality of pieces of information data related to the target equipment; an inference unit (32) that infers information data which is strongly correlated to the power amount among the plurality of pieces of information data; a reference calculation unit (33) that calculates, in advance, an approximation curve on the basis of a scatter diagram of the power amount and the information data; an equipment efficiency calculation unit (34) that acquires the power amount of the target equipment and operation data which is related to the target equipment, which is strongly correlated to the power amount, and which is data during operation of the target equipment, and that calculates, as an equipment efficiency index, the degree of deviation of the operation data from the approximation curve; a representative value recording unit (35) that, when one equipment efficiency index is calculated, records the equipment efficiency index as a representative value, and that, when a plurality of equipment efficiency indices are calculated, records a representative value which is obtained from the plurality of equipment efficiency indices; and a diagnosis unit (36) that determines whether or not the efficiency of the target equipment is deteriorated on the basis of the representative value itself or a change in the representative value.

Description

Equipment efficiency decline diagnostic device, learning device, monitoring device, and energy-saving activity support system

[0001] The present disclosure relates to an equipment efficiency decline diagnosis device, a learning device, a monitoring device, and an energy-saving activity support system that are used to present information on a trend of efficiency decline in equipment such as production equipment or related equipment.

[0002] Conventionally, there has been known a technique for easily detecting an abnormality in facility equipment using measurement values ​​collected during normal operation of the equipment. For example, a technique has been disclosed in which comparison measurement values ​​having a strong correlation with a measurement value to be determined, a correlation coefficient between the measurement value to be determined and each comparison measurement value is calculated for each divided period, and if the correlation coefficient falls below a predetermined threshold, it is determined that an abnormality has occurred during that period (see, for example, Patent Document 1).

[0003] Japanese Patent Application Laid-Open No. 2015-21965

[0004] According to the technology described in Patent Document 1, it is possible to detect whether an abnormality has occurred in equipment from data collected during a target period. However, with the technology described in Patent Document 1, an abnormality is determined based on a correlation coefficient within a certain target period, so it is not possible to make a comparison over time for each target period, and it is not possible to determine a time-series trend, such as a tendency for equipment efficiency to deteriorate.

[0005] The present disclosure has been made in consideration of the above, and aims to provide an equipment efficiency degradation diagnosis device that can contribute to presenting information indicating a trend in equipment efficiency degradation when the efficiency of the equipment declines.

[0006] In order to solve the above-mentioned problems and achieve the objectives, the equipment efficiency degradation diagnosis device according to the present disclosure includes an acquisition unit that acquires in advance the amount of power of the target equipment and multiple pieces of information data related to the target equipment, an estimation unit that estimates information data that has a strong correlation with the amount of power among the multiple pieces of information data acquired by the acquisition unit, and a reference calculation unit that calculates in advance an approximation curve that serves as a reference for an equipment efficiency index for determining whether the efficiency of the target equipment has decreased, based on a scatter plot of the amount of power and the information data estimated by the estimation unit. The equipment efficiency degradation diagnostic device according to the present disclosure further includes an equipment efficiency calculation unit that acquires the power amount of the target equipment and operation data that is related to the target equipment, has a strong correlation with the power amount, and is data when the target equipment is in operation, and calculates the degree of deviation of the operation data from the approximation curve calculated by the reference calculation unit as an equipment efficiency index; a representative value recording unit that records one equipment efficiency index as a representative value when one equipment efficiency index is calculated by the equipment efficiency calculation unit, and records representative values ​​obtained from the multiple equipment efficiency indexes when multiple equipment efficiency indexes are calculated by the equipment efficiency calculation unit; and a diagnostic unit that determines whether the efficiency of the target equipment has decreased based on the representative value itself recorded by the representative value recording unit or a change in the representative value.

[0007] The equipment efficiency degradation diagnosis device according to the present disclosure has an effect of being able to contribute to the presentation of information indicating a trend of a decrease in the efficiency of the equipment when the efficiency of the equipment has decreased.

[0008] FIG. 1 is a diagram showing the configuration of the energy-saving activity support system according to embodiment 1. FIG. 2 is a flowchart showing the procedure of operations performed by a learning device included in the energy-saving activity support system according to embodiment 1. FIG. 3 is a diagram showing an example of a scatter diagram created by an equipment data identification unit included in the learning device of the energy-saving activity support system according to embodiment 1. FIG. 4 is a flowchart showing the procedure of operations performed by a monitoring device included in the energy-saving activity support system according to embodiment 1. FIG. 5 is a diagram showing an example of a deviation calculated by a deviation calculation unit included in the monitoring device of the energy-saving activity support system according to embodiment 1. FIG. 6 is a diagram for explaining the determination made by an equipment efficiency deterioration trend determination unit included in the monitoring device of the energy-saving activity support system according to embodiment 1. FIG. 1 is a diagram for explaining calculations performed by a deviation degree calculation unit possessed by the device. FIG. 2 is a diagram for explaining operations performed by an equipment efficiency calculation unit and an equipment efficiency deterioration trend determination unit possessed by a monitoring device of the energy-saving activity support system according to embodiment 1. FIG. 3 is a diagram showing the configuration of an equipment efficiency decline diagnosis device according to embodiment 2. FIG. 4 is a diagram showing the configuration of an energy-saving activity support system according to embodiment 3. FIG. 5 is a diagram showing a processor in the case where at least some of the functions of the data acquisition unit and equipment data specification unit possessed by the learning unit of the learning device of the energy-saving activity support system according to embodiment 1 are realized by a processor. FIG. 6 is a diagram showing a processing circuit in the case where at least some of the functions of the data acquisition unit and equipment data specification unit possessed by the learning unit of the learning device of the energy-saving activity support system according to embodiment 1 are realized by a processing circuit.

[0009] Hereinafter, an equipment efficiency degradation diagnosis device, a learning device, a monitoring device, and an energy-saving activity support system according to embodiments will be described in detail with reference to the accompanying drawings.

[0010] Embodiment 1. Figure 1 is a diagram showing the configuration of an energy-saving activity support system 1 according to Embodiment 1. The energy-saving activity support system 1 includes a production facility 2, a related facility 3, a first power sensor 4, a second power sensor 5, a production volume sensor 6, an environmental sensor 7, a production management device 8, an energy management device 9, an information terminal 10, a learning device 11, and a monitoring device 16. In Embodiment 1, the energy used by the production facility 2 and the related facility 3 is electric power, but the energy used by the production facility 2 and the related facility 3 may be primary energy such as petroleum, coal, gas, or hydrogen, or a combination of electric power and primary energy.

[0011] The production facility 2 executes a production process to produce multiple items. In the production facility 2, for example, a production line is made up of multiple production devices, but the production facility 2 may have only one production device. The items produced by the production facility 2 are, for example, industrial products or work-in-progress of industrial products. Hereinafter, the items produced by the production facility 2 will be referred to as the product to be produced. The product to be produced may be a liquid or a gas.

[0012] The related equipment 3 is equipment used in connection with the production equipment 2. For example, the related equipment 3 is equipment such as lighting equipment, air conditioners, compressors, or dust collectors that are turned on by workers or the like when producing products produced by the production equipment 2. The related equipment 3 is also called utility equipment.

[0013] The first power sensor 4 is attached to the production facility 2 or to a power transmission line that transmits power to the production facility 2, and periodically measures the amount of power consumed by the production facility 2 and transmits information indicating the measured amount of power consumed to the energy management device 9 via a dedicated line. The first power sensor 4 may also transmit information indicating the measured amount of power consumed to the energy management device 9 via a network.

[0014] The second power sensor 5 is attached to the power transmission line that transmits power to the related equipment 3 or to the related equipment 3, and periodically measures the amount of power consumed by the related equipment 3 and transmits information indicating the measured amount of power consumed to the energy management device 9 via a dedicated line. The second power sensor 5 may also transmit information indicating the measured amount of power consumed to the energy management device 9 via a network.

[0015] The production quantity sensor 6 is provided in the production facility 2, periodically measures the production quantity of the production facility 2, and transmits information indicating the measured production quantity to the energy management device 9 via a dedicated line. The production quantity sensor 6 may transmit information indicating the measured production quantity to the energy management device 9 via a network. The production quantity sensor 6, for example, counts the number of products to be produced passing through the production line of the production facility 2, and measures the counted value as the production quantity. If the product to be produced is a liquid or a gas, the production quantity measured by the production quantity sensor 6 is the production flow rate. The production quantity measured by the production quantity sensor 6 may be expressed in weight or length.

[0016] The environmental sensor 7 periodically measures the production environment, which is the environment in the production process of the production equipment 2, and transmits information indicating the measured production environment to the energy management device 9. The production environment measured by the environmental sensor 7 is, for example, the temperature, humidity, carbon dioxide concentration, brightness, noise, or vibration in the room where the production equipment 2 is located. The information indicating the production environment may be input to a terminal device (not shown). In this case, the information indicating the production environment is transmitted from the terminal device to the energy management device 9 via a dedicated line or a network.

[0017] The production management device 8 has a storage unit (not shown) that stores production management information. The storage unit is realized by, for example, a semiconductor memory. The production management information includes information such as the type of product to be produced each day, the person in charge of the production process, error information for the production equipment 2, the lot size of the product to be produced, and the takt time of the production equipment 2. The takt time is, for example, the time obtained by dividing the operating time of the production equipment 2 in one day by the production volume for that day.

[0018] The production management device 8 has a processing unit (not shown), which reads out production management information stored in the storage unit from the storage unit and transmits the read production management information to the energy management device 9 via a dedicated line. The processing unit may also transmit the read production management information to the energy management device 9 via a network. The production management device 8 can acquire part of the production management information from the production equipment 2 via a dedicated line (not shown) or a network (not shown).

[0019] The production management device 8 has an image sensor (not shown) that can identify part of the production management information. The production management device 8 can also store information input from an input unit (not shown) in a storage unit as part of the production management information. The input unit can accept input from a keyboard or input by voice recognition, and output the accepted information to the energy management device 9.

[0020] The energy management device 9 collects information from each of the first power sensor 4 , the second power sensor 5 , the production amount sensor 6 , the environment sensor 7 and the production management device 8 .

[0021] The information terminal 10 is realized by, for example, a tablet or a personal computer, and displays the equipment efficiency deterioration trend obtained from an equipment efficiency deterioration trend notifying unit 22 (described later) of the monitoring device 16. The user can visually recognize the displayed equipment efficiency deterioration trend. The equipment efficiency deterioration trend means that the efficiency of the equipment being monitored is on a worsening trend.

[0022] The learning device 11 has a learning unit 12 that creates an approximation curve that serves as a reference for equipment efficiency before the monitoring device 16 begins monitoring, and an equipment-related data storage unit 15. The equipment-related data storage unit 15 is realized, for example, by a semiconductor memory. The learning unit 12 has a data acquisition unit 13 that acquires information collected by the energy management device 9. The information collected by the energy management device 9 includes information such as power amount and equipment data acquired by the environmental sensor 7. The learning unit 12 further has an equipment data identification unit 14 that identifies equipment data that has a high correlation with the power amount of the equipment to be monitored from the data acquired by the data acquisition unit 13. The equipment data identification unit 14 calculates an approximation curve for the equipment data that has a high correlation with the power amount of the equipment to be monitored.

[0023] The equipment-related data storage unit 15 stores information about the approximate curve generated by the learning unit 12. As described above, the approximate curve is an approximate curve of equipment-related data that has a high correlation with the amount of power of the equipment being monitored. Even while the monitoring device 16 is performing monitoring, the learning device 11 may operate to identify equipment data that has a high correlation with the amount of power of the equipment being monitored and update the approximate curve.

[0024] The monitoring device 16 includes a data acquisition unit 17, a deviation calculation unit 18, an equipment efficiency calculation unit 19, an equipment efficiency storage unit 20, an equipment efficiency deterioration trend determination unit 21, and an equipment efficiency deterioration trend notification unit 22. The monitoring device 16 sets in advance a monitoring interval for the equipment efficiency deterioration trend. The monitoring interval may be, for example, every hour, every day, every month, every season, or every year.

[0025] The data acquisition unit 17 acquires information collected by the energy management device 9. The information collected by the energy management device 9 includes information such as the amount of power and facility data obtained by the environmental sensor 7.

[0026] The deviation calculation unit 18 calculates the degree of deviation from the approximation curve for each of a plurality of measurement points made up of the power amount data and equipment data of the equipment to be monitored acquired by the data acquisition unit 17 within the monitoring interval. Information on the approximation curve is stored in the equipment-related data storage unit 15 of the learning device 11. For example, if the measurement point is on the approximation curve, the degree of deviation will be small, and the further the measurement point is from the approximation curve, the greater the degree of deviation.

[0027] The equipment efficiency calculation unit 19 calculates a representative value of the plurality of deviation degrees calculated by the deviation calculation unit 18 as an equipment efficiency index. The representative value is an average value, median value, weighted average value, moving average value, or the like of the plurality of deviation degrees. The equipment efficiency storage unit 20 stores information indicating the equipment efficiency index calculated by the equipment efficiency calculation unit 19. The equipment efficiency storage unit 20 is realized by, for example, a semiconductor memory.

[0028] The equipment efficiency deterioration trend determination unit 21 plots the equipment efficiency index calculated by the equipment efficiency calculation unit 19 on a graph in chronological order, and determines that the efficiency of the equipment to be monitored is on a deterioration trend when, for example, the slope of the line connecting two adjacent plotted points on the graph falls below a certain threshold, or when the equipment efficiency index falls below a certain threshold. When the equipment efficiency deterioration trend determination unit 21 determines that the efficiency of the equipment to be monitored is on a deterioration trend, the equipment efficiency deterioration trend notification unit 22 notifies the information terminal 10 of the equipment efficiency deterioration trend.

[0029] Next, the processing performed by the learning device 11 will be described with reference to Fig. 2 etc. Fig. 2 is a flowchart showing the procedure of the operation performed by the learning device 11 included in the energy-saving activity support system 1 according to the first embodiment. In step S1, the data acquisition unit 13 included in the learning unit 12 of the learning device 11 acquires information obtained by the first power sensor 4, the second power sensor 5, the production volume sensor 6, the environmental sensor 7, and the production management device 8 from the energy management device 9. In Fig. 2, the operation of step S1 is indicated by the phrase "acquire various data collected by the energy management device."

[0030] In the next step S2, the equipment data identification unit 14 of the learning unit 12 of the learning device 11 creates a scatter plot of the power consumption of the monitored equipment, which is showing a tendency toward deteriorating efficiency, and various equipment data consisting of information obtained by the first power sensor 4, the second power sensor 5, the production volume sensor 6, the environmental sensor 7, and the production management device 8 acquired in step S1.

[0031] 3 is a diagram showing an example of a scatter diagram created by the equipment data identification unit 14 of the learning device 11 of the energy-saving activity support system 1 according to embodiment 1. In the scatter diagram of Fig. 3, the horizontal axis represents various equipment data, and the vertical axis represents the amount of power consumed by the equipment to be monitored. As shown in Fig. 3, for example, the equipment data identification unit 14 creates a scatter diagram of various equipment data consisting of first data, second data, and third data, and the amount of power consumed by the equipment to be monitored.

[0032] In the next step S3, the equipment data identifying unit 14 identifies equipment data that has a high correlation with the power consumption of the equipment to be monitored from the scatter diagram created in step S2. In the example of Fig. 3, the equipment data identifying unit 14 identifies the third data as equipment data that has a high correlation with the power consumption of the equipment to be monitored. A high correlation may be determined by a correlation coefficient calculated based on the covariance and standard deviation, or may be determined by using AI (artificial intelligence) such as machine learning.

[0033] The scatter diagram may be expressed in three dimensions instead of two. When the scatter diagram is expressed in three dimensions, an axis of arbitrary data is set in addition to an axis showing the amount of power of the monitored equipment and an axis showing various equipment data. For equipment data that has a high correlation with the amount of power of the monitored equipment, a single piece of equipment data with the highest correlation may be selected, or multiple pieces of equipment data may be selected in descending order of correlation. In this case, in step S13 of FIG. 4 described below, each piece of equipment data is weighted based on the magnitude of the correlation to calculate the deviation.

[0034] In the next step S4, the equipment data identifying unit 14 creates an approximation curve of the equipment data identified in step S3 on the scatter plot. That is, the equipment data identifying unit 14 creates an approximation curve of the equipment data that has a high correlation with the power consumption of the equipment to be monitored on the scatter plot. In the example of Fig. 3, the equipment data identifying unit 14 creates an approximation curve of the third data that has been identified as having a high correlation on the scatter plot.

[0035] Finally, in step S5, the facility-related data storage unit 15 of the learning device 11 stores information indicating the graph including the approximation curve created in step S4 as facility-related data.

[0036] Next, the processing performed by the monitoring device 16 will be described with reference to Fig. 4 and other figures. Fig. 4 is a flowchart showing the procedure of the operation performed by the monitoring device 16 included in the energy-saving activity support system 1 according to the first embodiment. In step S11, the data acquisition unit 17 included in the monitoring device 16 acquires, from the energy management device 9, power amount data of the target equipment being monitored for a deterioration trend in efficiency and equipment data identified in step S3 of Fig. 2 as having a high correlation with the power amount of the equipment being monitored. The number of pieces of data acquired by the data acquisition unit 17 is the number of pieces of data obtained within a preset monitoring interval for a deterioration trend in the equipment's efficiency.

[0037] In the next step S12, the data acquisition unit 17 plots points indicating equipment data that have a high correlation with the amount of power of the equipment to be monitored acquired in step S11 on a graph indicated by the information stored in the equipment-related data storage unit 15 in step S5 in Fig. 2, for the number of acquired data. In Fig. 4, the operation of step S12 is indicated by the phrase "plot acquired data on graph."

[0038] In the next step S13, the deviation calculation unit 18 calculates the deviation of each point plotted in step S12 from the approximation curve. In Fig. 4, the operation of step S13 is indicated by the phrase "calculate the deviation of each acquired data from the approximation curve." Fig. 5 is a diagram showing an example of the deviation calculated by the deviation calculation unit 18 included in the monitoring device 16 of the energy-saving activity support system 1 according to embodiment 1. As shown in Fig. 5, for example, the deviation is the distance in the vertical direction between each point plotted in step S12 and the approximation curve.

[0039] In the next step S14, the equipment efficiency calculation unit 19 first calculates a representative value of the plurality of deviation degrees calculated in step S13 as an equipment efficiency index. The calculated equipment efficiency index is stored in the equipment efficiency storage unit 20. The representative value represents an average value, median value, weighted average value, moving average value, or the like of the plurality of deviation degrees. Next, the equipment efficiency calculation unit 19 plots the equipment efficiency index stored in the equipment efficiency storage unit 20 on a graph in chronological order.

[0040] In the next step S15, the equipment efficiency deterioration trend determination unit 21 determines whether or not the efficiency of the equipment to be monitored is on a deterioration trend, based on the time series graph plotted in step S14. For example, the equipment efficiency deterioration trend determination unit 21 determines that the efficiency of the equipment to be monitored is on a deterioration trend if the equipment efficiency index is less than a preset threshold and the slope of the line connecting the current equipment efficiency index and the previous equipment efficiency index is less than the preset threshold, if the equipment efficiency index is less than the preset threshold, or if the slope of the line connecting the current equipment efficiency index and the previous equipment efficiency index is less than the preset threshold.

[0041] FIG. 6 is a diagram illustrating the determination made by the equipment efficiency deterioration trend determination unit 21 included in the monitoring device 16 of the energy-saving activity support system 1 according to the first embodiment. The vertical axis represents the equipment efficiency index, which is a representative value of the deviation described with reference to FIG. 5 , and the horizontal axis represents time, such as days or months. The representative value of the deviation calculated in step S13 is plotted on a time-series graph as the equipment efficiency index. For example, if the equipment efficiency index is less than a predetermined threshold and the slope of the line connecting the current equipment efficiency index and the previous equipment efficiency index is less than the predetermined threshold, if the equipment efficiency index is less than the predetermined threshold, or if the slope of the line connecting the current equipment efficiency index and the previous equipment efficiency index is less than the predetermined threshold, the equipment efficiency deterioration trend determination unit 21 determines that the efficiency of the equipment being monitored is on a deterioration trend.

[0042] If the equipment efficiency deterioration trend determination unit 21 determines that the efficiency of the equipment to be monitored is on a worsening trend (Yes in S15), the equipment efficiency deterioration trend notification unit 22 notifies the user of the equipment efficiency deterioration trend in step S16. If the equipment efficiency deterioration trend determination unit 21 determines that the efficiency of the equipment to be monitored is not on a worsening trend (No in S15), the operation of the monitoring device 16 ends.

[0043] The process of the flowchart shown in FIG. 4 is repeatedly performed at preset monitoring intervals.

[0044] Next, a specific example of the processing of the deviation calculation unit 18 of the monitoring device 16 will be described with reference to FIG. 7 , and a specific example of the processing of the equipment efficiency calculation unit 19 and the equipment efficiency deterioration trend determination unit 21 of the monitoring device 16 will be described with reference to FIG. 8 .

[0045] Assume that the learning device 11 identifies the equipment to be monitored as an air conditioner, identifies the equipment data that has a high correlation with the amount of electricity consumed by the equipment to be monitored as the outside air temperature, and sets the monitoring interval to one day.

[0046] 7 is a diagram for explaining the calculation performed by the deviation calculation unit 18 included in the monitoring device 16 of the energy-saving activity support system 1 according to the first embodiment. For example, in a graph including an approximation curve for the amount of power consumed by an air conditioner, four points of data for the outside air temperature acquired for March 5th are plotted on the same graph. The deviation calculation unit 18 calculates the distance in the vertical direction between each point and the approximation curve as the deviation. The above-mentioned air conditioner, March 5th, and four points are examples.

[0047] FIG. 8 is a diagram for explaining the operations performed by the equipment efficiency calculation unit 19 and the equipment efficiency deterioration trend determination unit 21 included in the monitoring device 16 of the energy-saving activity support system 1 according to the first embodiment. First, the equipment efficiency calculation unit 19 calculates a representative value of the multiple deviations calculated by the deviation calculation unit 18 as described with reference to FIG. 7 as the equipment efficiency index. In FIG. 8 , the average value of four data points is used as the representative value of the deviations and is used as the equipment efficiency index. Next, the equipment efficiency calculation unit 19 plots the calculated equipment efficiency index for March 5th as the data for March 5th on a time-series graph of the equipment efficiency index. The above-mentioned March 5th is an example. The dates from March 1st to March 5th in FIG. 8 are examples.

[0048] An equipment efficiency index threshold for determining whether the efficiency of the equipment is on a deteriorating trend is set in advance. The equipment efficiency index on March 4th is greater than the equipment efficiency index threshold, but the equipment efficiency index on March 5th is smaller than the equipment efficiency index threshold, so the equipment efficiency deterioration trend determination unit 21 determines that the efficiency of the equipment to be monitored is on a deteriorating trend. The above dates of March 4th and March 5th are examples.

[0049] The energy-saving activity support system 1 according to the first embodiment uses a representative value of the deviation of the equipment data from the approximation curve as an equipment efficiency index, and determines whether the efficiency of the equipment to be monitored is deteriorating based on the equipment efficiency index itself or a trend in the equipment efficiency index. Therefore, when the efficiency of the equipment to be monitored is deteriorating, information indicating that the efficiency of the equipment to be monitored is on a deteriorating trend can be presented. Furthermore, the energy-saving activity support system 1 can present the timing for updating and maintaining the equipment to be monitored. The energy-saving activity support system 1 can provide users with an opportunity to take energy-saving measures.

[0050] Second Embodiment Fig. 9 is a diagram showing the configuration of an equipment efficiency degradation diagnosis device 30 according to a second embodiment. The equipment efficiency degradation diagnosis device 30 has an acquisition unit 31 that acquires in advance the amount of power of a target facility and a plurality of pieces of information data related to the target facility. For example, the target facility is the facility to be monitored in the first embodiment. For example, the plurality of pieces of information data are the first data, second data, and third data shown in Fig. 3 of the first embodiment.

[0051] The equipment efficiency degradation diagnosis device 30 further includes an estimation unit 32 that estimates information data that has a strong correlation with the power consumption of the target equipment from among the plurality of information data acquired by the acquisition unit 31. For example, the information data that has a strong correlation with the power consumption of the target equipment is the third data among the first data, second data, and third data shown in FIG. 3 of the first embodiment. The strong correlation with the power consumption of the target equipment is determined according to a predetermined rule. For example, the strong correlation with the power consumption of the target equipment may be determined by a correlation coefficient calculated based on the covariance and standard deviation, or may be determined by using AI such as machine learning.

[0052] The equipment efficiency degradation diagnosis device 30 further includes a reference calculation unit 33 that calculates in advance an approximation curve that serves as a reference for an equipment efficiency index for determining whether the efficiency of the target equipment has decreased, based on a scatter diagram of the power amount of the target equipment and the information data estimated by the estimation unit 32. An example of the scatter diagram is shown in FIG. 3 of the first embodiment. An example of the approximation curve is the approximation curve shown in FIG. 3.

[0053] The equipment efficiency degradation diagnostic device 30 further includes an equipment efficiency calculation unit 34 that acquires the power amount of the target equipment and operation data that is related to the target equipment, has a strong correlation with the power amount, and is data when the target equipment is in operation, and calculates, as an equipment efficiency index, the degree of deviation of the operation data from the approximation curve calculated by the reference calculation unit 33. The equipment efficiency calculation unit 34 calculates the deviation as, for example, the same as calculated by the deviation calculation unit 18 in the first embodiment, and uses the calculated deviation as the equipment efficiency index. For example, as described in the first embodiment using FIG. 5 , when the operation data is plotted on a graph including an approximation curve with the horizontal axis representing the operation data and the vertical axis representing the power amount of the target equipment, the equipment efficiency calculation unit 34 calculates, as the deviation.

[0054] The equipment efficiency degradation diagnosis device 30 further includes a representative value recording unit 35 that records one equipment efficiency index as a representative value when one equipment efficiency index is calculated by the equipment efficiency calculation unit 34, and records a representative value obtained from the multiple equipment efficiency indexes when multiple equipment efficiency indexes are calculated by the equipment efficiency calculation unit 34. For example, the representative value recording unit 35 includes a storage unit and records the representative value in the storage unit. Alternatively, the equipment efficiency degradation diagnosis device 30 includes a storage unit not included in the representative value recording unit 35, and the representative value recording unit 35 records the representative value in the storage unit not included in the representative value recording unit 35. The storage unit is realized by, for example, a semiconductor memory. When multiple equipment efficiency indexes are calculated by the equipment efficiency calculation unit 34, the representative value is the average value, median value, weighted average value, moving average value, or the like of the multiple equipment efficiency indexes.

[0055] The equipment efficiency degradation diagnostic device 30 further includes a diagnosing unit 36 ​​that determines whether the efficiency of the target equipment has decreased based on the representative value itself or a transition of the representative value recorded by the representative value recording unit 35. For example, as described in the first embodiment with reference to FIGS. 6 and 8 , the diagnosing unit 36 ​​determines that the efficiency of the target equipment has decreased in the following cases: when a plurality of equipment efficiency indices are calculated by the equipment efficiency calculation unit 34, the latest equipment efficiency index is less than a predetermined threshold value and the slope of a line connecting the latest equipment efficiency index and the equipment efficiency index immediately preceding the latest is less than a predetermined threshold value; when one equipment efficiency index is calculated by the equipment efficiency calculation unit 34, the equipment efficiency index is less than a predetermined threshold value; or when a plurality of equipment efficiency indices are calculated by the equipment efficiency calculation unit 34, the slope of a line connecting the latest equipment efficiency index and the equipment efficiency index immediately preceding the latest is less than a predetermined threshold value.

[0056] As described above, the equipment efficiency degradation diagnosis device 30 includes a reference calculation unit 33 that calculates an approximate curve in advance based on a scatter diagram of the power consumption of the target equipment and the information data estimated by the estimation unit 32, an equipment efficiency calculation unit 34 that calculates the degree of deviation of the operation data from the approximate curve as an equipment efficiency index, and a diagnosis unit 36 ​​that determines whether the efficiency of the target equipment has decreased based on a representative value itself based on the equipment efficiency index or a trend in the representative value. Therefore, the equipment efficiency degradation diagnosis device 30 can present a report that the efficiency of the target equipment has decreased when the efficiency of the target equipment has decreased. The meaning of "deterioration" in the second embodiment is substantially the same as the meaning of "deterioration" in the first embodiment.

[0057] Embodiment 3. Figure 10 is a diagram showing the configuration of an energy-saving activity support system 40 according to embodiment 3. The energy-saving activity support system 40 includes a learning device 41 and a monitoring device 45. The learning device 41 includes an acquisition unit 42, an estimation unit 43, and a reference calculation unit 44. The acquisition unit 42 has the same function as the acquisition unit 31 of embodiment 2, the estimation unit 43 has the same function as the estimation unit 32 of embodiment 2, and the reference calculation unit 44 has the same function as the reference calculation unit 33 of embodiment 2. The monitoring device 45 includes an equipment efficiency calculation unit 46, a representative value recording unit 47, and a diagnosis unit 48. The equipment efficiency calculation unit 46 has the same function as the equipment efficiency calculation unit 34 of embodiment 2, the representative value recording unit 47 has the same function as the representative value recording unit 35 of embodiment 2, and the diagnosis unit 48 has the same function as the diagnosis unit 36 ​​of embodiment 2.

[0058] The energy-saving activity support system 40 according to the third embodiment, like the equipment efficiency reduction diagnosis device 30 according to the second embodiment, can present the fact that the efficiency of the target equipment is decreasing when the efficiency of the target equipment is decreasing.

[0059] 11 is a diagram showing a processor 91 when at least some of the functions of the data acquisition unit 13 and the equipment data identification unit 14 of the learning unit 12 of the learning device 11 in the energy-saving activity support system 1 according to the first embodiment are realized by the processor 91. In other words, at least some of the functions of the data acquisition unit 13 and the equipment data identification unit 14 may be realized by the processor 91 that executes a program stored in a memory 92. The processor 91 is a CPU (Central Processing Unit), a processing system, an arithmetic system, a microprocessor, or a DSP (Digital Signal Processor). The memory 92 is also shown in FIG.

[0060] When at least some of the functions of the data acquisition unit 13 and the equipment data identification unit 14 are realized by the processor 91, the at least some of the functions are realized by the processor 91 and software, firmware, or a combination of software and firmware. The software or firmware is written as a program and stored in the memory 92. The processor 91 realizes at least some of the functions of the data acquisition unit 13 and the equipment data identification unit 14 by reading and executing the program stored in the memory 92.

[0061] When at least some of the functions of the data acquisition unit 13 and the equipment data identification unit 14 are realized by the processor 91, the learning unit 12 has a memory 92 for storing a program that results in the execution of at least some of the steps executed by the data acquisition unit 13 and the equipment data identification unit 14. It can also be said that the program stored in the memory 92 causes a computer to execute at least some of the procedures or methods executed by the data acquisition unit 13 and the equipment data identification unit 14.

[0062] The memory 92 may be, for example, a non-volatile or volatile semiconductor memory such as a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, an EPROM (Erasable Programmable Read Only Memory), an EEPROM (registered trademark) (Electrically Erasable Programmable Read-Only Memory), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, or a DVD (Digital Versatile Disk).

[0063] 12 is a diagram showing a processing circuit 93 in a case where at least some of the functions of the data acquisition unit 13 and the equipment data identification unit 14 of the learning unit 12 of the learning device 11 of the energy-saving activity support system 1 according to embodiment 1 are realized by the processing circuit 93. In other words, at least some of the functions of the data acquisition unit 13 and the equipment data identification unit 14 may be realized by the processing circuit 93.

[0064] The processing circuitry 93 is dedicated hardware, and may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a combination thereof.

[0065] Some of the functions of the data acquisition unit 13 and the equipment data identification unit 14 may be realized by dedicated hardware separate from the hardware that realizes the remaining functions of the data acquisition unit 13 and the equipment data identification unit 14.

[0066] With regard to the multiple functions possessed by the data acquisition unit 13 and the equipment data identification unit 14, some of the multiple functions may be realized by software or firmware, and the remaining multiple functions may be realized by dedicated hardware. In this way, the multiple functions possessed by the data acquisition unit 13 and the equipment data identification unit 14 can be realized by hardware, software, firmware, or a combination thereof.

[0067] At least some of the functions of the data acquisition unit 17, the deviation calculation unit 18, the equipment efficiency calculation unit 19, the equipment efficiency deterioration trend determination unit 21, and the equipment efficiency deterioration trend notification unit 22 included in the monitoring device 16 of the energy-saving activity support system 1 according to the first embodiment may be realized by a processor or a processing circuit. The processor is a processor equivalent to the processor 91. The processing circuit is a processing circuit equivalent to the processing circuit 93.

[0068] At least some of the functions of the production equipment 2, related equipment 3, first power sensor 4, second power sensor 5, production volume sensor 6, environmental sensor 7, production management device 8, energy management device 9, and information terminal 10 included in the energy-saving activity support system 1 according to the first embodiment may be realized by a processor or a processing circuit. The processor is a processor equivalent to the processor 91. The processing circuit is a processing circuit equivalent to the processing circuit 93.

[0069] At least some of the functions of the acquisition unit 31, the estimation unit 32, the reference calculation unit 33, the equipment efficiency calculation unit 34, the representative value recording unit 35, and the diagnosis unit 36 ​​included in the equipment efficiency degradation diagnosis device 30 according to the second embodiment may be realized by a processor or a processing circuit. The processor is a processor equivalent to the processor 91. The processing circuit is a processing circuit equivalent to the processing circuit 93.

[0070] At least some of the functions of the acquisition unit 42, estimation unit 43, and reference calculation unit 44 of the learning device 41 of the energy-saving activity support system 40 according to the third embodiment may be realized by a processor or a processing circuit. At least some of the functions of the equipment efficiency calculation unit 46, representative value recording unit 47, and diagnosis unit 48 of the monitoring device 45 of the energy-saving activity support system 40 may be realized by a processor or a processing circuit. The processor is equivalent to the processor 91. The processing circuit is equivalent to the processing circuit 93.

[0071] The configurations shown in the above embodiments are merely examples, and may be combined with other known technologies, or different embodiments may be combined with each other, or part of the configuration may be omitted or modified within the scope of the gist of the invention.

[0072] 1, 40 Energy saving activity support system, 2 Production equipment, 3 Related equipment, 4 First power sensor, 5 Second power sensor, 6 Production volume sensor, 7 Environmental sensor, 8 Production management device, 9 Energy management device, 10 Information terminal, 11, 41 Learning device, 12 Learning unit, 13, 17 Data acquisition unit, 14 Equipment data identification unit, 15 Equipment related data storage unit, 16, 45 Monitoring device, 18 Deviation calculation unit, 19, 34, 46 Equipment efficiency calculation unit, 20 Equipment efficiency storage unit, 21 Equipment efficiency deterioration trend determination unit, 22 Equipment efficiency deterioration trend notification unit, 30 Equipment efficiency decline diagnosis device, 31, 42 Acquisition unit, 32, 43 Estimation unit, 33, 44 Reference calculation unit, 35, 47 Representative value recording unit, 36, 48 Diagnosis unit, 91 Processor, 92 Memory, 93 Processing circuit.

Claims

1. An acquisition unit that acquires in advance the amount of electric power of a target equipment and a plurality of pieces of information data related to the target equipment; an estimation unit that estimates information data that has a strong correlation with the amount of electric power from among the plurality of pieces of information data acquired by the acquisition unit; a reference calculation unit that calculates in advance an approximation curve that serves as a reference for an equipment efficiency index for determining whether or not the efficiency of the target equipment has decreased based on a scatter plot of the amount of electric power and the information data estimated by the estimation unit; an equipment efficiency calculation unit that acquires the amount of electric power of the target equipment and operation data that is related to the target equipment, has a strong correlation with the amount of electric power, and is data when the target equipment is in operation, and calculates the degree of deviation of the operation data from the approximation curve calculated by the reference calculation unit as the equipment efficiency index; and a representative value recording unit that records the one equipment efficiency index as a representative value when one equipment efficiency index is calculated by the equipment efficiency calculation unit, and records a representative value obtained from the plurality of equipment efficiency indexes when multiple equipment efficiency indexes are calculated by the equipment efficiency calculation unit. a diagnosis unit that determines whether or not efficiency of the target equipment is decreasing based on the representative value itself recorded by the representative value recording unit or a change in the representative value.

2. A learning device comprising: an acquisition unit that acquires in advance the amount of power of a target facility and multiple pieces of information data related to the target facility; an estimation unit that estimates information data that has a strong correlation with the amount of power from among the multiple pieces of information data acquired by the acquisition unit; and a reference calculation unit that calculates in advance an approximation curve that serves as a reference for an equipment efficiency index for determining whether or not the efficiency of the target facility is decreasing, based on a scatter plot of the amount of power and the information data estimated by the estimation unit.

3. A monitoring device comprising: an equipment efficiency calculation unit that acquires an amount of power of a target equipment and operation data that is related to the target equipment, has a strong correlation with the amount of power, and is data when the target equipment is in operation, and calculates as the equipment efficiency index the degree of deviation of the operation data from the approximation curve calculated by the reference calculation unit in the learning device described in claim 2; a representative value recording unit that, when one equipment efficiency index is calculated by the equipment efficiency calculation unit, records the one equipment efficiency index as a representative value, and, when multiple equipment efficiency indexes are calculated by the equipment efficiency calculation unit, records a representative value obtained from the multiple equipment efficiency indexes; and a diagnosis unit that determines whether the efficiency of the target equipment is decreasing based on the representative value itself recorded by the representative value recording unit or a change in the representative value.

4. An energy saving activity support system comprising: the learning device according to claim 2; and the monitoring device according to claim 3.