Cooling capacity index estimation device

Abstract

To provide a cooling capacity index estimation device that is not easily affected by seasonality.SOLUTION: A cooling capacity index estimation device of an embodiment of the present invention comprises a data acquisition unit for acquiring observation data including vehicle data related to the operation of a railway vehicle and air conditioner specifications data related to the specifications of an air conditioner. The cooling capacity index estimation device also includes a feature data calculation unit for calculating feature data that are the values calculated from the observation data in the width of prescribed time. In addition, the cooling capacity index estimation device includes a heat load model processing unit for creating a heat load model, which is a model formula expressing an amount of change of the in-car temperature risen by a heat load in the railway vehicle, on the basis of the feature data. Furthermore, the cooling capacity index estimation device includes a cooling capacity index calculation unit for calculating a cooling capacity index, which is an index of cooling capacity, on the basis of the heat load model and the feature data.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] Embodiments of the present invention relate to a cooling capacity index estimation device.

Background Art

[0002] The ability of an air conditioner to cool a space per unit time is generally called the cooling capacity. This cooling capacity is used as an index for diagnosing the deterioration of the air conditioner. As the deterioration of the air conditioner progresses, the cooling capacity decreases, and by grasping this decreasing trend, it becomes possible to efficiently perform maintenance such as inspection, cleaning, and parts replacement of the air conditioner.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] As a method for estimating the cooling capacity, a method is known in which a sensor is attached to the refrigerant pipe of an air conditioner, and the cooling capacity is estimated from the amount of heat exchanged in the indoor heat exchanger.

[0005] Since this method requires attaching a sensor to the refrigerant pipe, it may be difficult to estimate the cooling capacity of an existing air conditioner.

[0006]

[0007] Also, as a method for estimating the cooling capacity of an existing air conditioner, from a dataset associating the temperature difference inside a railway vehicle in a plurality of time segments with the operating state of the compressor of the air conditioner, etc., using the relationship of heat balance generated in the vehicle, a method for estimating an index related to the cooling capacity is also known.In this method, due to the seasonal variation in the air-conditioning operation rate and the outside air temperature, the observed values of the data vary, and there is a possibility that the estimated values are affected by seasonality.

[0008] Therefore, an embodiment of the present invention provides a cooling capacity index estimation device that is less affected by seasonality.

Means for Solving the Problems

[0009] According to one embodiment, the cooling capacity index estimation device includes a data acquisition unit that acquires vehicle data related to the running of a railway vehicle and air-conditioning specification data related to the specifications of an air-conditioning device as observed data. Further, the cooling capacity index estimation device includes a feature data calculation unit that calculates feature data, which is a calculated value within a predetermined time width, calculated from the observed data. Further, the cooling capacity index estimation device includes a heat load model processing unit that creates a heat load model, which is a model formula of the amount of change in the vehicle interior temperature that rises due to the heat load inside the railway vehicle, based on the feature data. Further, the cooling capacity index estimation device includes a cooling capacity index calculation unit that calculates a cooling capacity index, which is an index of the cooling capacity, based on the heat load model and the feature data.

Brief Description of the Drawings

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[0011] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. This embodiment does not limit the present invention. The drawings are schematic or conceptual, and the ratios of each part are not necessarily the same as those in reality. In the specification and drawings, the same reference numerals are given to the same elements as those described above with respect to the existing drawings, and the detailed description is omitted as appropriate.

[0012] (First Embodiment) FIG. 1 is a block diagram of the cooling capacity index estimation device 20 in the first embodiment.

[0013] The cooling capacity index estimation device 20 in this embodiment estimates an index related to the cooling capacity (hereinafter, the cooling capacity index) using sensor measurement values installed in a railway vehicle (hereinafter, also simply referred to as the vehicle 10) and data related to the specifications of the air conditioner 40.

[0014] For example, the cooling capacity index can be used as an index of the deterioration of the cooling capacity of the air conditioner installed in the vehicle 10. The maintenance worker of the vehicle 10 can confirm the cooling capacity index estimated by the cooling capacity index estimation device 20 and efficiently perform maintenance such as inspection, cleaning, or component replacement of the air conditioner 40 by grasping the downward trend of the cooling capacity.

[0015] The cooling capacity index estimation device 20 is installed, for example, at the vehicle base 50, and the estimated cooling capacity index is displayed on a display unit 30 such as a monitor. Thereby, a user such as a maintenance worker of the vehicle 10 can check the cooling capacity index.

[0016] The cooling capacity index estimation device 20 includes a data storage unit 21, a data acquisition unit 25, a feature data calculation unit 26, a heat load model processing unit 27, a cooling capacity index calculation unit 28, and a data output unit 29. Further, the cooling capacity index estimation device 20 can be realized, for example, by installing a program for the cooling capacity index estimation device 20 in a PC (Personal Computer). When the CPU (Central Processing Unit) in the cooling capacity index estimation device 20 executes the program of the cooling capacity index estimation device 20, the functions of the data storage unit 21, the data acquisition unit 25, the feature data calculation unit 26, the heat load model processing unit 27, the cooling capacity index calculation unit 28, and the data output unit 29 are realized. Further, for example, the data storage unit 21 is constructed on an auxiliary storage device such as an HDD (Hard Disk Drive).

[0017] In this example, in the data storage unit 21 of the cooling capacity index estimation device 20, a database for storing vehicle data 22, air conditioning specifications data 23, and a heat load model 24 is constructed.

[0018] The vehicle data 22 is data related to the running of the vehicle 10, such as train speed, operation information regarding the running station interval, control values of mounted devices, sensor measurement values, etc. In this example, a data collection unit 11 mounted on the vehicle 10 collects these data and transmits them to the data storage unit 21. The data storage unit 21 receives and stores the vehicle data 22 collected by the data collection unit 11 via the network 60. Here, the description of network devices such as a network interface for communicating between the cooling capacity index estimation device 20 and the data collection unit 11 is omitted. The network 60 can use, for example, a public line such as an Internet line, or a private communication network.

[0019] The air-conditioning specification data 23 is data related to the specifications of the air-conditioning device, such as the number of installed air-conditioning devices 40 in the vehicle 10, the number of compressors of the air-conditioning device 40, and information regarding the operation mode, etc. In this example, the data collection unit 11 installed in the vehicle 10 collects these data and transmits them to the data storage unit 21. The data storage unit 21 receives and stores the air-conditioning specification data 23 collected by the data collection unit 11 via the network 60. Also, the air-conditioning specification data 23 may not be received from the data collection unit 11, but instead, for example, data imported from a CSV (comma-separated variables) file or data imported by user input may be used.

[0020] Hereinafter, for the sake of explanation, the vehicle data 22 and the air-conditioning specification data 23 are collectively referred to as observation data.

[0021] The heat load model 24 is a model that estimates the amount of change in the vehicle interior temperature caused by the heat load based on the vehicle data 22 and the air-conditioning specification data 23. In this example, this heat load model 24 is estimated for each vehicle number of the vehicle 10.

[0022] The data acquisition unit 25 acquires data from the data storage unit 21. More specifically, the data acquisition unit 25 acquires the vehicle data 22 and the air-conditioning specification data 23 for calculating the feature data from the data storage unit 21.

[0023] The feature data calculation unit 26 calculates the feature data to be input to the heat load model processing unit 27 and the cooling capacity index calculation unit 28. Here, the feature data refers to processed data used for the calculation of the heat load model 24 and the cooling capacity index, and is represented as a calculated value calculated from the observation data, such as the amount of change in temperature within a predetermined time span and the average value of the occupancy rate.

[0024] The heat load model processing unit 27 creates a heat load model 24 for each vehicle number of the vehicle 10 with the characteristic data as input. Further, when the characteristic data can be collected for a predetermined period or the like, the heat load model processing unit 27 updates the already created heat load model 24 based on a predetermined condition. Further, when the characteristic data for creating the heat load model 24 cannot be sufficiently collected, the heat load model 24 in another vehicle number of the already created vehicle 10 can be substituted as the heat load model 24 in the corresponding vehicle number of the vehicle 10. In the present embodiment, an example using the temperature difference between the inside and outside of the vehicle (hereinafter, the temperature difference between the inside and outside of the vehicle) and the occupancy rate as the characteristic data will be described. Further, the heat load model processing unit 27 stores the created, updated or substituted heat load model 24 in the data storage unit 21.

[0025] The cooling capacity index calculation unit 28 calculates an estimated value of the cooling capacity index using the characteristic data and the heat load model 24. In the present embodiment, as the cooling capacity index, an example using the amount of temperature change obtained by subtracting the vehicle interior temperature that rises due to the heat load from the vehicle interior temperature that changes due to the operation of the air conditioner 40 (the compressor is in the on state) in a predetermined time width (characteristic section) used in the calculation of the characteristic data will be described.

[0026] The data output unit 29 outputs the estimated value of the cooling capacity index calculated by the cooling capacity index calculation unit 28 to the display unit 30.

[0027] The display unit 30 is a display such as an organic EL display or a liquid crystal display, and displays the estimated value of the cooling capacity index output from the data output unit 29. Further, in this example, the cooling capacity index estimation device 20 shows an example of displaying the calculated estimated value of the cooling capacity index on the display unit 30, but the estimated value of the cooling capacity index may be presented to the user by another method such as lighting a display lamp or sounding an alarm buzzer according to the estimated value of the cooling capacity index.

[0028] FIG. 2 is another block diagram of the cooling capacity index estimation device 20 in the first embodiment.

[0029] In this example, the cooling capacity index estimation device 20 is set in the data center 70 instead of the vehicle base 50. Different from the example in FIG. 1, the data output from the data output unit 29 is displayed on the display unit 30 installed in the vehicle base 50 via the network 60.

[0030] Also, the calculation of the cooling capacity index by the cooling capacity index estimation device 20 may be provided as a cloud service as in the example of FIG. 2. In this case, the cooling capacity index estimation device 20 has a role as a cloud server that calculates an estimated value of the cooling capacity index by receiving vehicle data 22 and air conditioning specifications data 23 from the vehicle 10 via the network 60 and provides it to the user. Hereinafter, the cooling capacity index estimation device 20 will be described by taking the configuration of FIG. 1.

[0031] FIG. 3 shows an example of the operating period of the air conditioner 40 and the observed data at that time.

[0032] FIG. 3(a) shows the aggregated values of the average in-vehicle temperature, the average outside air temperature, the average in-vehicle and outside air temperature difference, and the air conditioning operation rate corresponding to the data sets divided by each month in the range of July to September during the summer period. In this example, July and September have an outside air temperature of 29 to 30 °C and a medium air conditioning operation rate, while August has a high outside air temperature of 33 °C and a high air conditioning operation rate. In such a case, in August, the outside air temperature is high during the day, and since the compressor continues to operate constantly, the data is concentrated on the data with a large in-vehicle and outside air temperature difference, and the number of observations of the data with a small in-vehicle and outside air temperature difference decreases. FIG. 3(b) is a diagram showing the relationship between the in-vehicle and outside air temperature difference and the change amount of the in-vehicle temperature for each data set divided by each month in such a case. Also, FIG. 3(c) is a diagram showing the relationship without dividing the entire data from July to September.

[0033] The triangular plots in Fig. 3(b) show the data from July 1st to July 31st, and the dashed line indicates the regression line. Also, the circular plots show the data from August 1st to August 31st, and the solid line indicates the regression line. Further, the cross plots show the data from September 1st to September 30th, and the dotted line indicates the regression line. Fig. 3(c) has the same types of plots as Fig. 3(b), but shows the regression line for all the data from July 1st to September 30th as a solid line.

[0034] In the dataset divided by month shown in Fig. 3(b), in August, due to fewer data with a small temperature difference between inside and outside the vehicle, an event occurs where the amount of change in the inside vehicle temperature when the temperature difference between inside and outside the vehicle is small appears larger compared to July and September. Generally, it is assumed that the amount of heat generated by the temperature difference between inside and outside the vehicle is the same regardless of the time. Therefore, since there is a difference in the amount of change in the inside vehicle temperature depending on the time, when calculating the estimated value of the cooling capacity index using the dataset divided by month, the variation in the estimated value becomes large, making it difficult to correctly grasp the trend.

[0035] On the other hand, in the dataset that does not divide the data from July to September shown in Fig. 3(c), it shows the relationship between the temperature difference between inside and outside the vehicle and the amount of change in the inside vehicle temperature in a form that includes data with a small and a large temperature difference between inside and outside the vehicle. In this embodiment, by using the heat load model 24 for all such data, a cooling capacity index that is less affected by seasonality is estimated.

[0036] Fig. 4 is an example of vehicle data 22 collected by the cooling capacity index estimation device 20 in the first embodiment.

[0037] In this example, the air conditioning capacity index estimation device 20 holds the collected vehicle data 22 in the data storage unit 21 as a database. Also, in this figure, as part of the vehicle data 22, the average values of the train speed (km / h) per second, the departure station, the arrival station, the operation state of the No. 1 car air conditioner compressor, and the interior temperature of the No. 1 car (°C) are shown. The collection date and time in the first column indicates the date and time information when each vehicle data 22 was collected, and the columns after the second column show the vehicle data 22 corresponding to each collection date and time. In this example, the average value is used as the interior temperature of the No. 1 car, but other values such as the maximum value or the minimum value may also be used.

[0038] For example, in the data at 13:00:00 on February 1, 2023, the train speed is shown to be 12.5 km / h, the departure station at this time is STA24, and the arrival station is shown to be STA25. Also, the compressor state of the No. 1 car at this time is shown to be 1 (on), and the interior temperature is shown to be 26.2 °C. Also, in this figure, the state of the compressor being 0 indicates that the compressor is off.

[0039] Figure 5 is an example of the characteristic data calculated by the air conditioning capacity index estimation device 20 in the first embodiment.

[0040] In this example, the air conditioning capacity index estimation device 20 displays the calculated characteristic data as a database. The calculated characteristic data may be stored in the data storage unit 21. Also, in this example, when calculating the characteristic data, an interval of 30 seconds is adopted as a predetermined time width. The date and time in the figure indicates the start date and time of the interval when the characteristic data was calculated. That is, the data for 30 seconds from this date and time is calculated as a set of characteristic data. The formation and car number are the information of the vehicle 10 corresponding to the data. The inter-station section is the section where the vehicle 10 is running. For example, STA16_STA17 indicates that the departure station is STA16 and the arrival station is STA17 and the vehicle is running.

[0041] The temperature difference inside the vehicle is the difference between the temperature inside the vehicle at the start time and the temperature inside the vehicle at the end time in the characteristic section. The occupancy rate, the temperature inside the vehicle, the outside air temperature, and the humidity inside the vehicle are information obtained by collecting each measured value in the characteristic section, and each value is shown as an average value. The temperature difference between the inside and outside of the vehicle is the difference between the temperature inside the vehicle collected in the characteristic section and the outside air temperature. The compressor operation is a value representing the compressor operation state in the characteristic section in the same manner as described above, where 0 indicates the off state and 1 indicates the on state. In this example, average values are used for the temperature difference inside the vehicle, the occupancy rate, the temperature inside the vehicle, the outside air temperature, and the humidity inside the vehicle, but other values such as the maximum value and the minimum value may also be used.

[0042] For example, the characteristic data of car No. 1 in formation F1000 from 15:34:10 on July 1, 2022 for 30 seconds indicates that the section between stations is STA16_STA17, the departure station is STA16, and the arrival station is STA17.

[0043] Also, the temperature difference inside the vehicle is shown to be 0.23 °C. Also, the average values of the occupancy rate, the temperature inside the vehicle, the outside air temperature, and the humidity inside the vehicle are shown to be 45%, 24.3 °C, 32.0 °C, and 55 °C respectively. The temperature difference between the inside and outside of the vehicle is shown to be 7.7 °C. Also, the compressor operation is 0, indicating the off state.

[0044] Figure 6 is a diagram for explaining the relationship between the heat load generated in vehicle 10 and the heat load model 24.

[0045] Generally, the heat generated in vehicle 10 includes the amount of heat flowing into vehicle 10 due to the temperature difference between the inside and outside of the vehicle, the amount of heat generated by the passengers in vehicle 10, the amount of heat flowing into vehicle 10 due to solar radiation, the amount of heat generated by equipment heat generation in vehicle 10, and the amount of heat flowing in and out due to ventilation, etc.

[0046] Figure 6 shows the change in the temperature inside the vehicle when the compressor is in the off stable state. In Figure 6, Time start and Time end respectively indicate the start time and the end time when the characteristic data was calculated. T start and T endshows the in-vehicle temperature at the start time and the end time, respectively. The model formula of the heat load model 24 is ΔT OFF Assuming that OFF ΔT start is the difference between end T OFF and T OFF and represents the in-vehicle temperature difference obtained by taking the difference between them, which is expressed as the temperature rise due to the heat load. At this time,

[0047] ΔT

[0048] is expressed by Equation (1). In Equation (1), as an example, the in-vehicle and out-vehicle temperature difference and the occupancy rate use the average values in the characteristic section, and the characteristic section is set as the unit time ΔT. ΔT OFF = α × in-vehicle and out-vehicle temperature difference + β × occupancy rate + ε (1)

[0049] Figure 7 is a flowchart for creating, updating, and replacing the heat load model 24 in the first embodiment.

[0050] In this flowchart, the characteristic data calculation unit 26 will be described as having already calculated the characteristic data for a predetermined period based on the vehicle data 22 and the air conditioning specifications data 23 acquired by the data acquisition unit 25. Also, in this flowchart, the characteristic data will be described using the characteristic data shown in Figure 5.

[0051] In step S011, the heat load model processing unit 27 acquires a list of vehicle types, formations, and car numbers from the calculated feature data. This list serves as information for managing the heat load model 24. In this example, the air-conditioning capacity index estimation device 20 manages the heat load model 24 with information up to the car number, that is, manages the heat load model 24 in units of cars of the vehicle 10. In step S012, the heat load model processing unit 27 acquires vehicle information related to the car, such as the vehicle type class, formation form, and car number car of the vehicle 10 that is the target for creating, updating, or replacing the heat load model 24 from the acquired list.

[0052] In step S013, the heat load model processing unit 27 checks whether there is a heat load model 24 corresponding to the vehicle information acquired in step S012. The heat load model processing unit 27 reads the database of the heat load model 24 stored in the data storage unit 21 and checks whether there is no corresponding heat load model 24. If the heat load model 24 does not exist (Yes in step S013), in step S014, the heat load model processing unit 27 determines whether the creation conditions of the heat load model 24 are satisfied. Here, the purpose of determining whether the heat load model processing unit 27 satisfies the creation conditions of the heat load model 24 is to check whether there is no difference in data observation values due to seasonality. The heat load model processing unit 27 checks, as the vehicle data 22, whether data of a certain determination threshold value or more (for example, data volume equivalent to 20 days per month or more) can be collected not only for a specific period such as only August but also for the summer period (for example, from July to September) when air-conditioning is used. If the data can be collected, the heat load model 24 is created.

[0053] The creation conditions of the heat load model 24 are not limited to the above-described example and can be various conditions. For example, the creation conditions of the heat load model 24 may be determined based on the distribution of data used for creation (the number of data at each temperature level with a temperature difference between inside and outside the vehicle being 1000 data or more, etc.).

[0054] When it is determined that the creation conditions of the heat load model 24 are satisfied (Yes in step S014), in step S015, the heat load model processing unit 27 creates the heat load model 24 corresponding to the acquired vehicle information. As the feature data used when creating the heat load model 24, any data can be used from the data that satisfies the creation conditions of the heat load model 24. For example, when creating the heat load model 24 for each year, it is conceivable to create the heat load model 24 using the feature data from July to September of that year. In step S016, the heat load model processing unit 27 stores and updates the created heat load model 24 in the database in the data storage unit 21. The created heat load model 24 is then used by the cooling capacity index calculation unit 28.

[0055] In step S017, the heat load model processing unit 27 determines whether the processing (creation, update, or replacement) of the heat load model 24 has been completed for all the cars of the target vehicles 10 among the list of vehicle types class, formations form, and car numbers car. If all the processing has been completed (Yes in step S017), the heat load model processing unit 27 ends the processing. If all the processing has not been completed (No in step S017), it returns to step S012, and the heat load model processing unit 27 acquires the next vehicle information from the list.

[0056] Next, the update flow of the heat load model 24 will be described. Even if the heat load model 24 for the target vehicle information has been created in the past and satisfies the update conditions, the heat load model processing unit 27 updates the heat load model 24 using new feature data. Since the flow up to steps S011 to S012 is the same as when creating the heat load model 24, the description is omitted.

[0057] In step S013, the heat load model processing unit 27 reads the database of the heat load model 24 stored in the data storage unit 21, and checks whether there is a corresponding heat load model 24 among the existing heat load models 24. If the heat load model 24 exists (No in step S013), in step S018, the heat load model processing unit 27 determines whether the update condition of the heat load model 24 is satisfied. Here, the heat load model processing unit 27 may determine using a predetermined threshold value as the update condition of the heat load model 24. For example, the heat load model processing unit 27 divides the dataset period used for the created heat load model 24 by year, and when the observation data for the entire summer period (for example, July to September) of this year is complete, the observation data for this year is used as the observation data in the new data period. The heat load model processing unit 27 compares the collection amount of the observation data in the period used in the created heat load model 24 with the collection amount of the observation data in the new data period, and if new data can be collected, it determines that the update condition of the heat load model 24 is satisfied.

[0058] When the update condition of the heat load model 24 is satisfied (Yes in step S018), the heat load model processing unit 27 creates and updates the heat load model 24 according to the flow of steps S015 and S016. When the update condition of the heat load model 24 is not satisfied (No in step S018), in step S019, the heat load model processing unit 27 acquires the heat load model 24 of the corresponding vehicle information from the database. The acquired heat load model 24 is then used by the cooling capacity index calculation unit 28.

[0059] After the update of the heat load model 24 in step S016 or after the acquisition of the heat load model 24 in step S019, in step S017, the heat load model processing unit 27 determines whether the processing (creation, update, or replacement) of the heat load model 24 has been completed for all the cars of all the vehicles 10 among the list of vehicle types class, formations form, and car numbers car. If all the processing has been completed (Yes in step S017), the heat load model processing unit 27 ends the processing. If all the processing has not been completed (No in step S017), it returns to step S012, and the heat load model processing unit 27 acquires the next vehicle information to be processed for the heat load model 24 from the list.

[0060] Next, the replacement flow of the heat load model 24 will be described. Generally, the heat load generated in the vehicle 10 is considered to be equivalent if the temperature difference between the inside and outside of the vehicle is the same and the material and area of the wall are the same, and the heat load generated by the passengers is also equivalent regardless of the vehicle 10. Therefore, even if the creation conditions of the heat load model 24 are not satisfied, the heat load model processing unit 27 can replace the heat load model 24 if the vehicle types are the same. Since the flow from steps S011 to S013 is the same as when creating the heat load model 24, the description is omitted.

[0061] When it is determined by the heat load model processing unit 27 that the creation conditions of the heat load model 24 are not satisfied (No in step S014), in step S020, the heat load model processing unit 27 reads out the database of the heat load model 24 stored in the data storage unit 21 as an alternative heat load model 24 and checks whether there is a heat load model 24 of the same vehicle type class and the same car number car as the target vehicle 10. If there is an alternative heat load model 24 (Yes in step S020), in step S021, the heat load model processing unit 27 acquires the alternative heat load model 24 in the same vehicle type class and the same car number car from the database. The acquired heat load model 24 is used by the cooling capacity index calculation unit 28 hereafter.

[0062] If there is no alternative heat load model 24 (No in step S020), the heat load model processing unit 27 does not perform substitution of the heat load model 24. In this case, the heat load model processing unit 27 may output an error.

[0063] After obtaining the heat load model 24 from the database in step S021, in step S017, the heat load model processing unit 27 determines whether the processing (creation, update, or substitution) of the heat load model 24 has been completed for all vehicles 10 among the vehicle type class, formation form, and car number list. If all the processing has been completed (Yes in step S017), the heat load model processing unit 27 ends the processing. If all the processing has not been completed (No in step S017), it returns to step S012, and the heat load model processing unit 27 obtains the next vehicle information to be processed for the heat load model 24 from the list.

[0064] FIG. 8 is a diagram for explaining the relationship between the temperature change of the vehicle 10 and the cooling capacity index.

[0065] ΔT ON is the temperature difference inside the vehicle obtained by taking the difference between T start and T end and is the temperature inside the vehicle that changes when the compressor is in the on-stable state. ΔT CP is the temperature change amount ΔT ON obtained by subtracting the temperature change amount ΔT OFF pred of the vehicle interior that rises due to the heat load from ΔT

[0066] FIG. 9 is a flowchart during the calculation of the cooling capacity index in the first embodiment.

[0067] In step S111, the cooling capacity index calculation unit 28 acquires a list of vehicle type class, formation form, and car number from the calculated feature data. The cooling capacity index is calculated in units of the heat load model 24, that is, in units of each car of the vehicle 10. In step S112, the cooling capacity index calculation unit 28 acquires vehicle information such as the vehicle type class, formation form, and car number of the vehicle 10 for which the cooling capacity index is to be calculated from the acquired list. Further, the cooling capacity index calculation unit 28 acquires the heat load model 24 corresponding to this vehicle information from the heat load model processing unit 27.

[0068] In step S113, the cooling capacity index calculation unit 28 calculates the temperature change amount ΔT due to the heat load OFF pred . ΔT OFF pred is expressed by Equation (3) and is calculated by applying data on the temperature difference between the inside and outside of the vehicle and the occupancy rate as feature data to the model formula of the heat load model 24 in Equation (1). Also, in step S114, the cooling capacity index calculation unit 28 calculates the cooling capacity index ΔT CP . The cooling capacity index ΔT CP is expressed by Equation (4) and is calculated by subtracting the temperature change amount ΔT due to the heat load ON from the temperature difference ΔT inside the vehicle OFF pred . ΔT OFF pred = α temperature difference between inside and outside of the vehicle + β occupancy rate + ε (3) ΔT cp = ΔT ON - ΔT OFF pred (4)

[0069] In step S115, the cooling capacity index calculation unit 28 determines whether the calculation of the cooling capacity index has been completed for all vehicle information among the list of vehicle types, formations, and car numbers. If all the processes are completed (Yes in step S115), the cooling capacity index calculation unit 28 ends the process. If all the processes are not completed (No in step S115), it returns to step S112, and the cooling capacity index calculation unit 28 acquires the next vehicle information to be processed by the heat load model 24 from the list.

[0070] According to this embodiment, the cooling capacity index estimation device 20 creates a heat load model 24 that does not include the difference in data observation values due to seasonality, using the characteristic data calculated from the vehicle data 22 and the air conditioning specifications data of the air conditioning device 40. Further, the cooling capacity index estimation device 20 can calculate an estimated value of the cooling capacity index that is less affected by seasonality by estimating the cooling capacity index using this heat load model 24.

[0071] Also, according to this embodiment, since the cooling capacity index estimation device 20 calculates an estimated value of the cooling capacity index that is less affected by seasonality, the maintenance worker of the vehicle 10 can grasp the decreasing trend of the cooling capacity and efficiently perform maintenance such as inspection, cleaning, or component replacement of the air conditioning device 40.

[0072] Also, according to this embodiment, there is no need to attach a sensor to the refrigerant pipe of the air conditioning device 40, and the cooling capacity index estimation device 20 can calculate an estimated value of the cooling capacity index of the existing air conditioning device 40.

[0073] (Second Embodiment) FIG. 10 is a diagram showing the transition of the cooling capacity index ΔT cp and the absolute humidity AH in the second embodiment.

[0074] In this embodiment, when humidity is obtained as the vehicle data 22, the cooling capacity index estimation device 20 estimates the cooling capacity index in consideration of the load corresponding to the latent heat (latent heat load) during cooling.

[0075] In the first embodiment, the cooling capacity index ΔT cp is calculated by subtracting the heat load estimated based on the temperature difference between the inside and outside of the vehicle and the occupancy rate in the characteristic section. In this embodiment, the cooling capacity index estimation device 20 further calculates the humidity in the characteristic section as characteristic data, and calculates the cooling capacity index ΔT cp taking this value into consideration.

[0076] The humidity inside the vehicle is often measured by a sensor for relative humidity. In this embodiment, the cooling capacity index estimation device 20 will explain an example of using the absolute humidity calculated from the vehicle interior temperature and the vehicle interior relative humidity using an approximate formula. However, the cooling capacity index estimation device 20 may use not only the absolute humidity but also the relative humidity.

[0077] FIG. 10(a) shows the cooling capacity index ΔT cp in the characteristic section of each characteristic data. Even when the influence of the heat load estimated based on the temperature difference between the inside and outside of the vehicle and the occupancy rate is subtracted, the value of the cooling capacity index ΔT cp may not be constant. FIG. 10(b) shows the transition of the absolute humidity AH (average value) calculated from the average value of the absolute humidity in each characteristic section. In this example, it shows that when the absolute humidity AH is high, the temperature drop of the cooling capacity index ΔT cp becomes smaller. Also, generally, when the vehicle interior temperature is high, the evaporation of moisture inside the vehicle is promoted, which affects the latent heat load. Similarly, when the outside air temperature is high, the air is more likely to hold water vapor, and the humidity is likely to be high, which affects the latent heat load. The amount of temperature change given by such absolute humidity, vehicle interior temperature, and outside air temperature to the cooling capacity index ΔT cp is estimated using Equation (5). In Equation (5), as an example, the absolute humidity, vehicle interior temperature, and outside air temperature use the average values in the characteristic section, and the characteristic section is taken as the unit time ΔT. ΔT cp = γ absolute humidity + δ vehicle interior temperature + ζ outside air temperature + ε (5)

[0078] At this time, the coefficient γ represents the cooling capacity index (°C) that changes per 1 (g / m^3) of absolute humidity per unit time ΔT, the coefficient δ represents the cooling capacity index (°C) that changes per 1 (°C) of the vehicle interior temperature per unit time ΔT, and the coefficient ζ represents the cooling capacity index (°C) that changes per 1 (°C) of the outside air temperature per unit time ΔT. Also, the coefficient ε represents the cooling capacity index (°C) that changes per unit time ΔT due to other factors.

[0079] The coefficients γ, δ, ζ, and ε may be determined such that the sum of squares of Equation (6), which is obtained by subtracting the right side from the left side of Equation (5), is minimized using the concept of a linear regression model. Thus, using the parameters estimated by Equation (5) and the absolute humidity, vehicle interior temperature, and outside air temperature of the feature data, the cooling capacity index ΔT cp taking into account the load corresponding to the latent heat is calculated. (Left side) - (Right side) = ΔT cp - (γ absolute humidity + δ vehicle interior temperature + ζ outside air temperature + ε) (6)

[0080] Also, when not all data of the absolute humidity, vehicle interior temperature, or outside air temperature are available, the temperature change amount given to the cooling capacity index ΔT cp may be estimated. Specifically, it is conceivable to calculate using Equations (7) to (12). ΔT cp = γ absolute humidity + ε (7) ΔT cp = δ vehicle interior temperature + ε (8) ΔT cp = ζ outside air temperature + ε (9) ΔT cp = γ absolute humidity + δ vehicle interior temperature + ε (10) ΔT cp = γ absolute humidity + ζ outside air temperature + ε (11) ΔT cp = δ vehicle interior temperature + ζ outside air temperature + ε (12)

[0081] According to this embodiment, when the relative humidity or absolute humidity inside the vehicle is obtained, the cooling capacity index estimation device 20 can calculate characteristic data from these data, and consider the load corresponding to the latent heat during cooling, thereby estimating a cooling capacity index that is less affected by seasonality.

[0082] FIG. 11 is a hardware configuration diagram of the cooling capacity index estimation device 20 in the first and second embodiments.

[0083] The cooling capacity index estimation device 20 in FIG. 11 includes a processor 52 such as a CPU, a main storage device 53 such as a RAM, an auxiliary storage device 54 such as an HDD, a network interface 55 such as a LAN (Local Area Network) board, a device interface 56 such as a memory slot or a memory port, and a bus 57 that connects these devices to each other. The cooling capacity index estimation device 20 is, for example, a computer such as a PC, and includes an external input device such as a keyboard or a mouse, and a display device 30 such as an LCD (Liquid Crystal Display) monitor.

[0084] In this embodiment, a program for causing a computer to execute the information processing of the cooling capacity index estimation device 20 is installed in the auxiliary storage device 54. The cooling capacity index estimation device 20 loads this program into the main storage device 53 and executes it by the processor 52. Thereby, the functions of the data storage unit 21, the data acquisition unit 25, the characteristic data calculation unit 26, the heat load model processing unit 27, the cooling capacity index calculation unit 28, and the data output unit 29 shown in FIG. 1 are realized in the cooling capacity index estimation device 20, and the calculation of the estimated value of the cooling capacity index described in the first and second embodiments becomes possible. Note that the data generated by this information processing is temporarily held in the main storage device 53 or stored in the auxiliary storage device 54.

[0085] Also, the data storage unit 21 is constructed on the auxiliary storage device 54. The above-described threshold values are stored in the auxiliary storage device 54. The threshold values are loaded into the main storage device 53 when this program is executed.

[0086] Further, the cooling capacity index estimation device 20 is connected to the network 60 via the network interface 55. Also, the cooling capacity index estimation device 20 controls the network interface 55 and acquires vehicle data 22 and air conditioning specification data 23 from the external data collection unit 11.

[0087] The program for the cooling capacity index estimation device 20 can be installed, for example, by attaching an external device 58 recording this program to the device interface 56 and storing this program from the external device 58 in the auxiliary storage device 54. Examples of the external device 58 are a computer-readable recording medium and a recording device incorporating such a recording medium. Examples of the recording medium are CD-ROM (Compact Disk Read Only Memory), CD-R (Compact Disk Recordable), flexible disk, DVD-ROM (Digital Versatile Disk Read Only Memory), DVD-R (Digital Versatile Disk Recordable), and an example of the recording device is an HDD. Also, this program can be installed, for example, by downloading this program via the network interface 55.

[0088] According to this embodiment, it becomes possible to realize the functions of the cooling capacity index estimation device 20 in the first and second embodiments by software.

[0089] As described above, several embodiments have been explained, but these embodiments are presented only as examples and are not intended to limit the scope of the invention. The novel cooling capacity index estimation device 20 described in this specification can be implemented in various other forms. Also, various omissions, substitutions, and changes can be made to the form of the cooling capacity index estimation device 20 described in this specification without departing from the gist of the invention. The scope of the appended claims and the equivalents thereof are intended to include such forms and modifications included in the scope and gist of the invention.

Description of Symbols

[0090] 10: Vehicle, 11: Data collection unit, 20: Cooling capacity index estimation device, 21: Data storage unit, 22: Vehicle data, 23: Air conditioning specifications data, 24: Heat load model, 25: Data acquisition unit, 26: Feature data calculation unit, 27: Heat load model processing unit, 28: Cooling capacity index calculation unit, 29: Data output unit, 30: Display unit, 40: Air conditioning device, 50: Vehicle base, 60: Network, 70: Data center

Claims

1. a data acquisition unit that acquires vehicle data related to the running of the railway vehicle and air conditioning specification data related to the specifications of the air conditioning device as observation data; a feature data calculation unit that calculates feature data, which is a calculated value for a predetermined time width, from the observation data; a heat load model processing unit that creates a heat load model, which is a model equation of an amount of change in an interior temperature caused by a heat load inside the railway vehicle, based on the feature data; A cooling capacity index calculation unit is provided that calculates a cooling capacity index, which is an index of cooling capacity, based on the heat load model and the feature data. Cooling capacity index estimator.

2. 2. The cooling capacity index estimation device according to claim 1, wherein the heat load model processing unit creates the heat load model using calculated values ​​of a temperature difference inside and outside the vehicle and an occupancy rate in a predetermined time span as the feature data.

3. The cooling capacity index estimation device of claim 1, wherein the cooling capacity index calculation unit calculates the cooling capacity index by subtracting the amount of change in the interior temperature caused by the heat load calculated by the heat load model from the interior temperature difference that changes over a predetermined time span due to the operation of the air conditioning device calculated as the feature data.

4. The cooling capacity index estimation device according to claim 1 , wherein the heat load model processing unit determines whether or not the data observation values ​​include differences due to seasonality based on a collection period of the observation data, and creates the heat load model according to a result of the determination.

5. The cooling capacity index estimation device of claim 1, wherein the heat load model processing unit determines whether to update the heat load model by comparing the amount of observation data collected during the period used in the created heat load model with the amount of observation data collected during a new data period.

6. The cooling capacity index estimation device of claim 1, wherein the heat load model processing unit, when the heat load model of the target railway vehicle does not exist, determines whether or not there is a heat load model for the same car type and car number as the target railway vehicle, and if there is, replaces the heat load model for the car type and car number as the heat load model of the target railway vehicle.

7. The cooling capacity index estimation device according to claim 1 , wherein the cooling capacity index calculation unit calculates the cooling capacity index using at least one of calculated values ​​of humidity, interior temperature, and outside air temperature in a predetermined time width as feature data.

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