JP4136574B2 - Maintenance time determination method, failure diagnosis device, program - Google Patents

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a system for managing and monitoring a refrigerated / refrigerated showcase, and more particularly to a maintenance timing determination method for determining an appropriate maintenance timing for each part of a refrigerated / refrigerated showcase, a failure diagnosis apparatus that implements this method, and the like. .
[0002]
[Prior art]
In recent years, in-store management systems that manage various devices installed in supermarkets and convenience stores have begun to spread, monitoring the internal temperature of freezer and refrigerated showcases and various state data such as refrigerators. Yes.
[0003]
Regarding the management system for such refrigerated / refrigerated showcases, etc., the applicant of the present application has already measured data after actual installation even if there is a difference for each device or a difference in installation environment. Based on the above, by setting an appropriate threshold value for each failure (frosting, clogging, refrigerant leakage, etc.), accurate failure detection / failure prediction can be performed, and a huge amount of database No. 2001-380083 has been proposed by the present applicant in Japanese Patent Application No. 2001-380083, which requires no processing load and requires only a small processing load.
In addition, as a method for determining a failure of a device such as a low-temperature showcase, a method described in, for example, Patent Document 1 has been proposed.
[0004]
[0005]
[Patent Document 1]
JP 11-337242 A
[0006]
[Problems to be solved by the invention]
In the apparatus proposed by the applicant, as described above, accurate failure detection / failure prediction can be performed for each failure (frosting, clogging, refrigerant leakage, etc.). Although clogging, etc., is called a “failure”, basically it can be dealt with by performing maintenance such as cleaning. If the appropriate maintenance time can be automatically determined, a freezing / refrigerated showcase can be used. Can be expected to be useful in the operation and management of For example, it is conceivable to determine an appropriate maintenance time from the viewpoint of energy saving.
[0007]
An object of the present invention is a maintenance timing determination method capable of automatically determining an appropriate maintenance timing for energy saving in an inexpensive system without being affected by an installation environment or the like, a failure diagnosis device for executing the method, To provide programs.
[0008]
[Means for Solving the Problems]
In the maintenance time determination method according to the present invention, within an arbitrary learning period, the computer predetermines state data of each part of the freezer / refrigerator showcase including the showcase and the refrigerator and the power consumption of the refrigerator. Based on the collection result, the correlation between the state of each part and the power consumption is obtained, and the threshold corresponding to the set energy saving target value is determined for each part using the correlation. During operation, the determined threshold value is used to determine the time when maintenance should be performed for each part.
[0009]
According to the above method, the threshold value for the energy saving target is determined based on the data measured after the installation for the refrigerated / refrigerated showcase, etc. An appropriate maintenance time for energy saving can be automatically determined for each part (maintenance target).
[0010]
Furthermore, instead of simply monitoring the increase in power consumption, for each part (maintenance target) of the refrigeration / refrigeration showcase, the correlation between the state and power consumption is obtained by learning, and for each maintenance target Since the threshold value is determined, an appropriate maintenance time for energy saving can be determined for each maintenance target.
[0011]
Further, for example, the state data of each part is a pull-down time of the inside temperature of the showcase or the discharge side pressure of the refrigerator or the temperature adjustment temperature of the showcase, and the discharge side pressure or the temperature adjustment temperature. With respect to the pull-down time, after correcting the data collected at the predetermined period to remove the influence of the surrounding environment at the time of data collection, the state of each part and the A correlation with power consumption is obtained.
[0012]
As described above, with respect to the discharge side pressure and the pull-down time of the temperature control temperature, by performing data correction, it is possible to remove the influence of the ambient environment (particularly temperature) at the time of data collection.
[0013]
Further, the failure diagnosis apparatus of the present invention comprises state data collecting means for collecting state data of each part of a freezer / refrigerated showcase comprising a showcase and a refrigerator, and state data collected by the state data collecting means. Power consumption detecting means for detecting the power consumption of the refrigerator when collecting, each state data collected by the state data collecting means during a predetermined learning period, and the power consumption detecting means when collecting the state data Correlation learning means for obtaining a correlation between the state of each part and the power consumption based on the respective power consumption detected by the method, and arbitrarily based on the correlation obtained by the correlation learning means Using the threshold value determining means for determining the threshold value corresponding to the set energy saving target value for each part, and the threshold value determined by the threshold value determining means during operation, Serial to each portion configured to have a maintenance timing determination means determines whether or not a time to be performed maintenance.
[0014]
It is noted that the program may be read out and executed by a computer from a computer-readable storage medium storing a program for causing the computer to perform the same control as the function performed by each configuration of the present invention described above. Can solve the problem.
[0015]
DETAILED DESCRIPTION OF THE INVENTION
Embodiments of the present invention will be described below with reference to the drawings.
FIG. 1 is a functional block diagram of a failure diagnosis apparatus according to the present embodiment.
[0016]
The illustrated failure diagnosis apparatus 70 includes a state data collection unit 71, a power consumption detection unit 72, a correlation learning unit 73, a threshold value determination unit 74, and a maintenance time determination unit 75. However, the present invention is not limited to this, and for example, a functional unit that realizes the processing of FIG. The failure diagnosis device 70 is mounted on, for example, the in-store PC 8 shown in FIG. However, the present invention is not limited to this, and may be implemented in the NA 3 in FIG.
[0017]
The state data collection unit 71 collects state data of each part (maintenance target) of a refrigeration / refrigeration showcase, which will be described later, composed of the showcase 80 and the refrigerator 90 at a predetermined cycle. The state data of each part is, for example, a showcase cabinet temperature, a refrigerator discharge pressure value, a temperature control temperature pull-down time, and the like, which will be described later.
[0018]
When the state data collection unit 71 collects various state data, the power consumption detection unit 72 detects the power consumption of the refrigerator at that time.
The correlation learning unit 73 performs various states (failures) based on the various state data collected by the state data collection unit 71 and the power consumption detected by the power consumption detection unit 72 during a predetermined learning period. Degree) and power consumption. At that time, regarding the discharge pressure value of the refrigerator and the pull-down time of the temperature control temperature, the collected data is corrected to be a value under the standard temperature environment, and the corrected data is used.
[0019]
The threshold value determination unit 74 determines a threshold value of each failure degree corresponding to an arbitrarily set energy saving target value based on the correlation.
The state data collection unit 71 further collects various state data at any time during the operation of the freezing / refrigeration showcase after the learning period.
[0020]
Then, the maintenance time determination unit 75 compares the various state data collected during the operation with the respective threshold values determined by the threshold value determination unit 74, and determines whether or not the maintenance time has come. Determine. At that time, the state data used for comparison uses the collected values for the showcase chamber temperature as they are, but the collected data is used under the standard temperature environment for the refrigerator discharge pressure value and the temperature control temperature pull-down time. It corrects so that it may become a value, and uses data after amendment.
[0021]
The failure diagnosis apparatus 70 shown in FIG. 1 will be described in detail later with reference to FIG.
Before describing the failure diagnosis apparatus 70 in detail, first, the entire system related to the failure diagnosis apparatus 70 and the description of the preconditions for failure diagnosis will be described below.
[0022]
FIG. 2 is a diagram schematically showing the overall configuration of the in-store device management system related to the failure diagnosis apparatus.
FIG. 3 is a block diagram showing a system configuration in the store 100A shown in FIG.
[0023]
Here, for convenience of explanation, the detailed configuration is shown only for the store 100A, but the stores 100B and 100C have substantially the same configuration as the store 100A.
The in-store equipment management system shown in FIGS. 2 and 3 has separate controllers (shows) for various equipment such as a showcase 1 (freezer / refrigerated open showcase), an air conditioner 2 and other lighting in each store 100A to 100C. A case controller 9, an air conditioner controller 11, a heat storage controller 12, and a lighting controller 21) are arranged, and an in-store PC 8 or an information processing device outside the store (NA) via a network adapter (NA) 3, a router 4, and a network 5. The store headquarters server 6, the service headquarters server 7, etc.). In this in-store device management system, each controller aggregates data from the devices under its jurisdiction over time, and transmits the collected data to an in-store PC (hereinafter referred to as in-store PC) 8. . As a specific example, the showcase controller 9 collects data of the showcase 1 and the refrigerator 10 and transmits it to the in-store PC 8. The air conditioner controller 11 collects data of the air conditioner 2 and transmits it to the in-store PC 8. The heat storage controller 12 collects data of the heat storage tank 13 and transmits it to the in-store PC 8. Alternatively, the collected data may be transmitted to the information processing apparatus outside the store via the network 5. It should be noted that these controllers are of course not dedicated to data collection.
[0024]
Then, the in-store PC 8 or any information processing apparatus outside the store that has received data from these controllers performs failure diagnosis / prediction of each device based on the received data. A specific failure diagnosis / prediction method will be described in detail later.
[0025]
In FIG. 2, a store 100A, a store 100B, and a store 100C are stores in which a showcase 1 and the like are arranged in the store. Moreover, the air conditioner 2, the vending machine 14, the security system 15, etc. may be arrange | positioned in these stores. Here, it is assumed that these stores 100A to 100C are stores that are dispersedly arranged in a region like a supermarket that has become a chain store.
[0026]
The store headquarter server 6 is a terminal device of the headquarters mechanism that controls the stores 100A to 100C. The store headquarters server 6 instructs each store on the type and price of the product to be provided, and the sales status of each store 100A to 100C. To grasp. The service headquarter server 7 is a terminal device of the headquarters mechanism that centrally manages the contents of services that the stores 100A to 100C should provide to customers. The security headquarter server 16 is a terminal device of the headquarters mechanism that supervises security mechanisms related to entrance / exit management, fire, and crime prevention in the stores 100A to 100C.
[0027]
As described above, in the in-store device management system of this example, the store headquarter server 6, the service headquarter server 7, and the security headquarter server 16 are not simply managed for each store, but for each store. The headquarters mechanism common to the stores 100A to 100C is provided, and the stores 100A to 100C are centrally managed for each function.
[0028]
The store owner server 17 is a terminal device owned by the owners of the stores 100A to 100C. The device owner server 18 is a terminal device owned by the owner of the rental device when the device such as a showcase installed in each store 100A to 100C is a rental device rented from a manufacturer or the like. The equipment maintenance contractor / server 19 is a terminal device owned by a maintenance contractor that maintains and manages equipment such as the showcase 1 installed in each of the stores 100A to 100C. Here, for convenience of explanation, it is assumed that the store owner, the device owner, and the device maintenance contractor of each of the stores 100A to 100C are the same person.
[0029]
Further, each of the stores 100A to 100C, the store headquarter server 6, the service headquarter server 7, the security headquarter server 16, the store owner / server 17, the device owner / server 18, and the device maintenance contractor / server 19 are respectively connected via the router 4. Connected to the network 5. Specifically, the network 5 is a public line network (telephone line, ISDN, etc.), the Internet, a network using ATM, and the like, and each terminal device performs communication using the TCP / IP protocol. Therefore, it is also possible to perform Internet file transfer (FTP) and e-mail transfer between the stores 100A to 100C and each terminal device.
[0030]
Therefore, failure diagnosis and prediction of each device in each store 100A to 100C is performed not only in the store PC 8 in each store 100A to 100C, but also in the store headquarter server 6, the service headquarter server 7, the security headquarter server 16, the store owner, It can also be performed in each terminal device such as the server 17, device owner / server 18, device maintenance contractor / server 19. Or you may make it make NA3 mentioned later perform.
[0031]
The system in each store 100A to 100C shown in FIG. 3 includes a store control controller 20, a showcase controller 9, an air conditioner controller 11, a heat storage controller 12, a lighting controller 21, a vending machine 14, a security system 15, and the like. It has a configuration in which it is connected to the NA 3 by a drop method (a method of connecting via various controllers, vending machines, etc. as shown in the figure), and can perform notification and instruction of information related to the status of each device.
[0032]
Specifically, for example, the heat storage controller 12 receives data indicating the load status of the refrigerator 22 and the refrigerator 23 from the showcase controller 9 and the air conditioner controller 11, and receives the received data (load status of the refrigerator). ) To control the heat storage tank 13.
[0033]
NA3 provides a homepage such as “apparatus status display / apparatus setting input”, for example. On the “browser” screen of a terminal device that is allowed access, for example, the status display of the connected apparatus as described above A function that allows you to make settings (hereinafter referred to as the homepage function) and for any administrator who is automatically designated or registered in advance if a problem occurs in the connected device. The network adapter has a function of notifying trouble occurrence by e-mail or the like (hereinafter referred to as automatic mail transmission function) (the invention relating to these functions has already been filed by the applicant of the present invention).
[0034]
By using the NA3, various devices in the stores 100A to 100C can be intensively monitored outside (for example, the store headquarters server 6), or the occurrence of troubles can be managed by a device maintenance company / server. 19 or the like can be quickly notified by e-mail. The NA3 can be formed as an option board having an interface for accommodating a serial communication line, and can be attached to the in-store PC 8 or the like. Moreover, it is good also as a structure which replaces NA3 with the personal computer etc. which have a homepage function and a mail transmission function, or attach NA3 directly to various apparatuses in a store.
[0035]
The store controller 20 is a controller that performs energy saving control of the entire store, and specifically includes a personal computer or the like. The showcase controller 9 is a controller that controls the refrigerator 22 in order to adjust the temperature of the goods in the store of the showcase 1 and the refrigerator 10. The air conditioner controller 11 is a controller that controls the air conditioner 2 for adjusting the inside of the store to a predetermined temperature using the refrigerator 23. The heat storage controller 12 is a controller that controls the heat storage tank 13 that stores ice based on the load state of the refrigerator 22 and the refrigerator 23.
[0036]
The lighting controller 21 is a controller that controls various lighting devices and the like disposed on the ceiling or the like in the store, and also performs fluorescent lamp dimming of the showcase 1. Specifically, for example, when performing fluorescent lamp dimming of the showcase 1, dimming control corresponding to the illumination control system disclosed in JP-A-6-325874 is performed.
[0037]
The vending machine 14 is an owner-managed vending machine that is installed in a store and sells beverages and foods. Since the vending machine 14 is not related to a showcase or the like, a controller for the vending machine 14 is not provided.
[0038]
The security system 15 is a system for managing the security of the entire store based on data from a sensor provided on the door 24, a fire alarm 25, a security device 26, and the like. Since the security system 15 originally has a controller function, no separate controller is provided.
[0039]
The in-store PC 8 is a personal computer installed in the store. In addition to managing the type and price of products sold in the store and counting sales, each device is based on time-series data received from each controller. Perform a fault diagnosis of. Note that it is also possible to communicate with the store owner / server 17 or the device owner / server 18 using a voice message via the PHS adapter 27 attached to the in-store PC 8. Specifically, for example, failure prediction information described later is transmitted.
[0040]
In the above description of the embodiment, various devices such as the showcase controller 9 and the air conditioner controller 11 are connected by the multi-drop method. However, the present invention is not limited to this, and the ring type is used. Various network topologies such as can also be used. Further, the stores 100A to 100C and the store headquarter server 6 and the like can communicate with each other via the network 5, but the present invention is not limited to this. Thus, the communication may be configured.
[0041]
Next, the configuration of the showcase 1 will be described. Here, an open showcase will be described as an example with reference to FIG.
FIG. 4 shows a side sectional view of an example of an open showcase.
[0042]
An open showcase 32 shown in FIG. 1 includes a main body 27, a display shelf 28, an opening 29, a refrigerator 30, a refrigerant pipe 31, an evaporator 33, a blower 34, a duct 35, a blowout port 36, and the like. 45, the internal temperature thermistor 46, the temperature control thermistor 47, the defrost temperature thermistor 48, etc. are installed. These are basically installed in the room (inside the store), and are hereinafter referred to as the main body side of the open showcase 32 in order to be distinguished from the refrigerator 30 that is an outdoor unit.
[0043]
The main body 27 is a basic housing forming the open showcase 32, and has a shape in which one direction is opened (opening 29) so that a product can be taken in and out. The display shelf 28 is a shelf for placing products.
[0044]
The refrigerator 30 generates a high-temperature and high-pressure liquid refrigerant and supplies it to the main body side of the open showcase 32. That is, the high-temperature and high-pressure liquid refrigerant generated by the refrigerator 30 passes through the refrigerant pipe 31 and is supplied to the open showcase 32 main body side. This is converted into a low-temperature and low-pressure liquid refrigerant by an expansion valve (not shown) and sent to the evaporator 33. Thereby, the air around the evaporator 33 is cooled. The cooled air is sent into the duct 35 by the blower 34 and further sent from the blowout port 36 to the interior 37 of the open showcase 32 to cool the entire interior 37. A part of the cool air sent from the blow-out port 36 to the interior 37 is sucked into the suction port 38 while forming an air curtain as shown by an arrow in the figure. This is again sent by the blower 34 to create a cold air circulation. Although not shown in FIG. 4, an electromagnetic valve is installed in the pipe before flowing into the evaporator 33, and this electromagnetic valve is opened and closed so that the temperature in the showcase chamber is maintained at the set temperature. The
[0045]
The outside temperature thermistor 45 is a temperature sensor for detecting the temperature around the open showcase 32. The inside temperature thermistor 46 is a temperature sensor for detecting the temperature inside the inside 37 of the open showcase 32. The temperature control temperature thermistor 47 is a temperature sensor for detecting the temperature of the air sent into the duct 35 by the blower 34. The defrosting temperature thermistor 48 is a temperature sensor for detecting the temperature in the vicinity of the evaporator 33.
[0046]
The refrigerator 30 will be described in detail.
As shown in the figure, the refrigerator 30 has a configuration including a compressor 39, a condenser 40, and the like.
[0047]
The low-temperature and low-pressure liquid refrigerant sent to the evaporator 33 becomes a low-temperature and low-pressure gas refrigerant and returns to the refrigerator 30 side through the refrigerant pipe 31. This is first sent to the compressor 39 and compressed to become a high-temperature / high-pressure gas refrigerant. This gas refrigerant is sent to the condenser 40 and becomes a high-temperature / high-pressure liquid refrigerant by dissipating heat by the condenser 40, and is again sent to the open showcase 32 side through the refrigerant pipe 31. The refrigerant pipe 31 is configured to circulate the refrigerant in a loop shape.
[0048]
For example, in the case of a certain inverter type refrigerator, the compressor 39 is controlled in rotation speed according to the load.
That is, since the load is large, when a larger cooling capacity is required, the compressor 39 is controlled to rotate at a high speed, and a large amount of refrigerant is fed. In this case, power consumption increases.
[0049]
On the other hand, when the load is small and the cooling capacity is not so necessary, the compressor 39 is controlled to rotate at a low speed, and the amount of refrigerant to be fed is reduced. That is, energy saving operation is performed.
[0050]
The refrigerator 30 is provided with a temperature sensor and a pressure sensor. That is, as shown in the figure, a suction (low pressure) side temperature sensor 41, a suction (low pressure) side pressure sensor 42, a discharge (high pressure) side temperature sensor 43, and a discharge (high pressure) side pressure sensor 44 are provided. The suction (low pressure) side temperature sensor 41 and the suction (low pressure) side pressure sensor 42 are sensors that are provided for measuring the temperature and pressure of the low-temperature and low-pressure gas refrigerant before entering the compressor 39. The discharge (high pressure) side temperature sensor 43 and the discharge (high pressure) side pressure sensor 44 are sensors that are provided to measure the temperature and pressure of the high-temperature and high-pressure gas refrigerant that is compressed by the compressor 39.
[0051]
Here, in the case of a certain inverter type refrigerator, when it is predicted that the allowable range of refrigeration cycle equipment and electric / electronic parts will be exceeded due to increased load and transient disturbance, protection control operation different from normal operation control is entered. . For example, protection control such as reducing the operating frequency when the discharge (high pressure) side pressure is too high is performed. By performing such protection control, it is possible to prevent a failure of the refrigerator 30 itself, but there may be a problem that the temperature in the showcase chamber rises.
[0052]
Next, signal change characteristics of the various sensors installed in the open showcase 32 and the refrigerator 30 when various failures occur will be described below. Of the various failures described below, a frost failure and a clogging failure may not be strictly speaking, but are referred to as failures here.
[0053]
First, a case where a frost failure (ice bank) occurs in the evaporator 33 will be described.
FIG. 5A is an external view of the evaporator 33 in a state in which frost is completely removed, and FIG. 5B is an evaporation in a state in which frost 49 is adhered to a limit amount that can completely remove frost by the defrosting operation. FIG.
[0054]
FIG. 6 mainly shows the thermistor 45 for the outside air temperature, the thermistor 46 for the internal temperature, and the thermistor for the temperature control temperature from the state of FIG. 5A to the state of FIG. 5B (and thereafter). The graph shows the change over time in the temperature measured at 47. The vertical axis of the graph shown in the figure is measured temperature, and the horizontal axis is time.
[0055]
In FIG. 6, time t1 is in the state of FIG. 5 (a), and time t2 is in the state of FIG. 5 (b). The outside temperature thermistor 45 measures the temperature around the open showcase 32 and is not affected by the state of frost adhesion to the evaporator 33. In the example shown in the figure, it is assumed that the ambient temperature is substantially constant. On the other hand, as the amount of frost adhering to the evaporator 33 increases, it becomes difficult for the cool air to be blown. Gradually rises as shown in the figure.
[0056]
Next, with reference to FIGS. 7A, 7 </ b> B, 8 </ b> A, and 8 </ b> B, a description will be given of the case where the condenser 40 of the refrigerator 30 is clogged.
7A is an external view of the condenser 40 in a state where the dust 50 is completely removed, and FIG. 7B is an external view of the condenser 40 in a state where the dust 50 is clogged.
[0057]
8 (a) and 8 (b) mainly show the discharge (high pressure) side temperature sensor 43 from the state of FIG. 7 (a) to the state of FIG. 7 (b) (and thereafter). The graph shows the change over time in the temperature / pressure of the refrigerant measured by the high pressure side sensor 44. In the graphs shown in FIGS. 4A and 4B, the horizontal axis represents time, the vertical axis represents (a) temperature, and (b) pressure.
[0058]
8A and 8B, time t3 is the state shown in FIG. 7A, and time t4 is the state shown in FIG. 7B. When the condenser 40 is clogged and the heat transfer area is reduced, the heat radiation from the gas refrigerant is not normally performed. Therefore, the temperature and pressure of the gas refrigerant on the discharge (high pressure) side increase according to the degree of clogging. . As the temperature and pressure of the gas refrigerant on the discharge (high pressure) side rise, the temperature and pressure of the refrigerant guided to the evaporator 33 also rise, and the temperature of the inside 37 of the showcase will rise.
[0059]
Furthermore, the temperature and pressure of the refrigerant returning from the evaporator 37 to the refrigerator 30 side, that is, the gas refrigerant on the suction (low pressure) side also rises. When the pressure of the gas refrigerant rises, in the case of the above-described inverter type refrigerator, the protection control operation for lowering the operation frequency is started as described above. By entering the protection control operation, the refrigerator 30 may be free from failure. However, if the operation frequency is lowered, the temperature and pressure of the refrigerant led to the evaporator 33 further increase, and the inside of the showcase chamber 37 is increased. Will not cool down at all.
[0060]
Furthermore, if the clogging of the condenser 40 becomes more severe by continuing the operation as it is, the refrigerator 30 eventually stops due to an abnormal high pressure.
Subsequently, the phenomenon at the time of leakage or shortage of the gas refrigerant will be described.
[0061]
FIG. 9 is a graph showing the pull-down time of the internal temperature 51 (measured by the internal temperature thermistor 46) and the temperature adjustment temperature 52 (measured by the internal temperature thermistor 47) after the defrosting operation is performed. Indicates.
[0062]
The left side of the figure shows the time-dependent changes in the internal temperature 51 and the temperature control temperature 52 in the state where there is no leakage / insufficiency of the gas refrigerant, and on the right side in the state where there is leakage / insufficiency of the gas refrigerant.
First, as shown on the left side of the figure, in a state where there is no leakage / insufficiency of the gas refrigerant, the internal defrosting operation causes the internal temperature 51 and the temperature adjustment temperature 52 to rise sharply. Drops rapidly. On the other hand, in the state where there is leakage / insufficiency of the gas refrigerant, the cooling capacity in the evaporator 33 is reduced, so that the internal temperature 51 and the temperature adjustment temperature 52 are reduced after the defrosting operation is completed, as shown on the right side of the figure. It will take a pull-down time.
[0063]
As described above, the signal change characteristics of the various sensors for detecting the state data of the open showcase 32 and the refrigerator 30 have been described when various failures occur. Based on the measurement data), threshold setting and subsequent failure diagnosis / prediction processing are performed.
[0064]
Hereinafter, the failure diagnosis / prediction method will be described with reference to FIG. Note that the prediction means that it is highly likely that a failure will occur in the near future.
FIG. 10 is a flowchart for explaining threshold determination / fault diagnosis / prediction processing executed by the fault diagnosis apparatus.
[0065]
As described above, in the in-store equipment management system shown in FIG. 2 and FIG. 3, data (various sensors) of equipment (showcase 1 and refrigerator 10 etc.) that each controller (showcase controller 9 etc.) has jurisdiction over. And the like are measured over time at regular intervals, and the collected time series data is transmitted to the in-store PC 8.
[0066]
In this example, an example in which the in-store PC 8 executes the process described below using the data transmitted as described above is not limited to this, and the process is executed by the NA 3. Alternatively, the above data may be transferred to an arbitrary information processing apparatus outside the store (for example, each server (service headquarter server 7 etc. in FIG. 2)) via the network 5 for processing. Good. If the function as a failure diagnosis apparatus according to this example is realized by the in-store PC 8, the function version can be easily upgraded, and if it is realized by the NA3 or any information processing apparatus outside the store, the inside of the store There is no need to provide PC8.
[0067]
The in-store PC 8 has a certain period of time from when a showcase and a refrigerator are delivered and installed in the store and the operation of the device is stabilized after the power is turned on within a specific period during which normal data can be collected. The learning process (in the form of a database) is performed based on the time-series data (starting after the elapse of time until the arbitrary period elapses) (step S1).
[0068]
That is, in the non-defrosting section (that is, when the defrosting is not performed; the temperature in the showcase chamber etc. temporarily increases during the defrosting and cannot be used for learning). The average value and instantaneous value of the internal temperature, the average value of the discharge pressure of the refrigerator, and the average value of the temperature outside the store are obtained and stored. Further, the data of the internal temperature pull-down time is naturally collected in the defrosting section (after the defrosting operation, etc.), and the average value of the temperature in the store at that time is obtained and stored (step S2). ).
[0069]
After collecting the above data within a predetermined period, based on this data, threshold value determination processing using a normal distribution curve described later is performed (or although not specifically shown / explained, for example, the amount of learning data is low). If the distribution is sufficient and the distribution of the distribution is unclear, a t distribution curve may be used instead of the normal distribution curve). Before that, correction processing is performed for the discharge pressure of the refrigerator and the internal temperature pull-down time. I do.
[0070]
That is, among the above data, the discharge pressure of the refrigerator and the internal temperature pull-down time are affected by seasonal fluctuations (mainly temperature changes) and change even if no failure occurs.
[0071]
Therefore, by obtaining the relationship between each of the collected average values of the discharge pressure of the refrigerator and the temperature pull-down time inside the warehouse and each influencing factor (mainly the temperature inside / outside the store), these data are It is necessary to correct it so that it is not affected by. Since the discharge pressure of the refrigerator is mainly influenced by the store outside temperature and the internal temperature pull-down time is mainly influenced by the store temperature, these relationships are obtained and the collected data is corrected (step S3).
[0072]
Here, an example of the relationship between the discharge pressure of the refrigerator and the store outside temperature will be described with reference to FIG. 11, but the relationship between the in-store temperature pull-down time and the store temperature will be similarly determined. .
[0073]
FIG. 11 is a diagram showing the relationship between the discharge pressure and the store outside temperature, created based on the data collected in step S2. The illustrated example is created based on about 15 pieces of data, and thereby the illustrated regression line (y = 0.2x + 9.7) is created (correlation coefficient; 0.76). As shown in the figure, the value of the discharge pressure data fluctuates with a certain tendency influenced by the store outside temperature, so each of these collected data is obtained when the store outside temperature is the same. It must be corrected to a power value. Therefore, an arbitrary temperature (hereinafter referred to as a standard temperature) is determined in advance, and each data of the discharge pressure is corrected to be a value at the standard temperature using the above relational expression (regression line).
[0074]
For the internal temperature pull-down time, a relational expression with the in-store temperature is obtained and corrected in the same manner.
Next, a process for determining a threshold value is performed (step S4). The threshold value is obtained using a normal distribution curve. The normal distribution curve is created using the collected data as it is for the showcase chamber temperature, and the refrigerator discharge pressure and the chamber temperature pull-down time are created using the data corrected in step S3.
[0075]
First, the case temperature in the showcase will be described as an example.
FIG. 12 is a diagram showing a histogram and a normal distribution curve created by collecting temperature data (about 40 pieces) in the showcase cabinet actually installed in the supermarket.
[0076]
As shown in the figure, the internal temperature data is such that the normal distribution curve 53 centered around −1 to −0.5 ° C. can be drawn without any particular correction. A method for obtaining the threshold using the normal distribution curve 53 will be described below.
[0077]
First, in general, from a statistical viewpoint, when testing whether the distribution of certain measurement data is the same as the learned distribution, normalization of the normal distribution of the learning data (the area surrounded by the normal distribution curve is 1) to determine what percentage of the probability that the measured data is located. Generally, values such as 5% and 1% are used. That is, in the example shown in FIG. 12, the threshold value is set so that the area 54 (shown by hatching) on the right side of the threshold is about 1% or 5% with respect to the total area surrounded by the normal distribution curve 53. Determine the value. At that time, the final threshold value may be determined in consideration of a certain margin.
[0078]
As for the discharge pressure of the refrigerator and the internal temperature pull-down time, similarly to the above, a normal distribution curve is obtained using the data corrected in step S3, and the threshold value is determined.
[0079]
Here, the fact that the correct threshold value can be obtained by performing the above correction will be described with reference to the discharge pressure of the refrigerator described in FIG. 11 as an example.
FIG. 13A is a histogram created using data before correction and its normal distribution curve, FIG. 13B is an example of a histogram created using data after correction and its normal distribution curve, and FIG. ) Is a diagram showing the relationship between FIG. 13 (a) and FIG. 13 (b).
[0080]
As apparent from comparing FIGS. 13A and 13B, the data variation is large due to the influence of the outside temperature before the correction, but after the correction, the influence of the outside temperature is removed. As a result, the width of the data distribution is considerably reduced. FIG. 13 (c) is a diagram showing this fact more visually.
[0081]
By using the normal distribution curve 56 obtained from the corrected data, a more appropriate threshold value can be determined. The same applies to the internal temperature pull-down time.
[0082]
As described above, data is actually collected on the spot for a predetermined period after installation, and the threshold value is determined based on this data. Therefore, according to the environment installed for each device (if it is not actually installed, it depends on the environment, etc. The threshold can be determined (because there is a part where the influence is not known). In this example, the collected data is further corrected as necessary, and the threshold is determined using a normal distribution curve. A threshold is set.
[0083]
After the threshold value is determined as described above, device abnormality (failure) determination is performed using this threshold value. Basically, it is determined whether it is abnormal or normal by collecting data at any time or every predetermined time and comparing this data with a threshold value. The data collection method is the same as in step S2, and in the non-defrosting section, the average value and instantaneous value of the showcase chamber internal temperature, the average value of the discharge pressure of the refrigerator, and the average value of the temperature outside the store are obtained. . In the defrosting section, the internal temperature pull-down time data and the average temperature in the store are obtained. And from these data, the frost formation (ice bank) of an evaporator, the heat exchange performance fall of the condenser represented by the clogging of a condenser, and the cooling performance fall represented by the refrigerant | coolant leakage are determined, respectively. Here, the following description will be made assuming that the threshold relating to the showcase cabinet temperature is threshold A, the threshold relating to the discharge pressure of the refrigerator is threshold B, and the threshold relating to the cabinet temperature pull-down time is threshold C.
[0084]
First, abnormality determination of frost formation (ice bank) of the evaporator will be described.
First, it is determined whether or not the average value of the showcase cabinet temperature obtained by collecting the data is equal to or higher than the threshold value A (step S6).
[0085]
If it is equal to or greater than the threshold A (may be equal to or greater than the threshold A n times in succession) (step S6, YES), the cause is uncertain but it is determined that some abnormality has occurred, An "abnormal" signal is issued (step S7). In this example, in consideration of the possibility of false alarms, even if an “abnormal” signal is issued about once, it is not immediately judged as a failure, but an “abnormal” signal is issued m times consecutively. (Step S8, YES), it is assumed that the temperature in the showcase chamber has surely increased, and an “evaporator frost (ice bank)” signal is issued (step S9).
[0086]
On the other hand, even when the average value of the temperature in the showcase chamber is less than the threshold A (step S6, NO), the instantaneous value of the temperature in the showcase chamber (a plurality of time series data, based on the above average value) The rate of change of the internal temperature is obtained (step S10), and if this rate of change exceeds a preset threshold value (it is considered that an abnormally rapid change has occurred). (Step S11, YES), an “evaporator frosting (ice bank)” signal is issued (step S9).
[0087]
In addition, even if it is not in a state where an “evaporator frost (ice bank)” signal is issued (threshold is not exceeded), if the internal temperature tends to increase, the internal temperature will rise. By calculating the speed (rising speed in units such as per day, per week, etc.) and calculating the time it takes to reach the threshold based on this, the failure occurrence time is predicted, and the prediction result is You may make it display / notify. By predicting the failure time in this way, a maintenance plan can be constructed with high accuracy.
[0088]
Next, the abnormality determination of the heat exchange performance deterioration of the condenser will be described.
In this case, first, it is necessary to correct the collected data (average value of the discharge pressure of the refrigerator) to be a value under the standard temperature environment using the relational expression obtained in step S3 (step S3). S12).
[0089]
Then, it is determined whether or not the corrected discharge pressure data is greater than or equal to the threshold value B (step S13).
If it is greater than or equal to the threshold B (may be greater than or equal to the threshold B consecutively n times) (step S13, YES), the cause is uncertain but it is determined that some abnormality has occurred, An “abnormal” signal is issued (step S14). In this example, in consideration of the possibility of false alarms, even if an “abnormal” signal is issued about once, it is not immediately judged as a failure, but an “abnormal” signal is issued m times consecutively. (Step S15, YES), it is determined that the discharge pressure of the refrigerator is surely increasing, and a “decrease in heat exchange performance of the condenser (clogging)” signal is issued (step S16).
[0090]
On the other hand, when the average value of the showcase chamber temperature is less than the threshold value B (NO in step S13), the process returns to step S5.
Finally, abnormality determination for cooling performance degradation will be described. This is similar to the deterioration of the heat exchange performance (clogging) of the condenser. First, the collected data (internal temperature pull-down time) is corrected using the relational expression obtained in step S3 (step S17). Then, it is determined whether or not the corrected data is equal to or greater than the threshold value C (step S18). If the corrected data is equal to or greater than the threshold value C (step S18, YES), an “abnormal” signal is issued (step S19). When it has continued for a number of times (step S20, YES), it is determined that the cooling performance has been lowered (refrigerant leakage) (step S21).
[0091]
In addition, even if the alarm such as the “evaporator frost (ice bank)” signal is issued once, there is no abnormality as a result of confirmation by the maintenance staff etc. If it is determined that insufficient learning is the cause of false alarms, the data collected at that time (data used for comparison / determination with threshold values) is also collected in the database (learning period). The threshold value may be set again in addition to the data database.
[0092]
FIG. 14A to FIG. 14D are diagrams visually representing the above-described learning to determination in the same graph format. Here, the discharge pressure of the refrigerator is shown as an example.
[0093]
FIG. 14A shows the learning stage (step S3). As described above, here, first, a regression line is created in order to obtain a correlation between the store outside temperature and the discharge pressure from the collected data and create a correction equation. Next, along the regression line, the outside-store temperature of each data is converged so as to become a preset standard temperature. This has already been described with reference to FIG. 21, but FIG. 14 (a) visually shows the state of convergence on the graph.
[0094]
FIG. 14B shows the threshold setting stage (step S4 above).
Here, the threshold value is determined from the data width of the discharge pressure at the standard temperature converged in the learning step. FIG. 14B visually shows how the threshold is determined on a graph.
[0095]
FIG. 14 (c) shows the measurement stage (after step S <b>5; during actual operation), and shows an example of measuring the temperature outside the store when the discharge pressure data is acquired.
FIG. 14D shows a determination stage (in this example, the above steps S12 and S13; and other steps S17 and S18).
[0096]
That is, on the graph, the discharge pressure data measured in the above measurement stage is corrected to a value at a preset standard temperature (corrected along the regression line) and compared with a threshold value after setting the corrected value. It is expressed visually.
[0097]
Hereinafter, the failure diagnosis apparatus 70 shown in FIG. 1 will be described.
First, the relationship between each failure and the power consumption of the showcase / refrigerator will be described with reference to FIGS.
[0098]
First, the relationship between the frosting failure (ice bank) of the evaporator 33 of a showcase and power consumption is demonstrated.
As described with reference to FIGS. 5A, 5 </ b> B, and 6, as the amount of frost adhering to the evaporator 33 increases, it becomes difficult for the cool air to be ventilated. And the temperature (the temperature of the inside 37 and the duct 35) measured by the temperature control temperature thermistor 47 gradually increases.
[0099]
On the other hand, since the operation control of the refrigerator 30 is basically performed so that the temperature in the showcase chamber is maintained at the set temperature, when the temperature in the showcase chamber is higher than the set temperature, when viewed from the refrigerator 30 side, Assuming that the load is large, control is performed to increase the rotational speed of the compressor. For this reason, as shown in FIG. 15 (a), as the amount of frost adhering to the evaporator 33 increases, the rotational speed of the compressor increases, and accordingly, FIG. 15 (b). As shown, the current value increases. That is, power consumption increases.
[0100]
Next, the relationship between the clogging failure of the condenser 40 of the refrigerator 30 and the power consumption will be described.
As described with reference to FIGS. 7A, 7B, 8A, and 8B, when the condenser 40 is clogged with, for example, dust and the heat transfer area is reduced, heat is released from the gas refrigerant. Therefore, the temperature and pressure of the gas refrigerant on the discharge (high pressure) side rise according to the degree of clogging. Then, in order to achieve a predetermined gas refrigerant temperature / pressure, control is performed to increase the heat radiation amount by increasing the number of rotations of the cooling fan installed in the condenser 40. For this reason, for example, as shown in FIG. 15 (c), the number of rotations of the cooling fan increases in accordance with the degree of clogging of the condenser 40, and accordingly, in FIG. 15 (d). As shown, the current value increases. That is, power consumption increases.
[0101]
Finally, the relationship between leakage or shortage of gas refrigerant and power consumption will be described.
As described with reference to FIG. 9, when the leakage or shortage of the gas refrigerant occurs, the cooling capacity in the evaporator 33 decreases, so that it is considered that the load is relatively large when viewed from the refrigerator side 30. Therefore, also in this case, like the frosting failure, the rotation speed of the compressor increases, and as a result, power consumption increases.
[0102]
FIG. 16 is a flowchart for explaining a process of determining the maintenance execution time from the energy saving target value, which is executed by the failure diagnosis apparatus 70 shown in FIG.
In FIG. 16, first, an energy saving target value is set (step S31). This is, for example, a power consumption value arbitrarily set by the user.
[0103]
Thereafter, within the predetermined learning period described with reference to FIG. 10, for example, the correlation between each failure degree and the power consumption is learned together with the processing of FIG. 10 or without performing the processing of FIG. 10 (step S32). . The degree of failure is the degree of frost formation of the evaporator 33, clogging of the condenser 40, or leakage / insufficiency of the gas refrigerant described with reference to FIG. 10, and the frost formation of the evaporator 33 is collected as in FIG. The data value of the showcase chamber temperature is used as it is. For the degree of clogging of the condenser 40 and the leakage / deficiency of the gas refrigerant, the collected data values are corrected by the same processing as in step S3, and the corrected discharge pressure and the corrected pull-down time values are used. Further, the power consumption detector 71 detects the power consumption of the refrigerator 30 when each data value is collected. The power consumption is basically corrected in the same manner as the discharge pressure and the pull-down time, but it is not necessary to perform the correction.
[0104]
Then, by collecting data at predetermined intervals within the learning period, at the end of the learning period, the correlation between the showcase chamber temperature and power consumption, the corrected discharge pressure value and power consumption And the correlation between the corrected pull-down time value and the power consumption is obtained. For example, as shown in FIG. 17, the correlation between the temperature in the showcase chamber (the degree of frost formation on the evaporator 33) and the power consumption is obtained. Note that the correlation shown in FIG. 17 may be obtained by extending the learning period until a frost failure occurs (a large amount of frost is attached), or a normal state in which no frost failure has occurred. You may estimate based on the correlation calculated | required by the state. The same applies to the discharge pressure and the pull-down time.
[0105]
Based on the correlation thus obtained, the failure degree corresponding to the energy saving target value set in step S1 is determined as a threshold (step S33). For example, FIG. 17 shows a threshold value related to a frost failure in the evaporator 33 (threshold value related to the temperature in the showcase chamber) based on the correlation between the temperature in the showcase chamber (the degree of frost formation in the evaporator 33) and the power consumption. It shows how it is determined. Similarly, for other failures (clogging of the condenser 40, leakage / insufficiency of gas refrigerant), the discharge pressure value (correction value) corresponding to the energy saving target value and the pull-down time value (correction value) corresponding to the energy saving target value ) Is determined as a threshold value.
[0106]
Each of these threshold values is different from each threshold value determined in the processing of FIG. Each threshold value in FIG. 10 is a threshold value for detecting the occurrence of a failure or predicting the occurrence time of a failure, but each threshold value obtained in step S33 is used in order to save energy through efficient operation. This is a threshold value for determining the optimal maintenance time. Therefore, the processes in steps S34 to S36 in FIG. 16 may be performed in parallel with the processes in and after step S5 in FIG. That is, it may be configured as a failure diagnosis device that detects failure occurrence, predicts failure occurrence time, and determines / notifies optimal maintenance time. Of course, the apparatus may be configured as a failure diagnosis apparatus that performs only the process of FIG. 16, that is, the determination / notification of the optimal maintenance time without performing the process of FIG.
[0107]
The processes in steps S34 to S36 in FIG. 16 are repeatedly performed during operation after the learning period. That is, as in the process of step S5 of FIG. 10, various data on the showcase cabinet temperature, the refrigerator discharge pressure value, and the pull-down time of the temperature adjustment temperature 52 are collected at any time (step S34). Then, the temperature in the showcase chamber is compared with the corresponding threshold value as it is (step S35), and when the threshold value is exceeded (step S35, YES), it is determined that it is time to perform maintenance. (Step S36), display / notification and the like are performed. As for the discharge pressure value and the pull-down time, the collected data is corrected to be a value under the standard temperature environment, and then the corrected value is compared with the threshold value (step S35). If it has exceeded (YES in step S35), it is determined that it is time to perform maintenance (step S36), and display / notification is performed.
[0108]
As described above, according to the failure diagnosis apparatus 70 of the present example, it is possible to automatically determine an appropriate maintenance time without being affected by the installation environment or the like, particularly with respect to a refrigeration / refrigeration showcase, etc. It is possible to save energy by maintaining efficient operation.
[0109]
Also, instead of simply monitoring the increase in power consumption, for each part of the refrigerated / refrigerated showcase (maintenance target), the correlation between the state and power consumption is obtained by learning, and for each maintenance target Since the threshold is determined, the maintenance time can be determined for each maintenance target.
[0110]
Furthermore, regarding the discharge side pressure and the temperature control temperature pull-down time, since the corrected data is used as described above, the influence of the surrounding environment at the time of data collection can be eliminated.
[0111]
Furthermore, since it becomes clear which maintenance object has the most influence on power consumption, the priority of maintenance can be determined.
FIG. 18 is a hardware configuration diagram of an information processing apparatus that performs the learning process, threshold setting / failure diagnosis / prediction / alarm notification process, and the like, and a diagram illustrating an example of a storage medium that stores a program that executes these processes. is there.
[0112]
In FIG. 18, the information processing apparatus 60 includes a CPU 61, a storage unit 62 (including a portable storage medium 62a), a memory 63, a display unit 64, an operation unit 65, an input / output interface unit 66, a communication unit 67, and the like. . The information processing device 60 is the in-store PC 8, NA (network adapter) 3, device maintenance company / server 19, or the like.
[0113]
The CPU 61 is a central processing unit that controls the entire information processing apparatus 60.
The storage unit 62 is a storage device such as an HDD in which a program for realizing at least the learning process and the failure diagnosis / prediction process described above is stored. Alternatively, the storage unit 62 may be a combination of a portable storage medium 62a and its drive reading device (for example, a floppy disk (FD) and a flexible disk drive (FDD)). In addition to the FD, the portable storage medium 62a includes a CD-ROM, a memory card, a DVD, an MO, and the like.
[0114]
The memory 63 is a RAM or the like for temporarily storing the program stored in the storage unit 62 and causing the CPU 61 to execute the program.
The display unit 64 is a display or the like, and performs display as shown in FIGS. 25 and 26, for example.
[0115]
The operation unit 65 is a keyboard, a mouse, or the like, and a user performs a desired operation using the operation unit 65.
The input / output interface unit 66 is an interface for inputting state data of various devices (showcase, refrigerator, etc.) via any communication line such as the multi-drop method or ring type.
[0116]
The communication unit 67 has a generally well-known configuration that enables communication with other information processing apparatuses via the router (dial-up router) and the network 8, for example.
[0117]
Here, the present invention is not limited to an apparatus such as an information processing apparatus itself, and when used by a computer, a computer-readable recording medium (storage) storing programs for realizing various functions according to the present invention. (Medium) itself can also be configured.
[0118]
In this case, the “recording medium” includes, for example, a portable storage medium such as a CD-ROM 68 and a floppy disk 69 shown as an example of the portable storage medium 62a in FIG. 18 (not limited to these examples, MO, Any information processing apparatus (not shown) that can communicate via the network 8 as well as DVDs, removable hard disks, and the like that fall within the category of “portable storage media”. A “recording medium” is also included. Of course, a storage device (RAM / ROM, hard disk or the like) in the information processing apparatus 60 is also included.
[0119]
【The invention's effect】
As described above in detail, according to the maintenance time determination method, the failure diagnosis apparatus, the program, etc. of the present invention, particularly with respect to the freezing / refrigeration showcase, etc., it is an inexpensive system without being affected by the installation environment. The appropriate maintenance time for energy saving can be automatically determined for each part (maintenance target) of the refrigerated / refrigerated showcase.
[0120]
Furthermore, since it becomes clear which maintenance object has the most influence on power consumption, the priority of maintenance can be determined.
[Brief description of the drawings]
FIG. 1 is a functional block diagram of a failure diagnosis apparatus according to an embodiment.
FIG. 2 is a diagram schematically showing the overall configuration of an in-store device management system related to the fault diagnosis apparatus.
FIG. 3 is a block diagram showing a system configuration in the store shown in FIG. 2;
FIG. 4 shows a side sectional view of an example of an open showcase.
5A is an external view of an evaporator in a state in which frost is completely removed, and FIG. 5B is an evaporator in a state in which frost is attached to a limit amount that can completely remove frost by a defrosting operation. It is an external view.
FIG. 6 is a graph showing changes over time in temperatures measured by an outside temperature thermistor, an internal temperature thermistor, and a temperature control thermistor.
7A is an external view of a condenser in which dust is completely removed, and FIG. 7B is an external view of the condenser in a state where dust is clogged.
FIGS. 8A and 8B are graphs showing changes in refrigerant temperature / pressure with time measured by a discharge (high pressure) side temperature sensor and a discharge (high pressure) side pressure sensor, respectively.
FIG. 9 is a graph showing a pull-down time of the inside temperature and the temperature adjustment temperature after the defrosting operation is performed.
FIG. 10 is a flowchart for explaining threshold determination / fault diagnosis / prediction processing executed by the fault diagnosis apparatus.
FIG. 11 is a relationship diagram of discharge pressure and store outside temperature.
FIG. 12 is a diagram showing a histogram of the temperature in the showcase cabinet and a normal distribution curve.
FIGS. 13A and 13B show an example of a histogram and its normal distribution curve created using data after correction, FIG. 13B shows the relationship between (a) and (b), and FIG. is there.
FIGS. 14A to 14D are diagrams visually showing the state from learning to determination in the same graph format.
FIGS. 15A to 15D are diagrams for explaining the relationship between each failure and the power consumption of the showcase / refrigerator. FIGS.
FIG. 16 is a flowchart illustrating a process for determining a maintenance execution time.
FIG. 17 is a diagram showing an example of the correlation between the temperature in the showcase cabinet (degree of frost formation on the evaporator) and the power consumption.
FIG. 18 is a hardware configuration diagram of a computer that realizes a failure diagnosis apparatus.
[Explanation of symbols]
1 showcase
2 Air conditioner
3 NA (network adapter)
4 routers
5 network
6 store headquarters server
7 Service Headquarters Server
8 In-store PC
9 Showcase controller
10 Refrigerator
11 Air conditioner controller
12 Heat storage controller
13 Thermal storage tank
14 Vending machines
15 Security system
16 Security Headquarters Server
17 Store owner / server
18 Equipment owner / server
19 Equipment Maintenance Contractor / Server
20 Store controller
21 Lighting controller
22 Refrigerator
23 Refrigerator
24 door
25 Fire alarm
26 Security device
27 Body
28 display shelf
29 opening
30 refrigerator
31 Refrigerant piping
32 open showcase
33 Evaporator
34 Blower
35 Duct
36 Outlet (Honeycomb)
37 Inside
38 Suction mouth
39 Compressor
40 Condenser
41 Suction (low pressure) side temperature sensor
42 Suction (low pressure) side pressure sensor
43 Discharge (high pressure) temperature sensor
44 Discharge (high pressure) pressure sensor
45 Thermistor for outside temperature
46 Thermistor for internal temperature
47 Thermistor for temperature control
48 Thermistor for defrosting temperature
49 Frost
50 garbage
51 Internal temperature
52 Temperature control temperature
53 Normal distribution curve
54 areas
55 Normal distribution curve
56 Normal distribution curve
60 Information processing device
61 CPU
62 Memory unit
62a Portable storage medium
63 memory
64 display
65 Operation unit
66 I / O interface section
67 Communication Department
68 CD-ROM
69 floppy disk
70 Fault diagnosis device
71 State data collection unit
72 Power consumption detector
73 correlation learning unit
74 Threshold determination unit
75 Maintenance time determination unit
80 showcase
90 refrigerator

Claims (2)

Within any learning period, the computer
Collecting the state data of each part of the refrigeration / refrigeration showcase consisting of the showcase and the refrigerator and the power consumption of the refrigerator at a predetermined cycle,
Based on the collection result, obtain a correlation between the state of each part and the power consumption,
Using the correlation, a threshold corresponding to the set energy saving target value is determined for each part,
During operation, using the determined threshold, for each part, determine when to perform maintenance,
The state data of each part is a pull-down time of the temperature in the showcase or the discharge side pressure of the refrigerator or the temperature control temperature of the showcase,
Regarding the pull-down time of the discharge side pressure or the temperature control temperature, the data collected at the predetermined period is corrected to remove the influence of the surrounding environment at the time of data collection, and then based on the corrected data. And determining a correlation between the state of each part and the power consumption . State data collecting means for collecting state data of each part of a freezer / refrigerated showcase comprising a showcase and a refrigerator in a predetermined cycle;
Power consumption detecting means for detecting the power consumption of the refrigerator when collecting state data by the state data collecting means;
Based on each state data collected by the state data collection means and each power consumption detected by the power consumption detection means at the time of state data collection during a predetermined learning period, Correlation learning means for obtaining a correlation with power consumption;
Based on the correlation obtained by the correlation learning means, a threshold value determining means for determining a threshold value corresponding to an arbitrarily set energy saving target value for each part;
During operation, using the threshold value determined by the threshold value determination means, for each of the parts, having a maintenance time determination means for determining whether it is time to perform maintenance,
The state data of each part is a pull-down time of the temperature in the showcase or the discharge side pressure of the refrigerator or the temperature control temperature of the showcase,
The correlation learning means performs correction for removing the influence of the surrounding environment at the time of data collection on the data collected at the predetermined period with respect to the discharge side pressure or the temperature control pull-down time, A failure diagnosis apparatus characterized in that a correlation between a state of each part and the power consumption is obtained based on the corrected data .

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