JP2019211130A - State detection device and state detection method - Google Patents

Abstract

To provide a state detection device and a state detection method capable of stably detecting the cumulative level of irreversible state change such as the amount of refrigerant leakage occurring in equipment.SOLUTION: A controller 1 comprises: an operation data acquisition unit 41 that acquires a plurality of pieces of time-series operation data on systems A and B at predetermined interval time points; a model generation unit 43 that generates a normal state operation model for multivariate statistical process management using the plurality of pieces of operation data; a Q statistic value calculating unit 44 that calculates time-series Q statistic values obtained by the multivariate statistical process management based on the normal state operation model and the plurality of pieces of operation data acquired by the operation data acquisition unit 41; an integrated Q value calculating unit 45 that calculates an integrated Q value which is an integrated value of the Q statistic values at each of the predetermined interval time points; an inclination calculation unit 46 that calculates inclination of the integrated Q value relatively to the predetermined interval time points; a determining unit 47 that determines the level of refrigerant leakage based on the inclinations of each of the predetermined interval time points; and an output control unit 48 that outputs determination result by the determining unit 47 to an input/output unit 2.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a state detection device and a state detection method capable of stably detecting a cumulative amount of irreversible state change such as a refrigerant leakage amount generated in equipment.

  In general, in the industrial field, a method for detecting an abnormal sign of equipment using multivariate statistical process control (MSPC) is known.

  Japanese Patent Application Laid-Open No. H10-228667 describes a refrigerant leak detection based on a threshold value using a square root of Mahalanobis distance, which is multivariate analysis, as a state quantity.

Japanese Patent No. 4396286

  By the way, when detecting abnormal signs of equipment using multivariate statistical process management, the measurement data obtained from the equipment varies greatly depending on the operating state of the equipment, so multivariate statistical process management is calculated. The Q statistic to be fluctuated greatly For this reason, when trying to detect an abnormality in equipment using Q statistics of multivariate statistical process management, the instantaneous Q statistics exceeds the threshold even though it is not abnormal depending on the operating state of the equipment. It may be determined that there is an abnormality, and stable abnormality detection could not be performed.

  For example, when trying to detect an abnormality in the amount of refrigerant leakage that progresses in a refrigeration system in a showcase installed in a store such as a convenience store or a supermarket, the suction pressure, discharge pressure, suction gas temperature, It is conceivable that the Q statistical value obtained by the multivariate statistical process management using the discharge gas temperature, the operating frequency, etc. is calculated, and the refrigerant leakage amount is detected as abnormal when the Q statistical value exceeds a threshold value. .

  However, the refrigeration system was obtained by multivariate statistical process management because measurement data fluctuate greatly due to various events such as refrigeration load fluctuation, showcase defrosting, refrigerator pressure control, oil return operation, etc. Q statistics also vary greatly. In particular, the refrigerator is not always operated at 100% output, but is operating energy-saving with variable operation frequency by inverter control according to the outside air temperature and the refrigeration load. The effect on Q statistics is large. As a result, although the refrigerant leakage amount does not actually exceed the predetermined value, the Q statistic value may exceed the predetermined value and erroneously detect that the refrigerant leakage amount is abnormal. That is, in the detection of the refrigerant leakage amount determined based on the Q statistical value at each measurement time, it is difficult to stably detect the refrigerant leakage amount.

  The present invention has been made in view of the above, and provides a state detection device and a state detection method that can stably detect the cumulative amount of irreversible state change such as the amount of refrigerant leakage occurring in equipment. With the goal.

  In order to solve the above-described problems and achieve the object, a state detection device according to the present invention is a state detection device that detects a cumulative amount of irreversible state changes occurring in equipment, and includes a plurality of time-series related to the equipment. An operation data acquisition unit for acquiring the operation data, a model generation unit for generating a normal model for multivariate statistical process management using a plurality of normal operation data, the normal model, and the normal model A Q statistic obtained by performing the multivariate statistical process management based on a plurality of operation data acquired by the operation data acquisition unit in a time series, and the Q statistic obtained by integrating the Q statistic every predetermined time An inclination calculating unit that calculates an integrated value and calculates an inclination of the Q integrated value with respect to the predetermined time; a determining unit that determines an accumulated amount of the irreversible state change based on an inclination every predetermined time; and the determination Judgment of parts Characterized by comprising an output section for outputting the result, the.

  In the above invention, the state detection device according to the present invention includes a temperature sensor that detects an outside air temperature, and the determination unit corrects the accumulated amount of the irreversible state change according to the outside air temperature. To do.

  In the state detection device according to the present invention, in the above invention, the determination unit determines that the accumulated amount of the irreversible state change is abnormal when the accumulated amount of the irreversible state change is equal to or greater than a predetermined value. The output unit outputs that the accumulated amount of the irreversible state change is abnormal.

  In the state detection device according to the present invention as set forth in the invention described above, the determination unit determines a change rate of the irreversible state change based on a time-series change amount of the inclination.

  In the state detection device according to the present invention, in the above invention, the determination unit may determine a point in time when the accumulated amount of the irreversible state change becomes a predetermined value based on the time-series change amount of the slope. And the output unit outputs the estimated time.

  In the state detection device according to the present invention, in the above invention, the output unit superimposes the Q integrated value at a normal time and the Q integrated value calculated by the inclination calculating unit with respect to the predetermined time. It is characterized by a graphical display.

  In the state detection device according to the present invention, in the above invention, the facility is a system using a refrigerator, and the accumulated amount of the irreversible state change is a refrigerant leakage amount of the refrigerator. .

  In the state detection device according to the present invention as set forth in the invention described above, the plurality of operation data includes the detected temperature of the portion to be frozen, the set temperature, the refrigerant suction pressure of the compressor, the refrigerant discharge pressure, the refrigerant suction temperature, and the refrigerant discharge. It includes temperature and operating frequency.

  Further, the state detection method according to the present invention is a state detection method for detecting a cumulative amount of irreversible state change that occurs in equipment, and an operation data acquisition step for acquiring a plurality of time-series operation data related to the equipment; A model generation step for generating a normal model for multivariate statistical process management using a plurality of normal operation data, and a plurality of operation data acquired in the normal model and the operation data acquisition step. A Q statistical value obtained by performing the multivariate statistical process management based on the time series is calculated in a time series, a Q integrated value obtained by integrating the Q statistical value every predetermined time is calculated, and the Q with respect to the predetermined time is calculated. An inclination calculation step for calculating an inclination of the integrated value, a determination step for determining an accumulated amount of the irreversible state change based on the inclination for each predetermined time, and a determination result of the determination step Characterized in that it comprises a and an output step of outputting.

  According to the present invention, it is possible to stably detect the cumulative amount of irreversible state change such as the amount of refrigerant leakage occurring in the facility.

FIG. 1 is a block diagram showing a configuration of a system including a controller having a function of a state detection device according to an embodiment of the present invention. FIG. 2 is a flowchart showing a state detection processing procedure by the controller. FIG. 3 is an explanatory diagram for explaining the abnormality detection based on the conventional Q statistical value. FIG. 4 is a diagram illustrating a time-series change in the Q integrated value, which is an integrated value of the Q statistical value for each predetermined time. FIG. 5 is a diagram showing a relationship between a specific inclination and the degree of refrigerant leakage. FIG. 6 is a diagram showing a display example in which the normal inclination and the current inclination are graphically superimposed and the period from the present time to the occurrence of an abnormality is displayed.

  DESCRIPTION OF EMBODIMENTS Hereinafter, embodiments for carrying out the present invention will be described with reference to the accompanying drawings. In the following embodiments, a state detection device that detects an accumulated amount of refrigerant leakage (degree of refrigerant leakage) in a refrigeration system such as a showcase installed in a store such as a convenience store or a supermarket will be described as an example. The refrigerant leakage degree is an example of an accumulated amount of change in the irreversible state.

[Device configuration]
FIG. 1 is a block diagram showing a configuration of a system including a controller 1 having a function of a state detection device according to an embodiment of the present invention. As shown in FIG. 1, in the present system, a refrigeration system 5 that is a system A, a refrigeration system 6 that is a system B, and an outside air temperature sensor 7 are connected to a controller 1.

  The refrigeration system 5 is a system in which the refrigerator 51 maintains the showcases 52 to 54 in a refrigerated temperature range of 4 to 8 ° C. The refrigeration system 6 is a system in which the refrigerator 61 maintains the showcases 62 to 64 in a refrigeration temperature range of −18 to −20 ° C. The refrigerators 51 and 61 are devices that form a refrigeration cycle, respectively, and cool the showcases 52 to 54 and 62 to 64 through heat exchangers in the refrigeration cycle, respectively.

  The controller 1 includes an input / output unit 2, a storage unit 3, and a control unit 4. The controller 1 controls the refrigerators 51 and 61 of the refrigeration systems 5 and 6 so that the showcases 52 to 54 and 62 to 64 are in the set temperature range set by the input / output unit 2, and the refrigeration systems 5 and 6 Control to detect the degree of refrigerant leakage.

  The input / output unit 2 is an input / output device such as a touch panel. The input / output unit 2 inputs various data such as a set temperature zone for the refrigeration systems 5 and 6, and the state of the refrigeration systems 5 and 6, particularly the state of refrigerant leakage and refrigerant leakage. Output various data such as degree prediction.

  The storage unit 3 is a storage device composed of a non-volatile memory such as a flash memory or a secondary storage medium such as a hard disk device. The storage unit 3 is a normal operation data D1, a normal model D2, a Q statistic D3, and a predetermined time Tc. , Q integrated value D4, inclination θ, and predetermined value D5 are stored.

  The control unit 4 controls the entire system, and includes a refrigerator control unit 40, an operation data acquisition unit 41, an outside air temperature acquisition unit 42, a model generation unit 43, a Q statistical value calculation unit 44, a Q integrated value calculation unit 45, and an inclination calculation. Unit 46, determination unit 47, and output control unit 48.

  The refrigerator control unit 40 obtains operation data such as the showcase interior temperature, set temperature, compressor suction pressure, discharge pressure, suction temperature, discharge temperature, and operation frequency in real time, and the interior temperature is the set temperature. Control the compressor so that

  The operation data acquisition unit 41 performs operations such as showcase internal temperature, set temperature, compressor suction pressure, discharge pressure, suction temperature, discharge temperature, and operation frequency at regular intervals to detect the degree of refrigerant leakage. Get the data. The certain time is, for example, 1 minute.

  The outside air temperature acquisition unit 42 acquires the outside air temperature from the outside air temperature sensor 7 at regular intervals.

  The model generation unit 43 generates a normal model D2 for multivariate statistical process management using a plurality of normal operation data D1 stored in advance in the storage unit 3. The normal operation data D1 is operation data for a past period in which no abnormality has occurred.

  The Q statistic value calculation unit 44 chronologically calculates the Q statistic value D3 obtained by performing multivariate statistical process management based on the normal model D2 and the plurality of operation data acquired by the operation data acquisition unit 41. calculate.

  The Q integrated value calculation unit 45 calculates a Q integrated value D4 obtained by integrating the Q statistical value D3 every predetermined time Tc. The predetermined time Tc is set by the input / output unit 2 and stored in the storage unit 3. The predetermined time Tc is, for example, 3 days.

  The inclination calculation unit 46 calculates the inclination θ of the Q integrated value D4 with respect to the predetermined time Tc.

  The determination unit 47 determines the refrigerant leakage degree D (%) based on the inclination θ every predetermined time Tc. Moreover, the determination part 47 determines the progress speed of a refrigerant | coolant leak based on the change of inclination (theta). Further, the determination unit 47 corrects the refrigerant leakage degree D based on the outside air temperature. Furthermore, the determination unit 47 estimates the time until the predetermined value D5 at which the refrigerant leakage level becomes abnormal, based on the time-series change amount of the inclination θ. The predetermined value D5 is stored in advance in the storage unit 3 and is a value that can be changed.

  The output control unit 48 performs control to output the result determined by the determination unit 47 to the input / output unit 2.

[Status detection processing]
Next, with reference to FIG. 2, the state detection process by the controller 1 will be described. As shown in FIG. 2, first, the controller 1 sets a predetermined time Tc via the input / output unit 2 (step S101).

  Thereafter, the operation data acquisition unit 41 acquires operation data every minute, for example (step S102). Furthermore, the outside temperature acquisition part 42 acquires the outside temperature for every minute, for example (step S103). Then, the model generation unit 43 generates a normal model D2 for multivariate statistical process management using a plurality of normal operation data D1 stored in advance in the storage unit 3 (step S104).

  Thereafter, the Q statistical value calculation unit 44 calculates the Q statistical value D3 obtained by performing multivariate statistical process management based on the normal model D2 and the plurality of operation data acquired by the operation data acquisition unit 41. The series is calculated (step S105).

  Thereafter, the Q integrated value calculation unit 45 calculates a Q integrated value D4 obtained by integrating the Q statistical value D3 every predetermined time Tc, for example, every 3 days (step S106).

  And the inclination calculation part 46 calculates inclination (theta) of Q integrated value D4 with respect to predetermined time Tc (step S107).

  Then, the determination unit 47 determines the refrigerant leakage degree D (%) based on the inclination θ every predetermined time Tc (step S108).

  The determination unit 47 determines whether or not the refrigerant leakage degree D is equal to or greater than a predetermined value D5 (step S109). When the refrigerant leakage degree D is equal to or greater than the predetermined value D5 (step S109, Yes), the output control unit 48 displays an abnormality occurrence output indicating that the refrigerant leakage has occurred and a state of the refrigerant leakage degree (step S110). The process proceeds to step S111. On the other hand, if the refrigerant leakage degree D is not equal to or greater than the predetermined value D5 (No in step S109), the process proceeds to step S111 as it is.

  Thereafter, it is determined whether or not there is an instruction to end the process (step S111). If there is no instruction to end the process (No at Step S111), the process proceeds to Step S102, and the inclination θ of the next predetermined time Tc is calculated to determine the refrigerant leakage degree D. On the other hand, when there is an instruction to end the process (Yes in step S111), the process ends.

  As shown in FIG. 3, when abnormality detection is performed that the refrigerant leakage abnormality has occurred when the Q statistical value D3 is equal to or greater than the predetermined Q statistical value Qth, the abnormality may or may not occur over time. Therefore, the abnormality detection of the refrigerant leakage could not be performed stably.

In contrast, in the present embodiment, as shown in FIG. 4, time series Q integrated values D4 (Q1 to Q5: Qn), which are integrated values of the Q statistical value D3, are obtained every predetermined time Tc. A series inclination θ (θ1 to θ5: θn) is calculated. The inclination θn can be obtained by the following equation (1).
θn (°) = tan ⁻¹ (Qn / Tc) × 180 / π (1)

  When refrigerant leakage occurs and progresses, the amount of refrigerant leakage is an irreversible state change. Therefore, as shown in FIG. 4, the time-series inclination θn increases with time. Therefore, when the inclination θn is equal to or greater than the predetermined value D5, it can be stably determined that the degree of refrigerant leakage has become abnormal.

  FIG. 5 is a diagram showing a relationship between a specific inclination θn and the refrigerant leakage degree D. As shown in FIG. As shown in FIG. 5, the refrigerant leakage degree D increases as the inclination θn increases. Since the inclination θn and the refrigerant leakage degree D depend on the outside air temperature range, the outside air temperature is divided into 25 ° C. or more, less than 25 ° C., 15 ° C. or more, and less than 15 ° C., respectively. Therefore, the outside air temperature is detected, and correction is applied to apply the relationship between the gradient θn corresponding to the outside air temperature and the refrigerant leakage degree D.

  Further, the degree of refrigerant leakage D increases with the corresponding inclination θn as the outside air temperature increases. That is, even when the refrigerant leakage degree D is the same, when the outside air temperature increases, the high Q integrated value Qn is calculated. The remaining amount of refrigerant is an amount obtained by subtracting the amount of refrigerant that has decreased corresponding to the degree of refrigerant leakage D, based on the refrigerant amount of 50.0 kg when there is no refrigerant leakage. In FIG. 5, when the refrigerant leakage degree D is 50% and the refrigerant remaining amount is 25.0 kg or more, an abnormality alarm is issued.

  Regardless of the outside air temperature, when the inclination θn is less than 5, the refrigerant leakage degree D is less than 10% and a normal state is obtained. Regardless of the outside air temperature, the refrigerant leakage degree D increases as the slope θn increases.

  For example, when the outside air temperature is less than 25 ° C. and 15 ° C. or more and the inclination θn is 22 (°), it can be determined that the refrigerant leakage degree D is 25 (%) and the refrigerant remaining amount is 37.5 kg. Further, when the outside air temperature is less than 25 ° C. and 15 ° C. or more and the slope θn is 42 (°), it is determined that the refrigerant leakage degree D is 50 (%) and the remaining amount of refrigerant is 50.0 kg, and an abnormal alarm is generated. Be emitted. At this time, when the outside air temperature is 25 ° C. or higher, the inclination θn is less than 50 (°), and the refrigerant leakage degree D does not reach 50 (%), so that an abnormality alarm is not issued.

  Note that the refrigerant leakage degree D is preferably output from the input / output unit 2 even when an abnormality alarm is not issued.

  By the way, when the refrigerant leakage progresses rapidly, the gradient θn has a large gradient change amount Δθ between the gradients θn adjacent in time series. On the other hand, when the refrigerant leakage progresses slowly, the slope change amount Δθ is a small value. Therefore, the determination unit 47 can predict and estimate the period Tx from the current time until the abnormality alarm for the refrigerant leak occurs, using the gradient change amount Δθ indicating the progress speed of the refrigerant leak.

That is, the slope change amount Δθ can be expressed by the following equation (2), where θn is a slope indicating the current refrigerant leak level and θn−1 is a slope indicating the refrigerant leak level one predetermined time Tc. .
Δθ = θn−θn-1 (2)
The period Tx from the present time until the refrigerant leakage abnormality alarm is generated can be obtained by the following equation (3).
Tx = Tc × (θmax−θn) / Δθ (3)
However, θmax is the value of the gradient θ when the abnormality alarm occurs.

  The output control unit 48 displays and outputs this period Tx to the input / output unit 2. For example, as shown in FIG. 6, in the system A (refrigeration system 5), the outside air temperature (average air temperature during the period) is 20 ° C., the refrigerant leakage degree D is 40%, and the period Tx is 80 hours. it's shown. On the other hand, in the system B (refrigeration system 6), the outside air temperature (average temperature within the period) is 20 ° C., the refrigerant leakage degree D is 10%, and the period Tx is 300 hours.

In FIG. 6, the normal inclination θ ₀ and the current inclination θ n are graphically superimposed. Accordingly, the inclination θn of the present time can visually recognize how much the case for the refrigerant leakage amount than the inclination theta ₀ in the normal state.

  As mentioned above, although the embodiment to which the invention made by the present inventors was applied has been described, the present invention is not limited by the description and the drawings that constitute a part of the disclosure of the present invention according to this embodiment. That is, other embodiments, examples, operation techniques, and the like made by those skilled in the art based on the present embodiment are all included in the scope of the present invention.

DESCRIPTION OF SYMBOLS 1 Controller 2 Input / output part 3 Memory | storage part 4 Control part 5,6 Refrigeration system 7 Outside temperature sensor 40 Refrigerator control part 41 Operation data acquisition part 42 Outside temperature acquisition part 43 Model generation part 44 Q statistic value calculation part 45 Q integrated value Calculation unit 46 Inclination calculation unit 47 Judgment unit 48 Output control unit 51, 61 Refrigerators 52-54, 62-64 Showcase D Refrigerant leakage degree D1 Normal operation data D2 Normal model D3 Q statistics D4 Q integrated value D5 predetermined value Qn Q integrated value Qth predetermined Q statistical value Tc predetermined time Tx period Δθ inclination change amount

Claims (9)

A state detection device that detects the cumulative amount of irreversible state changes that occur in equipment,
An operation data acquisition unit for acquiring a plurality of time-series operation data related to the facility;
A model generation unit for generating a normal model for multivariate statistical process management using a plurality of normal operation data;
The Q statistical value obtained by performing the multivariate statistical process management based on the normal model and a plurality of operation data acquired by the operation data acquisition unit is calculated in time series, and the predetermined time interval A slope calculating unit that calculates a Q integrated value obtained by integrating the Q statistical values and calculates a slope of the Q integrated value with respect to the predetermined time;
A determination unit that determines a cumulative amount of the irreversible state change based on the slope of each predetermined time;
An output unit for outputting a determination result of the determination unit;
A state detection device comprising: It has a temperature sensor that detects the outside air temperature,
The state determination device according to claim 1, wherein the determination unit corrects the cumulative amount of the irreversible state change according to an outside air temperature. The determination unit determines that the accumulated amount of the irreversible state change is abnormal when the accumulated amount of the irreversible state change is equal to or greater than a predetermined value,
The state detection apparatus according to claim 1, wherein the output unit outputs that the accumulated amount of the irreversible state change is abnormal.   The state determination device according to claim 1, wherein the determination unit determines a change speed of the irreversible state change based on a time-series change amount of the inclination. The determination unit estimates a time until the accumulated amount of the irreversible state change becomes a predetermined value indicating abnormality based on the time-series change amount of the slope,
The state detection apparatus according to claim 1, wherein the output unit outputs an estimated time.   6. The output unit according to claim 1, wherein the output unit displays the Q integrated value at a normal time and the Q integrated value calculated by the inclination calculating unit in a graphical manner by superimposing the Q integrated value calculated for the predetermined time. The state detection device according to one. The facility is a system using a refrigerator,
The accumulated amount of the irreversible state change is a refrigerant leakage amount of a refrigerator, according to any one of claims 1 to 6.   The plurality of operation data includes a detection temperature of a portion to be frozen, a set temperature, a refrigerant suction pressure of a compressor, a refrigerant discharge pressure, a refrigerant suction temperature, a refrigerant discharge temperature, and an operation frequency. State detection device. A state detection method for detecting the cumulative amount of irreversible state change that occurs in equipment,
An operation data acquisition step for acquiring a plurality of time-series operation data related to the facility;
A model generation step for generating a normal model for multivariate statistical process management using a plurality of normal operation data;
A Q statistic obtained by performing the multivariate statistical process management based on the normal model and a plurality of operation data acquired in the operation data acquisition step is calculated in a time series, and the predetermined time interval A slope calculating step of calculating a Q integrated value obtained by integrating the Q statistics and calculating a slope of the Q integrated value with respect to the predetermined time;
A determination step of determining a cumulative amount of the irreversible state change based on the slope every predetermined time;
An output step for outputting a determination result of the determination step;
The state detection method characterized by including.

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