CN115358352B - Method, apparatus, and medium for locating an anomaly sensor - Google Patents
Method, apparatus, and medium for locating an anomaly sensor Download PDFInfo
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Abstract
Embodiments of the present disclosure relate to methods, devices, and media for locating an anomaly sensor, including: obtaining a plurality of first operating parameters from a plurality of sensors of a system under test, thereby determining a first operating parameter dataset; determining a plurality of second operating parameters; based on the first and second operating parameter data sets, building a first model, a second model, and a third model and determining statistical quantity thresholds corresponding to the first model, the second model, and the third model, respectively; acquiring data to be detected from a plurality of sensors of a system to be detected and analyzing the data to be detected based on the established first model, second model and third model, thereby acquiring a statistical value of the data to be detected corresponding to the first model, second model and third model; and locating a sensor of the plurality of sensors having an abnormality based on a comparison between statistical values and statistical value thresholds corresponding to the first model, the second model, and the third model.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of fault diagnosis, and more particularly, to a method, apparatus, and medium for locating an abnormal sensor.
Background
With the development of intelligence, sensors play an increasingly important role. In various systems (e.g., air conditioning systems), measurement signals of sensors (e.g., sensors of a chiller in an air conditioning system) are the basis for controlling and monitoring the system, and also the basis for energy efficiency analysis and fault diagnosis of the system. Therefore, if the data provided by the sensors is unreliable or inaccurate, decision deviation of the control strategy may be caused, so that energy consumption of the whole system is increased or environmental comfort is reduced, and even the system can operate under an unsafe working condition, so that the system is shut down or damaged, and even safety accidents are caused. In general, the sensor has a problem of measurement deviation after a long-term use, and it is very important to find such abnormality or malfunction of the sensor in time.
Most of current methods for sensor fault detection research are data-driven methods, and a sensor diagnosis method based on Principal Component Analysis (PCA) is widely researched. The principal component analysis method mainly utilizes the high correlation among variables to project data to be detected to a principal component subspace and a residual error subspace respectively, when a fault occurs, the projection in the residual error subspace is increased remarkably, and the projection is analyzed through the construction statistics and the threshold value thereof to detect whether the fault occurs.
The method has strong sensitivity to abnormal data in the sensor and high detection efficiency, but the accuracy of the model depends on the data quality and the working condition coverage range, and in addition, a certain misjudgment rate exists when the specific sensor is positioned to be abnormal. This is because the principal component analysis method is a black box model and does not involve physical relationships between system parameters. Therefore, a problem in the prior art for locating an abnormal sensor is that the abnormal sensor in the system cannot be accurately located when the system has a plurality of sensors.
Disclosure of Invention
In view of the above, the present disclosure provides a method and apparatus for locating an abnormal sensor, enabling an expert rule-based decoupling method to improve the ability of principal component analysis sensor fault diagnosis. The method has the advantages that the rule model is added for optimization through analyzing the physical relation among variables during data cleaning, model training and fault diagnosis, so that the quality of training data, the accuracy of the detection model and the diagnosis efficiency of the abnormal sensor can be effectively improved, and the abnormal sensor can be accurately positioned. The method is suitable for various water chilling unit systems, and can complete the fault detection of the sensor on line.
According to a first aspect of the present disclosure, there is provided a method for locating an anomaly sensor, comprising: obtaining a plurality of first operating parameters from a plurality of sensors of the system under test based on the pre-processing configuration condition, thereby determining a first operating parameter dataset; determining a plurality of second operating parameters based on the first operating parameter dataset; based on the first operating parameter data set and the determined second operating parameter data, building a first model, a second model, and a third model and determining statistical quantity thresholds corresponding to the first model, the second model, and the third model, respectively; acquiring data to be detected from a plurality of sensors of a system to be detected and analyzing the data to be detected based on the established first model, second model and third model, thereby acquiring a statistical value of the data to be detected corresponding to the first model, second model and third model; and locating a sensor of the plurality of sensors having an abnormality based on a comparison between statistical values and statistical value thresholds corresponding to the first model, the second model, and the third model.
According to a second aspect of the present disclosure, there is provided a computing device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect of the disclosure.
In a third aspect of the present disclosure, a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of the first aspect of the present disclosure is provided.
In one embodiment, determining a plurality of second operating parameters based on the first operating parameter dataset comprises: acquiring the outdoor temperature and the host load rate when the tested system runs; dividing the first operating parameter data set into a plurality of first operating parameter data subsets at predetermined temperature intervals according to the acquired outdoor temperature; according to the obtained host load rate, dividing each first operation parameter data subset in the plurality of first operation parameter data subsets into a plurality of groups of low load rate subsets and high load rate subsets by a host load rate threshold; and determining a second operating parameter for the plurality of packets based on the plurality of sets of low load rate subsets and high load rate subsets.
In one embodiment, locating a sensor of the plurality of sensors that has an anomaly based on a comparison between statistical values and statistical threshold values corresponding to the first model, the second model, and the third model comprises: if the statistic values of the first model, the second model and the third model are all smaller than the statistic threshold value, the sensor is abnormal; if the statistic value of the first model is larger than or equal to the statistic threshold value and the statistic values of the second model and the third model are smaller than the statistic threshold value, the exhaust temperature sensor of the tested system is abnormal; if the statistic value of the second model is larger than or equal to the statistic threshold value, positioning a sensor with abnormality according to the residual value of the temperature difference between the inlet and the outlet of the chilled water and the residual value of the logarithmic mean temperature difference of the evaporator; and if the statistic value of the third model is larger than or equal to the statistic threshold value, positioning the sensor with abnormality according to the residual value of the temperature difference between the cooling water inlet and the cooling water outlet and the logarithmic mean temperature difference value of the condenser.
In one embodiment, the sensor for locating the abnormality according to the residual value of the temperature difference between the inlet and the outlet of the chilled water and the residual value of the logarithmic mean temperature difference of the evaporator comprises: if the absolute value of the residual value of the temperature difference between the inlet and the outlet of the chilled water and the logarithmic mean temperature difference value of the evaporator is greater than or equal to a preset threshold value, and the positive sign of the residual value of the temperature difference between the inlet and the outlet of the chilled water is the same as the positive sign of the residual value of the logarithmic mean temperature difference value of the evaporator, the temperature sensor for the inlet of the chilled water is abnormal; if the absolute value of the residual value of the temperature difference between the inlet and the outlet of the chilled water and the logarithmic mean temperature difference value of the evaporator is greater than or equal to a preset threshold value, and the residual value of the temperature difference between the inlet and the outlet of the chilled water and the logarithmic mean temperature difference value of the evaporator are different in sign, the chilled water outlet temperature sensor is abnormal; and if the absolute value of the residual value of the temperature difference between the inlet and the outlet of the chilled water is smaller than a preset threshold value and the absolute value of the residual value of the logarithmic average temperature difference of the evaporator is larger than or equal to the preset threshold value, the evaporation temperature sensor is abnormal.
In one embodiment, the sensor for locating the abnormality based on the residual cooling water inlet and outlet temperature difference and the logarithmic mean temperature difference of the condenser comprises: if the absolute value of the residual difference value of the cooling water inlet and outlet temperature difference and the absolute value of the logarithmic mean temperature difference value of the condenser are greater than or equal to a preset threshold value, and the residual difference value of the cooling water inlet and outlet temperature difference and the logarithmic mean temperature difference value of the condenser are the same in sign, the cooling water inlet temperature sensor is abnormal; if the absolute value of the residual difference value of the cooling water inlet and outlet temperature difference and the absolute value of the logarithmic mean temperature difference value of the condenser are larger than or equal to the preset threshold value, and the residual difference value of the cooling water inlet and outlet temperature difference and the logarithmic mean temperature difference value of the condenser are different in sign, the cooling water outlet temperature sensor is abnormal; and if the absolute value of the residual value of the temperature difference between the cooling water inlet and the cooling water outlet is smaller than a preset threshold value and the absolute value of the residual value of the logarithmic average temperature difference of the condenser is larger than or equal to the preset threshold value, the condensing temperature sensor is abnormal.
In one embodiment, the system under test is a chiller, and the chiller includes a condenser, an evaporator, an expansion device, and a compressor.
In one embodiment, the plurality of sensors includes a chilled water inlet temperature sensor, a chilled water outlet temperature sensor, an evaporation temperature or pressure sensor, a cooling water inlet temperature sensor, a cooling water outlet temperature sensor, a condensation temperature or pressure sensor, and an exhaust temperature sensor.
In one embodiment, the first operating parameter data comprises: by the refrigerated water temperature of intaking that the refrigerated water temperature sensor measured that intake, by the refrigerated water leaving water temperature of temperature sensor measurement, by evaporating temperature or pressure sensor measuring evaporating temperature, by the cooling water temperature of intaking that the cooling water temperature sensor measured that intakes, by the cooling water leaving water temperature of temperature sensor measurement, by the condensation temperature or pressure sensor measuring condensation temperature and by exhaust temperature sensor measuring exhaust temperature.
In one embodiment, the pre-processing configuration conditions include: starting a water chilling unit of the system to be tested for more than preset time; the outlet temperature of the chilled water of the system to be tested is greater than the evaporation temperature and less than the inlet temperature of the chilled water; and the outlet water temperature of the cooling water of the tested system is greater than the inlet water temperature of the cooling water and less than the condensation temperature.
In one embodiment, the second operating parameter data comprises: cooling water inlet and outlet temperature difference, cooling water flow, chilled water inlet and outlet temperature difference, chilled water flow, evaporator logarithmic mean temperature difference and condenser logarithmic mean temperature difference.
It should be understood that the statements in this section are not intended to identify key or critical features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements.
FIG. 1 shows a schematic diagram of an example system for implementing a method for locating an anomaly sensor according to an embodiment of the present invention.
Fig. 2 shows a schematic diagram of an exemplary chiller according to an embodiment of the present disclosure.
FIG. 3 shows a flow chart of a method for locating an anomaly sensor according to an embodiment of the present disclosure.
Fig. 4 shows the correspondence of parameters in the second model B according to an embodiment of the present invention.
Fig. 5 shows the correspondence of parameters in the third model C according to an embodiment of the present invention.
Fig. 6 shows a block diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like may refer to different or the same objects. Other explicit and implicit definitions are also possible below.
As described above, most of current methods of sensor failure detection research are data-driven based methods, and among them, a sensor diagnosis method based on Principal Component Analysis (PCA) is widely studied. The principal component analysis method mainly utilizes the high correlation among variables to project data to be detected to a principal component subspace and a residual error subspace respectively, when a fault occurs, the projection in the residual error subspace is increased remarkably, and the projection is analyzed through the construction statistics and the threshold value thereof to detect whether the fault occurs. The method has strong sensitivity to abnormal data in the sensors and high detection efficiency, but the accuracy of the model depends on the data quality and the working condition coverage range, and in addition, certain misjudgment rate exists when the specific sensor is positioned to be abnormal. This is because the principal component analysis method is a black box model and does not involve physical relationships between system parameters. Therefore, a problem in the prior art for locating an abnormal sensor is that the abnormal sensor in the system cannot be accurately located when the system has a plurality of sensors.
To address at least in part one or more of the above issues and other potential issues, an example embodiment of the present disclosure is directed to a method for locating an anomaly sensor, comprising: obtaining a plurality of first operating parameters from a plurality of sensors of a system under test based on a pre-processing configuration condition, thereby determining a first operating parameter dataset; determining a plurality of second operating parameters based on the first operating parameter dataset; based on the first operating parameter data set and the determined second operating parameter data, building a first model, a second model, and a third model and determining statistical quantity thresholds corresponding to the first model, the second model, and the third model, respectively; acquiring data to be detected from a plurality of sensors of a system to be detected and analyzing the data to be detected based on the established first model, second model and third model, thereby acquiring a statistical value of the data to be detected corresponding to the first model, second model and third model; and locating a sensor of the plurality of sensors having an abnormality based on a comparison between statistical values and statistical value thresholds corresponding to the first model, the second model, and the third model.
In this way, the on-line diagnosis of the sensor included in the tested device can be completed by utilizing the real-time operation parameters of the tested device, so that the sensor with a fault is accurately positioned in the tested system including a plurality of sensors, the accuracy and the reliability of the data measured by the sensor are ensured, and effective data support is provided for the safe operation and the energy-saving control strategy of the tested device. Moreover, the method can be used in various tested devices with corresponding sensors, so that the mobility and the universality are high.
FIG. 1 shows a schematic diagram of an example system 100 for implementing a method for locating an anomaly sensor according to an embodiment of the invention. As shown in fig. 1, the system 100 includes one or more fault diagnosis devices 110 and a system under test 120. The fault diagnosis device 110 and the system under test 120 may perform data interaction via a host communication protocol, for example, over the network 130. In the present disclosure, the system under test 120 may be a system including a plurality of sensors 1201, such as a chiller in a central air conditioning system. The fault diagnosis device 110 may be used to perform fault diagnosis on the system under test 120, including performing fault diagnosis on a plurality of sensors 1201 included in the system under test 120, so as to locate a faulty (or abnormal) sensor among the sensors. The fault diagnosis device 110 may be implemented by a computing device such as a desktop, laptop, notebook, industrial control computer, or cloud platform, which may include at least one processor 1101 and at least one memory 1102 coupled to the at least one processor 1101, the memory 1102 having stored therein instructions executable by the at least one processor 1101 that, when executed by the at least one processor 1101, perform the method 300 as described below. The specific structure of the fault diagnosis device 110 may be, for example, the electronic device 600 described below in conjunction with fig. 6. The system under test 120 may be, for example, a chiller in a central air conditioning system.
Fig. 2 shows a schematic diagram of an exemplary water chiller 200 according to an embodiment of the present disclosure. As shown in fig. 2, the chiller 200 may include four main components, a condenser 202, an evaporator 204, a compressor 206, and an expansion device 208, which are in fluid communication via conduits (represented by lines in fig. 2) to achieve a chiller cooling and heating effect. Specifically, the expansion device 208 is in fluid communication with the condenser 202 and the evaporator 204 via conduits, and the compressor 206 is also in fluid communication with the condenser 202 and the evaporator 204 via conduits. The condenser 202 may receive cooling water from a cooling tower (not shown) through a pipe, and may discharge the cooling water after heat exchange. The evaporator 204 may receive the chilled water through a pipe, and after performing heat exchange on the chilled water, cause the heat-exchanged chilled water to flow out through the pipe. To enable control monitoring of the chiller 200, a plurality of sensors, represented by black dots in FIG. 2, may also be included in the chiller 200, and may include a sensor for measuring the chilled water inlet temperature T 1 Chilled water inlet temperature sensor S in,e Is used for measuring the outlet water temperature T of the chilled water 2 Chilled water outlet temperature sensor S out,e For measuring the evaporation temperature T 5 Of the evaporation temperature or pressure sensor S e For measuring the water inlet temperature T of the cooling water 3 Cooling water inlet temperature sensor S in,c And is used for measuring the outlet water temperature T of the cooling water 4 Cooling water outlet temperature sensor S out,c For measuring the condensation temperature T 6 Condensing temperature or pressure sensor S c For measuring the exhaust gas temperature T 7 Exhaust gas temperature sensor S dis . The solution of the present disclosure can be used to locate sensors in which an abnormality (or fault) has occurred among these sensors.
If the evaporation temperature can not be directly read, the evaporation temperature can be obtained by fitting or calling software according to the evaporation pressure or the suction pressure; if the condensation temperature cannot be directly read, the condensation temperature can be obtained by fitting or calling software according to the condensation pressure or the exhaust pressure; if the exhaust temperature cannot be directly read, the fuel supply temperature or the tank temperature is used instead.
In the case where the system under test is a chiller, the chiller may include a condenser, an evaporator, an expansion device, and a compressor. The plurality of sensors in the device under test may include a chilled water inlet temperature sensor, a chilled water outlet temperature sensor, an evaporation temperature or pressure sensor, a cooling water inlet temperature sensor, a cooling water outlet temperature sensor, a condensation temperature or pressure sensor, and an exhaust temperature sensor.
For clarity, the basic operation of the chiller 200 in cooling operation will be briefly described. The chiller 200 uses the evaporator 204 to exchange heat between the chilled water entering the evaporator 204 and the refrigerant, the refrigerant absorbs the heat load in the water, thereby cooling the water to produce cold water, then the heat is brought to the condenser 202 by the action of the compressor 206, the refrigerant exchanges heat with the entering cooling water in the condenser 202, and the cooling water absorbs the heat and then takes the heat out through the water pipe. As shown in fig. 2, in the refrigeration cycle, low-temperature and low-pressure refrigerant gas subjected to evaporation cooling is initially sucked by the compressor 206, and the gas is compressed into high-temperature and high-pressure gas and sent to the condenser. The high-temperature high-pressure gas is cooled by a condenser and then condensed into high-temperature high-pressure liquid. The high-temperature and high-pressure liquid flows into the expansion device 208, and then is throttled by the expansion device 208 into a low-temperature and low-pressure two-phase refrigerant, and the two-phase refrigerant flows into the evaporator 204 to absorb the heat of the chilled water in the evaporator 204, so that the temperature of the water is reduced. The evaporated refrigerant is sucked back into the compressor 206 and the next refrigeration cycle is repeated. It follows that there should actually be certain constraints between the various operating parameter data for the chiller, which can be determined based on the energy conditioning (e.g., heat transfer mechanism or thermal conditioning) effect of each component in the chiller on the fluid flowing through that component.
FIG. 3 shows a flow diagram of a method 300 for locating an anomaly sensor according to an embodiment of the present disclosure. The method 300 may be performed by the fault diagnosis device 110 shown in fig. 1, and the specific structure of the fault diagnosis device 110 may be shown as the electronic device 600 shown in fig. 6. It should be understood that method 300 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the disclosure is not limited in this respect.
At step 302, the fault diagnosis device 110 may obtain a plurality of first operating parameters from a plurality of sensors of the system under test based on the pre-process configured conditions, thereby determining a first operating parameter data set.
The system under test may be a chiller 200 as shown in fig. 2, and thus in this example, the plurality of first operating parameters may include: temperature sensor S for inlet water of chilled water in,e Measured chilled water inlet water temperature T 1 And a chilled water outlet temperature sensor S out,e Measured chilled water exit temperature T 2 A temperature sensor S for the water fed by the cooling water in,c Measured cooling water inlet temperature T 3 And a temperature sensor S for the outlet water of the cooling water out,c Measured cooling water outlet temperature T 4 By evaporation temperature or pressure sensors S e Measured evaporation temperature T 5 From a condensation temperature or pressure sensor S c Measured condensation temperature T 6 An exhaust gas temperature sensor S dis Measured exhaust gas temperature T 7 。
In one embodiment, the pre-processing configuration condition may include that the chiller of the system under test is turned on for more than a preset time, i.e., the time for the system under test to operate stably or the turn-on time of the system under test has reached or exceeded a threshold time period (e.g., 2 hours). The first operating parameter may be obtained in real time. During this steady state operating time, if all values of a particular first operating parameter data are null values or all values are 0, then the sensor used to sense or measure the first operating parameter data is deemed to be absent and therefore not included in the sensor diagnostic list.
The pre-treatment configuration conditions can also include that the chilled water outlet temperature of the system to be tested is greater than the evaporation temperature and less than the chilled water inlet temperature, and the cooling water outlet temperature of the system to be tested is greater than the cooling water inlet temperature and less than the condensation temperature.
In one embodiment, the fault diagnosis device 110 may further obtain an outdoor temperature when the system under test is running and a host load rate. The first operating parameter is accurately partitioned based on both parameters, thereby making the model built in the subsequent principal component analysis modeling more accurate.
Based on the obtained outdoor temperature, the fault diagnosis device 110 may divide the first operating parameter data set into a plurality of first operating parameter data subsets at predetermined temperature intervals (e.g., 5 ℃). Taking the collected first operation parameter data set a as an example, the data in the data set a may be partitioned by the outdoor temperature corresponding to the operation time of the system to be tested, and for example, the data sets at 5 ℃ intervals are respectively marked as the first operation parameter data subset a 1 、A 2 To A i 。
The fault diagnosis device 110 may further divide each of the plurality of first operating parameter data subsets into a plurality of sets of low load rate subsets and high load rate subsets at a host load rate threshold according to the obtained host load rate. For example, in a first subset A of operating parameter data 1 、A 2 To A i Further partitioning on a data subset basis according to whether the cold water host load rate exceeds a threshold (e.g., 50%), i.e., a first operational parameter data subset A 1 Divided into two groups of low load rate subsets A 10 High load factor subset a 11 (ii) a First subset of operating parameter data A 2 Divided into two groups of low load rate subsets A 20 High load factor subset a 21 Up to a first subset A of operating parameter data i Divided into two groups of low load rate subsets A i0 High load factor subset a i1 . If the collected data has no load rate, the ratio of the real-time power of the cold machine to the rated power can be used as a substitute value of the load rate. For example, the cold water main load rate may be the ratio of the real-time power to the rated power of the compressor. If the real-time power of the compressor cannot be directly read, an electric meter needs to be installed for reading.
Note that both the above temperature interval and the load rate threshold may be dynamically set and adjusted by the user according to the actual operating conditions of the system under test.
At step 304, the fault diagnosis device 110 may determine a plurality of second operating parameters based on the first operating parameter data set.
For example, in the aforementioned example of the chiller, the second plurality of operating parameter data may include chilled water inlet and outlet temperature differences
Mean logarithmic temperature difference of evaporator
Flow rate of chilled water
Temperature difference between cooling water inlet and cooling water outlet
Logarithmic mean temperature difference of condenser
Cooling water flow
。
Temperature difference between inlet and outlet of chilled water
Can be based on the inlet water temperature of chilled water
And the temperature of the outlet water of the chilled water
To be determined. Specifically, the inlet and outlet temperature difference of the chilled water
May be determined based on the following equation (1):
evaporator log meanTemperature difference
Can be based on the inlet water temperature of chilled water
The temperature of the outlet water of the chilled water
And evaporation temperature
And (4) obtaining. Specifically, the evaporator log mean temperature difference
May be determined based on equation (2) below:
actual flow of chilled water
Can be based on power consumption
Enthalpy of induction
Enthalpy of exhaust gas
Enthalpy value of outlet of condenser and inlet temperature of chilled water
And the temperature of the outlet water of the chilled water
To be determined. In particular, the actual flow of chilled water
May be determined based on equation (3) below:
wherein k is a coefficient, the value of k is in a range (0,1), and the value is specifically taken according to factors such as the structure, the cooling form and the operation time of the compressor;
the inspiration enthalpy value is dependent on the inspiration temperature and the evaporation temperature and can also be obtained by fitting or calling software from the evaporation temperature;
is an exhaust enthalpy value, which depends on the exhaust temperature and the condensation temperature, and can be obtained from the condensation temperature and the exhaust temperature by fitting or invoking software;
the enthalpy value of the outlet of the condenser is determined by the temperature before the valve and the condensation temperature, and can also be obtained by fitting or calling software according to the condensation temperature;
the temperature of the inlet water of the chilled water is the temperature;
the temperature of the outlet water of the chilled water is the temperature of the outlet water of the chilled water;
is the constant pressure specific heat capacity of the chilled water (e.g., typically 4.182);
is the compressor power.
Temperature difference between inlet and outlet of cooling water
Can be based on the inlet water temperature of cooling water
And the outlet temperature of the cooling water
To be determined. Specifically, the temperature difference between the cooling water inlet and the cooling water outlet
May be determined based on equation (3) below:
logarithmic mean temperature difference of condenser
May be based on the temperature of the cooling water inlet
Cooling water outlet temperature
And condensation temperature
To be determined. In particular, the logarithmic mean temperature difference of the condenser
May be determined based on equation (5) below:
actual flow calculation of cooling water
Can be based on power consumption
Enthalpy of induction
Enthalpy of exhaust gas
Enthalpy value of outlet of condenser
Cooling water inlet temperature
And the outlet temperature of the cooling water
To be determined. Specifically, the actual flow calculation value of the cooling water
May be determined based on equation (6) below:
wherein k is a coefficient, the value of k is in a range (0,1), and the value is specifically taken according to factors such as the structure, the cooling form and the operation time of the compressor;
the value of the suction enthalpy is determined by the suction temperature and the evaporation temperature, and can also be obtained by fitting or calling software according to the evaporation temperature;
is an exhaust enthalpy value which depends on the exhaust temperature and the condensation temperature and can be obtained by fitting or calling software from the condensation temperature and the exhaust temperature;
to be condensedThe enthalpy value of the outlet of the device is dependent on the temperature before the valve and the condensation temperature, and can also be obtained by fitting or calling software according to the condensation temperature;
the water inlet temperature of the cooling water is set;
the outlet water temperature of the cooling water;
is the constant pressure specific heat capacity of the cooling water (e.g., typically 4.182);
is the compressor power.
At step 306, fault diagnostic device 110 may establish a first model a, a second model B, and a third model C based on the first set of operating parameters and the determined second set of operating parameter data and determine statistical quantity thresholds corresponding to the first model a, the second model B, and the third model C, respectively.
In one embodiment, the first model a may be determined by the following parameters: inlet water temperature T of chilled water 1 The temperature T of the outlet water of the chilled water 2 Cooling water inlet temperature T 3 Cooling water outlet temperature T 4 Evaporation temperature T 5 Condensation temperature T 6 Exhaust temperature T 7 Flow rate of chilled water
Cooling water flow
And a cold water main machine load rate W. The cold water main machine load rate can select the real-time power of the compressor
Ratio to rated power. The first model A can represent a system level model of a tested system, and the modeling mode can be a pivot analysis field common modeThe modeling method used.
The second model B may be determined by the following parameters: inlet water temperature T of chilled water 1 And the outlet water temperature T of the chilled water 2 Evaporation temperature T 5 Temperature difference between inlet and outlet of chilled water
Mean logarithmic temperature difference of evaporator
Flow rate of chilled water
And the load rate W of the cold water main engine. The cold water main machine load rate can select the real-time power of the compressor
Ratio to rated power. The second model B may represent an evaporator model of the system under test, and the modeling may be performed in a modeling method commonly used in the field of principal component analysis.
The third model C may be determined by the following parameters: inlet temperature T of cooling water 3 Cooling water outlet temperature T 4 Condensation temperature T 6 Temperature difference between cooling water inlet and cooling water outlet
Logarithmic mean temperature difference of condenser
Cooling water flow
And a cold water main machine load rate W. The cold water main machine load rate can select the real-time power of the compressor
Ratio to rated power. The third model C can represent a condenser model of the measured system, and the modeling mode can be a modeling method commonly used in the field of principal component analysis.
By the partition data A determined in step 302 10 、A 11 ；A 20 、A 21 And the characteristic value and the characteristic vector of the matrix are calculated after the data matrix of the three models A, B and C under different partitions is established and the data is standardized, the number of principal elements is determined by adopting a principal element contribution percentage method (for example, the percentage is 85 percent), and the principal element vector, the residual vector and a statistic threshold Q representing normal data are determined according to the number of the principal elements 0 And waiting for key detection parameters, so as to complete the modeling of normal operation data.
Based on the established model, a statistic threshold Q corresponding to the first model A, the second model B and the third model C under different partitioned data can be determined 0 . And after the data to be detected are brought in, if the statistical quantity of the model is higher than the statistical quantity threshold value, the fact that the sensor of the system to be detected is possibly abnormal is indicated. If the threshold value is not exceeded, the abnormal sensor is temporarily not found, otherwise, the abnormal sensor is found, and the abnormality needs to be further positioned. And then positioning the abnormal sensor according to the mutual influence relation among the parameters.
Note that the statistic of the model (statistic of the residual value) being higher than the threshold value or exceeding the threshold value means that the data group proportion of the data of the model (for example, the first model a) satisfying the threshold condition exceeds a preset percentage. When a model is constructed using multiple sets of data, each set of data can calculate a statistic for determining whether a sensor is abnormal.
Note that the statistical quantity thresholds for the first model a, the second model B, and the third model C may be the same or different.
In step 308, the fault diagnosis device 110 may acquire data to be detected from a plurality of sensors of the system under test and analyze the data to be detected based on the established first model, the second model and the third model, so as to acquire a statistical value of the data to be detected corresponding to the first model, the second model and the third model.
In one embodiment, the fault diagnosis device 110 may be a chilled water inlet water temperature sensor S in,e Obtaining the current inlet water temperature T of the chilled water 1 A temperature sensor S for outputting water from the chilled water out,e Obtaining the current outlet water temperature T of the chilled water 2 A temperature sensor S for the water fed by the cooling water in,c Obtaining the current inlet water temperature T of the cooling water 3 And a temperature sensor S for the outlet water of the cooling water out,c Obtaining the current outlet water temperature T of the cooling water 4 By evaporation temperature or pressure sensors S e Obtaining the current evaporating temperature T 5 By a condensation temperature or pressure sensor S c Obtaining the current condensing temperature T 6 An exhaust gas temperature sensor S dis Obtaining the current exhaust temperature T 7 And the data is brought into the first model a, the second model B and the third model C established in step 304, so as to obtain the statistical quantities of the current data to be detected corresponding to the first model a, the second model B and the third model C.
And (3) processing the data to be detected according to steps 304 and 306, acquiring data matrixes corresponding to the first model A, the second model B and the third model C according to corresponding partitions, decomposing the data matrixes to be detected of each model on principal component vectors and residual vectors of corresponding training models respectively, and calculating statistic Q of any group of data and residual of each sensor parameter.
In step 310, the failure diagnosing apparatus 110 may locate a sensor having an abnormality among the plurality of sensors based on a comparison between statistical values and statistical value thresholds corresponding to the first model, the second model, and the third model.
In one embodiment, statistics Q of each group of data to be detected and statistics threshold Q of corresponding training model can be compared 0 A comparison is made. And if the statistical magnitude of the first model A, the second model B and the third model C is less than the statistical magnitude threshold value, the sensor has no abnormity.
Note that the statistic of the model (statistic of the residual value) being higher than the threshold value or exceeding the threshold value means that the data group proportion of the data of the model (for example, the first model a) satisfying the threshold condition exceeds a preset percentage. When a model is constructed using multiple sets of data, a statistic can be calculated for each set of data to determine sensor anomalies.
And if the statistic value of the first model A is larger than or equal to the statistic threshold value and the statistic values of the second model B and the third model C are smaller than the statistic threshold value, the exhaust gas temperature sensor of the tested system is abnormal.
And if the statistic value of the second model B is larger than or equal to the statistic threshold value, positioning the sensor with abnormality according to the residual value of the temperature difference between the inlet and the outlet of the chilled water and the residual value of the logarithmic mean temperature difference of the evaporator. The anomaly sensor can be located according to the residual relationship of the parameters shown in fig. 4 and 5.
Fig. 4 shows the correspondence of parameters in the second model B according to an embodiment of the present invention. Specifically, if the absolute value of the residual value of the temperature difference between the chilled water inlet and the chilled water outlet and the absolute value of the residual value of the logarithmic mean temperature difference of the evaporator are greater than or equal to the preset threshold value, and the signs of the residual value of the temperature difference between the chilled water inlet and the chilled water outlet and the residual value of the logarithmic mean temperature difference of the evaporator are the same, the chilled water inlet temperature sensor is abnormal, for example, the chilled water inlet temperature is in positive deviation under the condition that the residual value of the temperature difference between the chilled water inlet and the chilled water outlet and the residual value of the logarithmic mean temperature difference of the evaporator are both positive, namely the chilled water inlet temperature sensor is abnormal; if the absolute value of the residual value of the temperature difference between the inlet and the outlet of the chilled water and the logarithmic mean temperature difference value of the evaporator are greater than or equal to a preset threshold value, and the residual value of the temperature difference between the inlet and the outlet of the chilled water and the logarithmic mean temperature difference value of the evaporator are different in sign, the temperature sensor of the chilled water is abnormal, for example, the temperature of the chilled water is negatively deviated under the condition that the residual value of the temperature difference between the inlet and the outlet of the chilled water is positive and the residual value of the logarithmic mean temperature difference of the evaporator is negative, namely the temperature sensor of the chilled water is abnormal; and if the absolute value of the residual value of the temperature difference between the inlet and the outlet of the chilled water is smaller than the preset threshold value and the absolute value of the residual value of the logarithmic average temperature difference of the evaporator is larger than or equal to the preset threshold value, the evaporation temperature sensor is abnormal.
Fig. 5 shows the correspondence of parameters in the third model C according to an embodiment of the present invention. Specifically, if the statistic value of the third model C is greater than or equal to the statistic threshold value, the sensor with abnormality is located according to the residual value of the cooling water inlet and outlet temperature difference and the residual value of the logarithmic mean temperature difference of the condenser. Specifically, if the absolute value of the residual difference value between the cooling water inlet and the cooling water outlet and the absolute value of the logarithmic mean temperature difference value of the condenser are greater than or equal to the preset threshold value, and the residual difference value between the cooling water inlet and the cooling water outlet and the logarithmic mean temperature difference value of the condenser have the same sign, the cooling water inlet temperature sensor is abnormal, for example, in the case that the residual difference value between the cooling water inlet and the cooling water outlet and the logarithmic mean temperature difference value of the condenser are both negative, the cooling water inlet temperature is positively deviated, that is, the cooling water inlet temperature sensor is abnormal; if the absolute value of the residual difference value of the cooling water inlet and outlet temperature difference and the absolute value of the logarithmic mean temperature difference value of the condenser are greater than or equal to the preset threshold value, and the residual difference value of the cooling water inlet and outlet temperature difference and the logarithmic mean temperature difference value of the condenser are different in sign, the cooling water outlet temperature sensor is abnormal, for example, under the condition that the residual difference value of the cooling water inlet and outlet temperature difference is positive and the logarithmic mean temperature difference value of the condenser is negative, the cooling water outlet temperature is positively deviated, namely, the cooling water outlet temperature sensor is abnormal; and if the absolute value of the residual value of the temperature difference between the inlet and the outlet of the cooling water is smaller than the preset threshold value and the absolute value of the residual value of the logarithmic average temperature difference of the condenser is larger than or equal to the preset threshold value, the condensing temperature sensor is abnormal.
By adopting the means, the method and the device can complete online diagnosis of the sensors included in the tested equipment by utilizing the real-time operation parameters of the tested equipment, so that the sensors with faults can be found in time. After the sensor with the abnormality is positioned, corresponding information can be output so that a professional can take corresponding measures to process the information. According to the invention, additional sensors such as temperature or flow and the like are not required to be added, the on-line detection of a fault sensor can be completed by reading the running parameters of the water chilling unit and combining a professional mechanism model based on principal component analysis, the abnormal sensor can be found in time, the accuracy and reliability of data are ensured, and effective data support is provided for the safe running and energy-saving control strategy of the unit.
FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. For example, the fault diagnosis device 110 as shown in fig. 1 may be implemented by the electronic device 600. As shown, electronic device 600 includes a Central Processing Unit (CPU) 601 that can perform various appropriate actions and processes according to computer program instructions stored in a Read Only Memory (ROM) 602 or loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the random access memory 603, various programs and data required for the operation of the electronic apparatus 600 can also be stored. The central processing unit 601, the read only memory 602, and the random access memory 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the electronic device 600 are connected to the input/output interface 605, including: an input unit 606 such as a keyboard, a mouse, a microphone, and the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the electronic device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The various processes and processes described above, such as method 300, may be performed by central processing unit 601. For example, in some embodiments, the method 300 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 600 via the read only memory 602 and/or the communication unit 609. When the computer program is loaded into the random access memory 603 and executed by the central processing unit 601, one or more of the actions of the method 300 described above may be performed.
The present disclosure relates to methods, apparatuses, systems, electronic devices, computer-readable storage media and/or computer program products. The computer program product may include computer-readable program instructions for performing various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge computing devices. The network adapter card or network interface in each computing/processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (8)
1. A method for locating an anomaly sensor, comprising:
based on the pre-processing configuration conditions, obtaining a plurality of first operating parameters from a plurality of sensors of the system to be tested, thereby determining a first operating parameter data set, wherein the first operating parameters comprise chilled water inlet water temperature, chilled water outlet water temperature, evaporation temperature, cooling water inlet water temperature, cooling water outlet water temperature, condensation temperature, and exhaust temperature;
determining a plurality of second operating parameters based on the first operating parameter dataset, wherein determining the plurality of second operating parameters comprises obtaining an outdoor temperature and a host load rate when the system to be tested operates; dividing the first operating parameter data set into a plurality of first operating parameter data subsets at predetermined temperature intervals according to the acquired outdoor temperature; according to the obtained host load rate, dividing each first operation parameter data subset in the plurality of first operation parameter data subsets into a plurality of groups of low load rate subsets and high load rate subsets by a host load rate threshold; determining a plurality of grouped second operating parameters based on the plurality of groups of low load rate subsets and high load rate subsets, wherein the second operating parameters comprise cooling water inlet and outlet temperature differences, cooling water flow, chilled water inlet and outlet temperature differences, chilled water flow, evaporator logarithmic mean temperature differences and condenser logarithmic mean temperature differences;
establishing a first model, a second model and a third model based on the first operational parameter data set and the determined second operational parameter data and determining statistical quantity thresholds corresponding to the first model, the second model and the third model respectively, wherein the first model is a system level model, the second model is an evaporator model and the third model is a condenser model;
acquiring data to be detected from a plurality of sensors of a system to be detected and analyzing the data to be detected based on the established first model, second model and third model, thereby acquiring a statistical value of the data to be detected corresponding to the first model, second model and third model; and
locating a sensor of the plurality of sensors having an anomaly based on a comparison between statistical values corresponding to the first model, the second model, and the third model and a statistical value threshold, wherein the sensor has no anomaly if the statistical values of the first model, the second model, and the third model are all less than the statistical value threshold; if the statistic value of the first model is larger than or equal to the statistic threshold value and the statistic values of the second model and the third model are smaller than the statistic threshold value, the exhaust temperature sensor of the tested system is abnormal; if the statistic value of the second model is larger than or equal to the statistic threshold value, positioning a sensor with abnormality according to the residual value of the temperature difference between the inlet and the outlet of the chilled water and the residual value of the logarithmic mean temperature difference of the evaporator; and if the statistic value of the third model is larger than or equal to the statistic threshold value, positioning the sensor with abnormality according to the residual value of the temperature difference between the cooling water inlet and the cooling water outlet and the logarithmic mean temperature difference value of the condenser.
2. The method of claim 1, wherein locating the sensor having the abnormality based on the residual value of the chilled water inlet/outlet temperature difference and the residual value of the mean logarithmic temperature difference of the evaporator comprises:
if the absolute value of the residual value of the temperature difference between the inlet and the outlet of the chilled water and the logarithmic mean temperature difference value of the evaporator is greater than or equal to a preset threshold value, and the positive sign of the residual value of the temperature difference between the inlet and the outlet of the chilled water is the same as the positive sign of the residual value of the logarithmic mean temperature difference value of the evaporator, the temperature sensor for the inlet of the chilled water is abnormal;
if the absolute value of the residual value of the temperature difference between the inlet and the outlet of the chilled water and the logarithmic mean temperature difference value of the evaporator is greater than or equal to a preset threshold value, and the residual value of the temperature difference between the inlet and the outlet of the chilled water and the logarithmic mean temperature difference value of the evaporator are different in sign, the chilled water outlet temperature sensor is abnormal; and
and if the absolute value of the residual value of the temperature difference between the inlet and the outlet of the chilled water is smaller than the preset threshold value and the absolute value of the residual value of the logarithmic average temperature difference of the evaporator is larger than or equal to the preset threshold value, the evaporation temperature sensor is abnormal.
3. The method of claim 1, wherein locating a sensor having an anomaly based on a cooling water inlet and outlet temperature difference residual and a condenser log mean temperature difference residual comprises:
if the absolute value of the residual difference value of the cooling water inlet and outlet temperature difference and the absolute value of the logarithmic mean temperature difference value of the condenser are greater than or equal to a preset threshold value, and the residual difference value of the cooling water inlet and outlet temperature difference and the logarithmic mean temperature difference value of the condenser are the same in sign, the cooling water inlet temperature sensor is abnormal;
if the absolute value of the residual difference value of the cooling water inlet and outlet temperature difference and the absolute value of the logarithmic mean temperature difference value of the condenser are larger than or equal to the preset threshold value, and the residual difference value of the cooling water inlet and outlet temperature difference and the logarithmic mean temperature difference value of the condenser are different in sign, the cooling water outlet temperature sensor is abnormal; and
and if the absolute value of the residual value of the temperature difference between the cooling water inlet and the cooling water outlet is smaller than the preset threshold value and the absolute value of the residual value of the logarithmic average temperature difference of the condenser is larger than or equal to the preset threshold value, the condensing temperature sensor is abnormal.
4. The method of claim 1, wherein the system under test is a chiller, and the chiller comprises a condenser, an evaporator, an expansion device, and a compressor.
5. The method of claim 1, wherein the plurality of sensors includes a chilled water inlet temperature sensor, a chilled water outlet temperature sensor, an evaporation temperature or pressure sensor, a cooling water inlet temperature sensor, a cooling water outlet temperature sensor, a condensation temperature or pressure sensor, and an exhaust temperature sensor.
6. The method of claim 1, the pre-processing configuration condition comprising:
the water chilling unit of the system to be tested is started for more than preset time;
the outlet temperature of the chilled water of the system to be tested is greater than the evaporation temperature and less than the inlet temperature of the chilled water; and
the outlet temperature of the cooling water of the system to be measured is greater than the inlet temperature of the cooling water and less than the condensation temperature.
7. A computing device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor;
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
8. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-6.
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Address after: No. 118, Building C1, No.1 Qingsheng Avenue, Nansha District, Guangzhou City, Guangdong Province, 511455 Patentee after: Guangdong Mushroom IoT Technology Co.,Ltd. Country or region after: China Address before: 518109 room 2202, building 1, Huide building, Beizhan community, Minzhi street, Longhua District, Shenzhen, Guangdong Patentee before: MOGULINKER TECHNOLOGY (SHENZHEN) CO.,LTD. Country or region before: China |