Refrigeration facility health trend diagnosis method and system based on real-time equipment monitoring

Abstract

The invention relates to the technical field of refrigeration facility operation monitoring, in particular to a refrigeration facility health trend diagnosis method and system based on real-time equipment monitoring, and the method comprises the steps: obtaining the filtering process vibration data of a dry filter in a refrigeration system, and generating a filtering vibration health index; judging whether the dry filter vibrates abnormally or not according to the filtering vibration health index; if not, dry filtering state data of a dry filter in the refrigerating system are obtained, and a pressure drop deviation trend factor is generated according to the dry filtering state data; whether the pressure drop deviation trend factor is larger than or equal to a standard pressure drop trend threshold value or not is judged, if yes, a filter health diagnosis abnormity instruction is generated, and refrigeration facility management personnel are instructed to conduct maintenance according to the filter health diagnosis abnormity instruction. Rapid health diagnosis based on vibration monitoring is realized, and multi-dimensional judgment of the health state of the refrigeration system facility is realized by combining the pressure drop factor.

Description

Technical Field

[0001] The present application relates to the technical field of refrigeration facility operation monitoring, and in particular to a refrigeration facility health trend diagnosis method and system based on real-time equipment monitoring. Background Art

[0002] A refrigeration system is a collection of equipment and processes used to reduce or maintain low temperatures, usually by removing heat from the system.

[0003] In order to ensure the long-term stable operation of the refrigeration system, reduce equipment failures and energy consumption, and improve system efficiency, it is particularly important to maintain and monitor the various components of the system, especially the filter drier. In the prior art, the filter drier is generally inspected and replaced regularly. However, the health status of the filter may not show problems immediately, and manual monitoring is not suitable for high-risk, long-term refrigeration systems.

[0004] In addition, there are solutions in the prior art that use sensors for detection, but most of them have the problem of low data analysis accuracy, which causes minor fault changes to be ignored, leading to premature or late maintenance and health warnings, resulting in low efficiency in monitoring the health trends of the refrigeration system due to inaccurate and untimely health diagnosis. Summary of the invention

[0005] Based on this, it is necessary to provide a method and system for refrigeration facility health trend diagnosis based on real-time equipment monitoring that can solve the problem of low efficiency in monitoring the health trend of the refrigeration system due to inaccurate and untimely health diagnosis, and realize rapid health diagnosis based on vibration monitoring, and then realize multi-dimensional judgment of the health status of refrigeration system facilities through comprehensive pressure drop factors.

[0006] The technical solution of the present invention is as follows: A method for diagnosing health trends of refrigeration facilities based on real-time equipment monitoring, the method comprising: Acquire filtration process vibration data of a filter drying system in a refrigeration system, and generate a filtration vibration health index according to the filtration process vibration data; determining whether the filter drier vibrates abnormally according to the filter vibration health index; If the judgment is no, obtaining the drying and filtering state data of the drying filter in the refrigeration system, and generating the pressure drop deviation trend factor according to the drying and filtering state data; It is determined whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold. If so, a filter health diagnosis abnormality instruction is generated, and the refrigeration facility management personnel are instructed to perform maintenance according to the filter health diagnosis abnormality instruction.

[0007] Optionally, the filtered vibration health index includes a fault frequency deviation value and a vibration frequency fluctuation index; Generating a filtered vibration health index according to the filtered process vibration data includes: Extracting data according to the vibration data of the filtering process according to a preset sampling window, and generating a plurality of vibration process window data; Obtaining the number of calibration points of pre-calibrated frequency points in the standard fault frequency segment, and setting the number of calibration points as the number of frequency sampling; Sampling data from the vibration process window data according to the frequency sampling quantity, and generating a plurality of sampling frequency segments, wherein the number of sampling frequency points in the sampling frequency segment is the same as the number of pre-calibrated frequency points in the standard fault frequency segment; Generating a fault frequency deviation value according to each sampling frequency point in the sampling frequency segment and each pre-calibrated frequency point in the standard fault frequency segment, wherein each sampling frequency segment corresponds to a fault frequency deviation value; According to the fault frequency deviation value corresponding to each sampling frequency segment in the vibration process window data, a vibration frequency fluctuation index is generated based on the following formula: ; Wherein, VFC is the vibration frequency fluctuation index, N is the number of fault frequency deviation values ​​corresponding to the sampling frequency segment, DVF is the fault frequency deviation value, is the fault frequency deviation value corresponding to the jth sampling frequency segment, j is the serial number of the sampling frequency segment, is the mean value of the fault frequency deviation value corresponding to the sampling frequency segment.

[0008] Optionally, the fault frequency deviation value DVF is calculated based on the following formula: ; Where DVF is the fault frequency deviation value, n is the number of calibration points, is the frequency value of the i-th sampling frequency point, is the frequency value of the i-th pre-calibrated frequency point.

[0009] Optionally, judging whether the filter drier has abnormal vibration according to the filter vibration health index includes: Determine whether the fault frequency deviation value is less than or equal to a preset first deviation threshold; If the answer is yes, it is determined that the filter drying device has abnormal vibration; If the judgment is no, determining whether the vibration frequency fluctuation index is greater than or equal to a preset second deviation threshold; If the answer is yes, it is determined that the filter drying device has abnormal vibration; If the determination result is negative, it is determined that the filter drier is not vibrating abnormally.

[0010] Optionally, judging whether the filter drying device has abnormal vibration according to the filter vibration health index, and then further comprising: If the judgment is yes, a vibration health abnormality instruction is generated; The vibration health abnormality instruction is sent to a refrigeration facility manager, wherein the vibration health abnormality instruction is used to instruct the refrigeration facility manager to perform vibration abnormality maintenance on the drying filter.

[0011] Optionally, generating a pressure drop deviation trend factor according to the dry filtration state data includes: Extracting refrigerant status data, filter status data and filter inlet and outlet pressure data according to the dry filter status data, wherein the filter inlet and outlet pressure data include the inlet pressure and outlet pressure of the dry filter; generating a refrigerant influence coefficient according to the refrigerant state data; generating a dryer influence coefficient according to the filter status data; The pressure drop deviation trend factor is generated based on the following formula: ; in, is the pressure drop deviation trend factor at the data sampling time point t, is the outlet pressure of the filter drier at the data sampling time point t, is the inlet pressure of the filter drier at the data sampling time point t, is the dynamic viscosity of the refrigerant at the data sampling time point t, L is the length of the filter filling layer of the dry filter, is the permeability coefficient of the filter drier, is the filtration cross-sectional area of ​​the filter drier, is the flow rate of the refrigerant at the data sampling time point t, is the refrigerant influence coefficient at the data sampling time point t, is the dryer influence coefficient at the data sampling time point t.

[0012] Optionally, the refrigerant state data includes refrigerant mass flow rate, refrigerant density, and refrigerant dynamic viscosity; and the refrigerant influence coefficient is generated based on the following formula: ; in, is the refrigerant influence coefficient at the data sampling time point t, is the basic influence coefficient, is the refrigerant mass flow rate at the data sampling time point t, is the mass flow reference value, is the flow index, is the refrigerant density at the data sampling time point t, is the density reference value, is the density index, is the dynamic viscosity of the refrigerant at the data sampling time point t, is the reference value of dynamic viscosity, is the viscosity index.

[0013] Optionally, the refrigerant status data further includes a refrigerant flow rate; Generating a dryer influence coefficient according to the filter status data includes: Acquire the usage time of the drying filter according to the filter status data; Generate filter element influence coefficient and filter medium influence coefficient according to the used time; updating the standard deviation of the historical flow rate and the mean of the historical flow rate according to the refrigerant flow rate, and generating the standard deviation of the real-time flow rate and the mean of the real-time flow rate; The dryer influence factor is generated based on the following formula: ; in, is the dryer influence coefficient, is the filter medium influence coefficient, is the filter element influence coefficient, is the roughness of the filter element surface in the dry filter, is the particle diameter of the filter medium in the dry filter, G is the filter shape influence coefficient of the filter, is the porosity of the filter element in the filter drier, is the real-time flow rate standard deviation, is the real-time flow rate average.

[0014] Optionally, judging whether the pressure drop deviation trend factor is greater than or equal to a standard pressure drop trend threshold value further includes: If the judgment is no, a normal pressure drop deviation trend instruction is generated; A current health assessment report is generated according to the pressure drop deviation trend normal instruction, and the current health assessment report is stored.

[0015] Optionally, the present application also provides a refrigeration facility health trend diagnosis system based on real-time equipment monitoring, the system comprising: A vibration health generation module, used for acquiring vibration data of a filtration process of a dry filter in a refrigeration system, and generating a filtration vibration health index according to the vibration data of the filtration process; A vibration abnormality judgment module, used for judging whether the drying filter has abnormal vibration according to the filter vibration health index; A pressure deviation generating module, for obtaining drying and filtering state data of a drying filter in the refrigeration system if the judgment is no, and generating a pressure drop deviation trend factor according to the drying and filtering state data; The health diagnosis generation module is used to determine whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold. If it is judged to be yes, a filter health diagnosis abnormality instruction is generated, and the refrigeration facility management personnel are instructed to perform maintenance according to the filter health diagnosis abnormality instruction.

[0016] Optionally, the filtered vibration health index includes a fault frequency deviation value and a vibration frequency fluctuation index; the vibration health generation module is also used to: extract data according to the preset sampling window based on the vibration data of the filtering process, and generate multiple vibration process window data; obtain the number of calibration points of the pre-calibrated frequency points in the standard fault frequency segment, and set the number of calibration points as the frequency sampling number; sample data from the vibration process window data according to the frequency sampling number, and generate multiple sampling frequency segments, wherein the number of sampling frequency points in the sampling frequency segment is the same as the number of pre-calibrated frequency points in the standard fault frequency segment; generate a fault frequency deviation value based on each sampling frequency point in the sampling frequency segment and each pre-calibrated frequency point in the standard fault frequency segment, wherein each sampling frequency segment corresponds to a fault frequency deviation value; based on the following formula, generate a vibration frequency fluctuation index according to the fault frequency deviation value corresponding to each sampling frequency segment in the vibration process window data: ; Wherein, VFC is the vibration frequency fluctuation index, N is the number of fault frequency deviation values ​​corresponding to the sampling frequency segment, DVF is the fault frequency deviation value, is the fault frequency deviation value corresponding to the jth sampling frequency segment, j is the serial number of the sampling frequency segment, is the mean value of the fault frequency deviation value corresponding to the sampling frequency segment.

[0017] Optionally, the vibration health generation module is further used to calculate a fault frequency deviation value DVF based on the following formula: ; Where DVF is the fault frequency deviation value, n is the number of calibration points, is the frequency value of the i-th sampling frequency point, is the frequency value of the i-th pre-calibrated frequency point.

[0018] Optionally, the vibration abnormality judgment module is also used to: judge whether the fault frequency deviation value is less than or equal to a preset first deviation threshold; if the judgment is yes, judge that the vibration of the drying filter is abnormal; if the judgment is no, judge whether the vibration frequency fluctuation index is greater than or equal to a preset second deviation threshold; if the judgment is yes, judge that the vibration of the drying filter is abnormal; if the judgment is no, judge that the drying filter is not abnormal in vibration.

[0019] Optionally, the vibration abnormality judgment module is also used to: if the judgment is yes, generate a vibration health abnormality instruction; send the vibration health abnormality instruction to the refrigeration facility management personnel, wherein the vibration health abnormality instruction is used to instruct the refrigeration facility management personnel to perform vibration abnormality maintenance on the drying filter.

[0020] Optionally, the pressure deviation generating module is further used to: extract refrigerant state data, filter state data and filter inlet and outlet pressure data according to the dry filter state data, wherein the filter inlet and outlet pressure data include the inlet pressure and outlet pressure of the dry filter; generate a refrigerant influence coefficient according to the refrigerant state data; generate a dryer influence coefficient according to the filter state data; and generate a pressure drop deviation trend factor based on the following formula: ; in, is the pressure drop deviation trend factor at the data sampling time point t, is the outlet pressure of the filter drier at the data sampling time point t, is the inlet pressure of the filter drier at the data sampling time point t, is the dynamic viscosity of the refrigerant at the data sampling time point t, L is the length of the filter filling layer of the dry filter, is the permeability coefficient of the filter drier, is the filtration cross-sectional area of ​​the filter drier, is the flow rate of the refrigerant at the data sampling time point t, is the refrigerant influence coefficient at the data sampling time point t, is the dryer influence coefficient at the data sampling time point t.

[0021] Optionally, the refrigerant state data includes refrigerant mass flow, refrigerant density, and refrigerant dynamic viscosity; the pressure deviation generating module is further used to: generate a refrigerant influence coefficient based on the following formula: ; in, is the refrigerant influence coefficient at the data sampling time point t, is the basic influence coefficient, is the refrigerant mass flow rate at the data sampling time point t, is the mass flow reference value, is the flow index, is the refrigerant density at the data sampling time point t, is the density reference value, is the density index, is the dynamic viscosity of the refrigerant at the data sampling time point t, is the reference value of dynamic viscosity, is the viscosity index.

[0022] Optionally, the refrigerant state data also includes a refrigerant flow rate; the pressure deviation generating module is further used to: obtain the use time of the drying filter according to the filter state data; generate a filter element influence coefficient and a filter medium influence coefficient according to the use time; update the standard deviation and the mean of the historical flow rate according to the refrigerant flow rate, and generate a real-time flow rate standard deviation and a real-time flow rate mean; generate a dryer influence coefficient based on the following formula: ; in, is the dryer influence coefficient, is the filter medium influence coefficient, is the filter element influence coefficient, is the roughness of the filter element surface in the dry filter, is the particle diameter of the filter medium in the dry filter, G is the filter shape influence coefficient of the filter, is the porosity of the filter element in the filter drier, is the real-time flow rate standard deviation, is the real-time flow rate average.

[0023] Optionally, the health diagnosis generation module is also used to: if the judgment is no, generate a pressure drop deviation trend normal instruction; generate a current health assessment report according to the pressure drop deviation trend normal instruction, and store the current health assessment report.

[0024] Optionally, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps described in the above-mentioned method for diagnosing the health trend of refrigeration facilities based on real-time equipment monitoring are implemented.

[0025] Optionally, a computer-readable storage medium is also provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned refrigeration facility health trend diagnosis method based on real-time equipment monitoring are implemented.

[0026] The technical effects achieved by the present invention are as follows: The above-mentioned refrigeration facility health trend diagnosis method and system based on real-time equipment monitoring obtains the filtration process vibration data of the dry filter in the refrigeration system, and generates a filtration vibration health index based on the filtration process vibration data; determines whether the dry filter vibrates abnormally based on the filtration vibration health index; if the judgment is no, obtains the dry filtration status data of the dry filter in the refrigeration system, and generates a pressure drop deviation trend factor based on the dry filtration status data; determines whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold value, and if the judgment is yes, generates a filter health diagnosis abnormality instruction, and instructs the refrigeration facility management personnel to perform maintenance based on the filter health diagnosis abnormality instruction. In the present application, in order to quickly detect the refrigeration facilities in the refrigeration system, the dry filter between the condenser and the expansion valve is selected as the core of the equipment monitoring. In order to quickly make a health judgment, the dry filter is first subjected to vibration detection, specifically to obtain the vibration data of the filtration process of the dry filter in the refrigeration system, and generate a filtration vibration health index based on the filtration process vibration data. The filtration vibration health index is used to indicate the degree of influence of the vibration of the dry filter during the filtration process on its working state, and then the filtration vibration health index can be used to determine whether the vibration of the dry filter is abnormal. When it is determined whether the vibration of the dry filter is abnormal, vibration maintenance is performed, thereby realizing rapid health diagnosis based on vibration monitoring, meeting the requirements of rapid diagnosis in health trend diagnosis. If it is determined that the vibration is not abnormal, the dry filter is further subjected to status monitoring and health diagnosis in other dimensions to realize a health diagnosis method of first vibration and then multi-dimensional monitoring, specifically considering the dimension of pressure drop, including obtaining the dry filtration status data of the dry filter in the refrigeration system, and generating a pressure drop deviation trend factor based on the dry filtration status data. Based on the principle that the pressure drop of the filter dryer should be maintained within a certain range when it is working normally, the pressure drop deviation trend factor is generated to represent the fluctuation state of the pressure drop of the filter dryer when it is working and the deviation between the pressure drop corresponding to the normal working state. The larger the pressure drop deviation trend factor is, the greater the fluctuation state of the pressure drop of the filter dryer when it is working and the deviation between the pressure drop corresponding to the normal working state is, and the worse the health trend is. Therefore, it is determined whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold. If it is determined to be yes, a filter health diagnosis abnormality instruction is generated, and the refrigeration facility management personnel are instructed to perform maintenance according to the filter health diagnosis abnormality instruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flowchart of a refrigeration facility health trend diagnosis method based on real-time equipment monitoring in one embodiment; Figure 2The structure block diagram of a refrigeration facility health trend diagnosis system based on real-time equipment monitoring in one embodiment. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0029] In one embodiment, Figure 1 As shown, a refrigeration facility health trend diagnosis method based on real-time equipment monitoring is provided, the method comprising: Step S100: acquiring vibration data of a filtration process of a filter drying system in a refrigeration system, and generating a filtration vibration health index according to the vibration data of the filtration process; Step S200: judging whether the filter drier has abnormal vibration according to the filter vibration health index; Step S300: if the judgment is no, obtaining the drying and filtering state data of the drying filter in the refrigeration system, and generating a pressure drop deviation trend factor according to the drying and filtering state data; Step S400: determine whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold. If so, generate a filter health diagnosis abnormality instruction and instruct the refrigeration facility manager to perform maintenance according to the filter health diagnosis abnormality instruction.

[0030] In this embodiment, in order to quickly detect the refrigeration facilities in the refrigeration system, the dry filter between the condenser and the expansion valve is selected as the core of the equipment monitoring. In order to quickly make a health judgment, the dry filter is first subjected to vibration detection, specifically, the vibration data of the filtration process of the dry filter in the refrigeration system is obtained, and a filtration vibration health index is generated according to the filtration process vibration data. The filtration vibration health index is used to indicate the degree of influence of the vibration of the dry filter during the filtration process on its working state, and then the filtration vibration health index can be used to determine whether the vibration of the dry filter is abnormal. When it is determined whether the vibration of the dry filter is abnormal, vibration maintenance is performed, thereby realizing a rapid health diagnosis based on vibration monitoring, meeting the requirements of rapid diagnosis in health trend diagnosis. If it is determined that the vibration is not abnormal, the dry filter is further subjected to state monitoring and health diagnosis in other dimensions, so as to realize a health diagnosis method of first vibration and then multi-dimensional monitoring, specifically, considering the dimension of pressure drop, including obtaining the dry filtration state data of the dry filter in the refrigeration system, and generating a pressure drop deviation trend factor according to the dry filtration state data. Based on the principle that the pressure drop of the filter dryer should be maintained within a certain range when it is working normally, the pressure drop deviation trend factor is generated to represent the fluctuation state of the pressure drop of the filter dryer when it is working and the deviation between the pressure drop corresponding to the normal working state. The larger the pressure drop deviation trend factor is, the greater the fluctuation state of the pressure drop of the filter dryer when it is working and the deviation between the pressure drop corresponding to the normal working state is, and the worse the health trend is. Therefore, it is determined whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold. If it is determined to be yes, a filter health diagnosis abnormality instruction is generated, and the refrigeration facility management personnel are instructed to perform maintenance according to the filter health diagnosis abnormality instruction, thereby achieving efficient monitoring and high-precision monitoring.

[0031] In one embodiment, the filtered vibration health index includes a fault frequency deviation value and a vibration frequency fluctuation index; In step S100, generating a filtered vibration health index according to the vibration data of the filtering process includes: Step S110: extracting data according to the vibration data of the filtering process according to a preset sampling window, and generating a plurality of vibration process window data; In this step, in order to perform a refined analysis of the data, data is extracted according to the preset sampling window based on the vibration data of the filtering process, and a plurality of vibration process window data are generated. Wherein, the preset sampling window includes two situations, the first being that adjacent preset sampling windows do not overlap, and the second being that adjacent preset sampling windows partially overlap. When adjacent preset sampling windows partially overlap, the degree of refinement of the vibration data analysis is higher. For example, taking the vibration data of the filtering process as a vibration sequence as an example, specifically {f1, f2, f3, f4, f5, f6, f7, f8, f9}, the length of the preset sampling window is 3. When adjacent preset sampling windows do not overlap, the number of vibration process window data is 3, namely {f1, f2, f3}, {f4, f5, f6} and {f7, f8, f9}. When adjacent preset sampling windows partially overlap, one of the situations is that the vibration process window data are {f1, f2, f3}, {f2, f3, f4}, {f3, f4, f5}, {f4, f5, f6}, {f5, f6, f7}, {f6, f7, f8} and {f7, f8, f9} respectively. It should be understood that when adjacent preset sampling windows partially overlap, the amount of vibration process window data is greater, the vibration analysis is more refined and comprehensive, and the vibration abnormality conclusion obtained by analysis is more accurate.

[0032] Step S120: obtaining the number of calibration points of pre-calibrated frequency points in the standard fault frequency segment, and setting the number of calibration points as the frequency sampling number; Step S130: sampling data from the vibration process window data according to the frequency sampling quantity, and generating a plurality of sampling frequency segments, wherein the number of sampling frequency points in the sampling frequency segment is the same as the number of pre-calibrated frequency points in the standard fault frequency segment; In step S120-step S130, a standard fault frequency segment is stored in advance, and a pre-calibrated frequency point is set in the standard fault frequency segment, and the number of pre-calibrated frequency points is also pre-set. In this way, the vibration process window data that needs to be analyzed can be split into more detailed frequency segments based on the number of calibration points of the pre-calibrated frequency points in the standard fault frequency segment, specifically obtaining the number of calibration points of the pre-calibrated frequency points in the standard fault frequency segment, and setting the number of calibration points as the number of frequency samplings, and sampling data from the vibration process window data according to the number of frequency samplings, and generating multiple sampling frequency segments. By pre-setting the number of frequency samplings, the number of sampling frequency points in the sampling frequency segment is made the same as the number of pre-calibrated frequency points in the standard fault frequency segment, thereby facilitating subsequent similarity calculations between the actual frequency segment and the standard fault frequency segment, and improving the efficiency of vibration analysis.

[0033] Step S140: generating a fault frequency deviation value according to each sampling frequency point in the sampling frequency segment and each pre-calibrated frequency point in the standard fault frequency segment, wherein each sampling frequency segment corresponds to a fault frequency deviation value; Step S150: Generate a vibration frequency fluctuation index according to the fault frequency deviation value corresponding to each sampling frequency segment in the vibration process window data based on the following formula: ; Wherein, VFC is the vibration frequency fluctuation index, N is the number of fault frequency deviation values ​​corresponding to the sampling frequency segment, DVF is the fault frequency deviation value, is the fault frequency deviation value corresponding to the jth sampling frequency segment, j is the serial number of the sampling frequency segment, is the mean value of the fault frequency deviation value corresponding to the sampling frequency segment.

[0034] In step S140-step S150, firstly, a fault frequency deviation value is generated according to each sampling frequency point in the sampling frequency segment and each pre-calibrated frequency point in the standard fault frequency segment, so that the fault frequency deviation value integrates each sampling frequency point in the sampling frequency segment and is used to indicate the degree to which the sampling frequency segment approaches the standard fault frequency segment. Then, a vibration frequency fluctuation index is generated by integrating the fault frequency deviation values ​​corresponding to each sampling frequency segment in the vibration process window data. The vibration frequency fluctuation index is used to indicate the fluctuation state of the degree to which the sampling frequency segment approaches the standard fault frequency segment. Specifically, firstly, by calculating To express the difference, and then calculate the overall To indicate the fluctuation state. The larger the value of the vibration frequency fluctuation index, the greater the fluctuation, and the greater the possibility of failure caused by vibration. Therefore, in this embodiment, the vibration data of the filtering process is first sampled, and a plurality of vibration process window data are obtained. Then, the vibration process window data is sampled in a data segment in a manner that matches the standard fault frequency segment and the frequency sampling quantity, and a plurality of sampling frequency segments are generated, thereby achieving the maximum possible splitting and refinement of the vibration data. In terms of data analysis, the fault frequency deviation value is first generated for the sampling frequency segment, and then the vibration frequency fluctuation index reflecting the overall vibration condition of the vibration process window data is generated according to the fault frequency deviation value corresponding to each sampling frequency segment, thereby achieving a refined, accurate and rapid health diagnosis of the drying filter.

[0035] In one embodiment, in step S140, the fault frequency deviation value DVF is calculated based on the following formula: ; Where DVF is the fault frequency deviation value, n is the number of calibration points, is the frequency value of the i-th sampling frequency point, is the frequency value of the i-th pre-calibrated frequency point.

[0036] In this embodiment, by calculating To represent the difference between the sampling frequency segment and the specific frequency value in the standard fault frequency segment, and then calculate To indicate the degree to which the sampling frequency segment approaches the standard fault frequency segment. The smaller the fault frequency deviation value DVF is, the more similar the sampling frequency segment is to the standard fault frequency segment, and the greater the probability of a fault. Therefore, by calculating the difference between the actual frequency segment and the pre-stored fault frequency segment, the probability of abnormal vibration of the current dry filter is measured. By comparing frequency segments, a more comprehensive comparison is achieved, thereby improving the accuracy of the vibration comparison results.

[0037] In one embodiment, step S200: judging whether the filter drying filter has abnormal vibration according to the filter vibration health index comprises: Step S210: determining whether the fault frequency deviation value is less than or equal to a preset first deviation threshold; Step S220: If the answer is yes, then determine that the drying filter is vibrating abnormally; Step S230: If the judgment is no, determining whether the vibration frequency fluctuation index is greater than or equal to a preset second deviation threshold; Step S240: If the answer is yes, then determine that the drying filter is vibrating abnormally; Step S250: If the answer is no, it is determined that the drying filter is not vibrating abnormally.

[0038] In this embodiment, the fault frequency deviation value and the vibration frequency fluctuation index are comprehensively combined to comprehensively judge whether the dry filter has abnormal vibration. First, it is judged whether the fault frequency deviation value is less than or equal to a preset first deviation threshold value. If it is judged to be yes, it means that the sampling frequency segment is more similar to the standard fault frequency segment, so the probability of a fault is greater, so it is judged that the dry filter has abnormal vibration. If it is judged to be no, in order to accurately judge whether there is abnormal vibration, it is further judged whether the vibration frequency fluctuation index is greater than or equal to a preset second deviation threshold value. When it is judged to be yes, it is judged that the fluctuation is too large, so it is judged that the dry filter has abnormal vibration. Otherwise, it is judged that the dry filter does not have abnormal vibration.

[0039] In one embodiment, step S200: judging whether the drying filter has abnormal vibration according to the filter vibration health index, and then further comprising: Step S281: if the judgment is yes, then generate a vibration health abnormality instruction; Step S282: Sending the vibration health abnormality instruction to a refrigeration facility manager, wherein the vibration health abnormality instruction is used to instruct the refrigeration facility manager to perform vibration abnormality maintenance on the drying filter.

[0040] In this embodiment, when the judgment is yes, that is, it is determined that the drying filter has a vibration abnormality, immediate maintenance is required at this time, specifically, a vibration health abnormality instruction is generated, and based on the generated vibration health abnormality instruction, the refrigeration facility manager is instructing to perform vibration abnormality maintenance on the drying filter.

[0041] Specifically, the vibration abnormality inspection includes checking whether the pipeline support is loose, checking whether there is excessive vibration transmission, checking whether the pipeline interface of the filter is tight, and checking whether the pipeline interface is loose or poorly sealed.

[0042] In one embodiment, in step S300, generating a pressure drop deviation trend factor according to the drying and filtering state data includes: Step S310: extracting refrigerant status data, filter status data and filter inlet and outlet pressure data according to the dry filter status data, wherein the filter inlet and outlet pressure data include the inlet pressure and outlet pressure of the dry filter; Step S320: generating a refrigerant influence coefficient according to the refrigerant state data; Step S330: generating a dryer influence coefficient according to the filter status data; Step S340: Generate a pressure drop deviation trend factor based on the following formula: ; in, is the pressure drop deviation trend factor at the data sampling time point t, is the outlet pressure of the filter drier at the data sampling time point t, is the inlet pressure of the filter drier at the data sampling time point t, is the dynamic viscosity of the refrigerant at the data sampling time point t, L is the length of the filter filling layer of the dry filter, is the permeability coefficient of the filter drier, is the filtration cross-sectional area of ​​the filter drier, is the flow rate of the refrigerant at the data sampling time point t, is the refrigerant influence coefficient at the data sampling time point t, is the dryer influence coefficient at the data sampling time point t.

[0043] In this embodiment, the permeability coefficient of the dry filter reflects the permeability of the dry filter and is preset according to the dry filter structure in the dry filter.

[0044] Specifically, when calculating the pressure drop deviation trend factor, first calculate It indicates the difference between the outlet pressure and the inlet pressure of the filter drier, and is used to indicate the actual measured pressure drop. It represents the theoretical pressure drop of the refrigerant in the porous medium of the dry filter, and finally calculates the difference between the two for further calculation. At the same time, the refrigerant influence coefficient and the dryer influence coefficient are also considered, so that the generated pressure drop deviation trend factor is more accurate and comprehensive for evaluating the health status of the current dry refrigerator.

[0045] In one embodiment, the refrigerant state data includes refrigerant mass flow rate, refrigerant density, and refrigerant dynamic viscosity; the refrigerant influence coefficient is generated based on the following formula: ; in, is the refrigerant influence coefficient at the data sampling time point t, is the basic influence coefficient, is the refrigerant mass flow rate at the data sampling time point t, is the mass flow reference value, is the flow index, is the refrigerant density at the data sampling time point t, is the density reference value, is the density index, is the dynamic viscosity of the refrigerant at the data sampling time point t, is the reference value of dynamic viscosity, is the viscosity index.

[0046] In this embodiment, the refrigerant influence coefficient Used to indicate that at the data sampling time point t, It is used to indicate the influence of refrigerant-related flow rate, density, viscosity and other factors on the pressure drop deviation. , density index and viscosity index They are used to adjust the sensitivity of flow rate change, density and dynamic viscosity to the refrigerant influence coefficient, flow index , density index and viscosity index All are pre-set. Mass flow reference value , Density reference value and dynamic viscosity reference value All are pre-set to indicate the values ​​of mass flow rate, refrigerant density and refrigerant dynamic viscosity under standard operating conditions.

[0047] Specifically, by calculating It represents the mass flow rate ratio, which indicates the degree of change of the current flow rate relative to the reference flow rate. represents the density ratio. By calculating Indicates the dynamic viscosity ratio, which indicates the degree of change of the current refrigerant dynamic viscosity relative to the reference viscosity. Combined with the flow index , density index and viscosity index To adjust the sensitivity of flow change, density and dynamic viscosity to the refrigerant influence coefficient, and finally combine the basic influence coefficient To generate the refrigerant influence coefficient corresponding to the current sampling time point t, it is adjusted in combination with the current actual state of the refrigerant to improve the accuracy and sensitivity of the generated pressure drop deviation trend factor.

[0048] In this embodiment, the refrigerant density is obtained by consulting the refrigerant physical property table. For the refrigerant dynamic viscosity, the refrigerant density is first obtained, and then the dynamic viscosity value corresponding to the current temperature and current pressure in the physical property table is found. The refrigerant mass flow rate is calculated by the following formula: ; in, is the refrigerant mass flow rate at the data sampling time point t, is the flow coefficient of the filter drier, is the filtration cross-sectional area of ​​the filter drier, is the outlet pressure of the filter drier at the data sampling time point t, is the inlet pressure of the filter drier at the data sampling time point t, is the refrigerant density at the data sampling time point t.

[0049] Among them, the flow coefficient of the dry filter is It is associated with the fluid characteristics of the filter drier and the refrigerant and is preset. The filter cross-sectional area of ​​the filter drier It is a fixed value set based on the filter drier.

[0050] In one embodiment, the refrigerant status data further includes a refrigerant flow rate; In step S330, generating a dryer influence coefficient according to the filter status data includes: Step S331: acquiring the usage time of the drying filter according to the filter status data; Step S332: generating a filter element influence coefficient and a filter medium influence coefficient according to the usage time; Step S333: updating the standard deviation and mean of the historical flow rate according to the refrigerant flow rate, and generating the standard deviation and mean of the real-time flow rate; Step S334: Generate the dryer influence coefficient based on the following formula: ; in, is the dryer influence coefficient, is the filter medium influence coefficient, is the filter element influence coefficient, is the roughness of the filter element surface in the dry filter, is the particle diameter of the filter medium in the dry filter, G is the filter shape influence coefficient of the filter, is the porosity of the filter element in the filter drier, is the real-time flow rate standard deviation, is the real-time flow rate average.

[0051] In this embodiment, the filter element influence coefficient and the filter medium influence coefficient respectively represent the influence of the use time on the roughness of the filter element surface of the dry filter and the particle diameter of the filter medium. The porosity of the filter element in the dry filter, the roughness of the filter element surface in the dry filter, and the particle diameter of the filter medium in the dry filter are all preset based on the basic conditions of the dry filter. G is a preset value used to represent the influence of the geometric shape of the dry filter on the flow.

[0052] Specifically, by calculating To express the effect of the surface roughness of the filter drier on the flow resistance, The effect of porosity on flow resistance is expressed by To represent the effect of flow velocity non-uniformity in the filter drier on flow resistance.

[0053] Furthermore, the usage time of the filter drier affects the roughness of the filter element surface of the filter drier and the particle diameter of the filter medium, and the roughness of the filter element surface of the filter drier and the particle diameter of the filter medium have an impact on the pressure drop. Therefore, the filter element influence coefficient and the filter medium influence coefficient are generated by considering the usage time. Specifically, different usage times are preset to correspond to different filter element influence coefficients and filter medium influence coefficients. In order to more accurately represent the current state of the filter drier, the standard deviation and the mean of the historical flow rate are pre-stored. After the refrigerant flow rate is obtained, the standard deviation and the mean of the historical flow rate can be updated according to the refrigerant flow rate, and the real-time flow rate standard deviation and the real-time flow rate mean are generated. Finally, the dryer influence coefficient is generated. The dryer influence coefficient is used to describe the influence of the filter element surface roughness, porosity, shape and flow unevenness on the flow resistance when the refrigerant flows through the filter element. By updating the dryer influence coefficient, more accurate and more consistent with the current state of the pressure drop information is generated.

[0054] In one embodiment, in step S400, determining whether the pressure drop deviation trend factor is greater than or equal to a standard pressure drop trend threshold value further includes: Step S421: if the judgment is no, then generating a pressure drop deviation trend normal instruction; Step S422: Generate a current health assessment report according to the pressure drop deviation trend normal instruction, and store the current health assessment report.

[0055] In this embodiment, when the judgment is no, that is, it is judged that the pressure drop deviation trend factor is less than the standard pressure drop trend threshold, indicating that the greater the fluctuation state of the pressure drop of the dry filter during operation and the deviation between the pressure drop corresponding to the normal working state is normal, a pressure drop deviation trend normal instruction is generated, and a current health assessment report is generated according to the pressure drop deviation trend normal instruction, and the current health assessment report is stored. By generating multiple current health assessment reports for archiving and recording, it can also be used for subsequent overall use status analysis of the dry filter, thereby facilitating the diagnosis of dry filters in other refrigeration systems and providing auxiliary decision-making information.

[0056] In one embodiment, Figure 2 As shown, the present invention also provides a refrigeration facility health trend diagnosis system based on real-time equipment monitoring, the system comprising: A vibration health generation module, used for acquiring vibration data of a filtration process of a dry filter in a refrigeration system, and generating a filtration vibration health index according to the vibration data of the filtration process; A vibration abnormality judgment module, used for judging whether the drying filter has abnormal vibration according to the filter vibration health index; A pressure deviation generating module, for obtaining drying and filtering state data of a drying filter in the refrigeration system if the judgment is no, and generating a pressure drop deviation trend factor according to the drying and filtering state data; The health diagnosis generation module is used to determine whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold. If it is judged to be yes, a filter health diagnosis abnormality instruction is generated, and the refrigeration facility management personnel are instructed to perform maintenance according to the filter health diagnosis abnormality instruction.

[0057] In another embodiment, the filtering vibration health index includes a fault frequency deviation value and a vibration frequency fluctuation index; the vibration health generation module is also used to: extract data according to the preset sampling window according to the filtering process vibration data, and generate multiple vibration process window data; obtain the number of calibration points of the pre-calibrated frequency points in the standard fault frequency segment, and set the number of calibration points as the frequency sampling number; sample data from the vibration process window data according to the frequency sampling number, and generate multiple sampling frequency segments, wherein the number of sampling frequency points in the sampling frequency segment is the same as the number of pre-calibrated frequency points in the standard fault frequency segment; generate a fault frequency deviation value according to each sampling frequency point in the sampling frequency segment and each pre-calibrated frequency point in the standard fault frequency segment, wherein each sampling frequency segment corresponds to a fault frequency deviation value; based on the following formula, generate a vibration frequency fluctuation index according to the fault frequency deviation value corresponding to each sampling frequency segment in the vibration process window data: ; Wherein, VFC is the vibration frequency fluctuation index, N is the number of fault frequency deviation values ​​corresponding to the sampling frequency segment, DVF is the fault frequency deviation value, is the fault frequency deviation value corresponding to the jth sampling frequency segment, j is the serial number of the sampling frequency segment, is the mean value of the fault frequency deviation value corresponding to the sampling frequency segment.

[0058] In another embodiment, the vibration health generation module is further used to calculate the fault frequency deviation value DVF based on the following formula: ; Where DVF is the fault frequency deviation value, n is the number of calibration points, is the frequency value of the i-th sampling frequency point, is the frequency value of the i-th pre-calibrated frequency point.

[0059] In another embodiment, the vibration abnormality judgment module is also used to: judge whether the fault frequency deviation value is less than or equal to a preset first deviation threshold; if the judgment is yes, judge that the vibration of the drying filter is abnormal; if the judgment is no, judge whether the vibration frequency fluctuation index is greater than or equal to a preset second deviation threshold; if the judgment is yes, judge that the vibration of the drying filter is abnormal; if the judgment is no, judge that the drying filter is not abnormal in vibration.

[0060] In another embodiment, the vibration abnormality judgment module is also used to: if the judgment is yes, generate a vibration health abnormality instruction; send the vibration health abnormality instruction to the refrigeration facility management personnel, wherein the vibration health abnormality instruction is used to instruct the refrigeration facility management personnel to perform vibration abnormality maintenance on the drying filter.

[0061] In another embodiment, the pressure deviation generating module is further used to: extract refrigerant state data, filter state data and filter inlet and outlet pressure data according to the dry filter state data, wherein the filter inlet and outlet pressure data include the inlet pressure and outlet pressure of the dry filter; generate a refrigerant influence coefficient according to the refrigerant state data; generate a dryer influence coefficient according to the filter state data; and generate a pressure drop deviation trend factor based on the following formula: ; in, is the pressure drop deviation trend factor at the data sampling time point t, is the outlet pressure of the filter drier at the data sampling time point t, is the inlet pressure of the filter drier at the data sampling time point t, is the dynamic viscosity of the refrigerant at the data sampling time point t, L is the length of the filter filling layer of the dry filter, is the permeability coefficient of the filter drier, is the filtration cross-sectional area of ​​the filter drier, is the flow rate of the refrigerant at the data sampling time point t, is the refrigerant influence coefficient at the data sampling time point t, is the dryer influence coefficient at the data sampling time point t.

[0062] In another embodiment, the refrigerant state data includes refrigerant mass flow, refrigerant density, and refrigerant dynamic viscosity; the pressure deviation generating module is further used to: generate a refrigerant influence coefficient based on the following formula: ; in, is the refrigerant influence coefficient at the data sampling time point t, is the basic influence coefficient, is the refrigerant mass flow rate at the data sampling time point t, is the mass flow reference value, is the flow index, is the refrigerant density at the data sampling time point t, is the density reference value, is the density index, is the dynamic viscosity of the refrigerant at the data sampling time point t, is the reference value of dynamic viscosity, is the viscosity index.

[0063] In another embodiment, the refrigerant state data also includes a refrigerant flow rate; the pressure deviation generating module is further used to: obtain the use time of the drying filter according to the filter state data; generate a filter element influence coefficient and a filter medium influence coefficient according to the use time; update the standard deviation and the mean of the historical flow rate according to the refrigerant flow rate, and generate a real-time flow rate standard deviation and a real-time flow rate mean; generate a dryer influence coefficient based on the following formula: ; in, is the dryer influence coefficient, is the filter medium influence coefficient, is the filter element influence coefficient, is the roughness of the filter element surface in the dry filter, is the particle diameter of the filter medium in the dry filter, G is the filter shape influence coefficient of the filter, is the porosity of the filter element in the filter drier, is the real-time flow rate standard deviation, is the real-time flow rate average.

[0064] In another embodiment, the health diagnosis generation module is also used to: if the judgment is no, generate a pressure drop deviation trend normal instruction; generate a current health assessment report according to the pressure drop deviation trend normal instruction, and store the current health assessment report.

[0065] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned method for diagnosing health trends of refrigeration facilities based on real-time equipment monitoring are implemented.

[0066] In one embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for diagnosing health trends of refrigeration facilities based on real-time equipment monitoring are implemented.

[0067] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0068] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0069] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for diagnosing the health trend of refrigeration facilities based on real-time equipment monitoring, characterized in that: The method comprises: Acquire filtration process vibration data of a filter drying system in a refrigeration system, and generate a filtration vibration health index according to the filtration process vibration data; determining whether the filter drier vibrates abnormally according to the filter vibration health index; If the judgment is no, obtaining the drying and filtering state data of the drying filter in the refrigeration system, and generating the pressure drop deviation trend factor according to the drying and filtering state data; It is determined whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold. If so, a filter health diagnosis abnormality instruction is generated, and the refrigeration facility management personnel are instructed to perform maintenance according to the filter health diagnosis abnormality instruction.

2. The refrigeration facility health trend diagnosis method based on real-time equipment monitoring according to claim 1 is characterized in that: The filtering vibration health index includes a fault frequency deviation value and a vibration frequency fluctuation index; Generating a filtered vibration health index according to the filtered process vibration data includes: Extracting data according to the vibration data of the filtering process according to a preset sampling window, and generating a plurality of vibration process window data; Obtaining the number of calibration points of pre-calibrated frequency points in the standard fault frequency segment, and setting the number of calibration points as the number of frequency sampling; Sampling data from the vibration process window data according to the frequency sampling quantity, and generating a plurality of sampling frequency segments, wherein the number of sampling frequency points in the sampling frequency segment is the same as the number of pre-calibrated frequency points in the standard fault frequency segment; Generating a fault frequency deviation value according to each sampling frequency point in the sampling frequency segment and each pre-calibrated frequency point in the standard fault frequency segment, wherein each sampling frequency segment corresponds to a fault frequency deviation value; According to the fault frequency deviation value corresponding to each sampling frequency segment in the vibration process window data, a vibration frequency fluctuation index is generated based on the following formula: ； Wherein, VFC is the vibration frequency fluctuation index, N is the number of fault frequency deviation values ​​corresponding to the sampling frequency segment, DVF is the fault frequency deviation value, is the fault frequency deviation value corresponding to the jth sampling frequency segment, j is the serial number of the sampling frequency segment, is the mean value of the fault frequency deviation value corresponding to the sampling frequency segment.

3. The refrigeration facility health trend diagnosis method based on real-time equipment monitoring according to claim 2 is characterized in that: The fault frequency deviation value DVF is calculated based on the following formula: ； Where DVF is the fault frequency deviation value, n is the number of calibration points, is the frequency value of the i-th sampling frequency point, is the frequency value of the i-th pre-calibrated frequency point.

4. The refrigeration facility health trend diagnosis method based on real-time equipment monitoring according to claim 3 is characterized in that: Judging whether the filter drier has abnormal vibration according to the filter vibration health index includes: Determine whether the fault frequency deviation value is less than or equal to a preset first deviation threshold; If the answer is yes, it is determined that the filter drying device has abnormal vibration; If the judgment is no, determining whether the vibration frequency fluctuation index is greater than or equal to a preset second deviation threshold; If the answer is yes, it is determined that the filter drying device has abnormal vibration; If the determination result is negative, it is determined that the filter drier is not vibrating abnormally.

5. The refrigeration facility health trend diagnosis method based on real-time equipment monitoring according to any one of claims 1 to 4, characterized in that: Judging whether the filter drying machine has abnormal vibration according to the filter vibration health index, and then further comprising: If the judgment is yes, a vibration health abnormality instruction is generated; The vibration health abnormality instruction is sent to a refrigeration facility manager, wherein the vibration health abnormality instruction is used to instruct the refrigeration facility manager to perform vibration abnormality maintenance on the drying filter.

6. The refrigeration facility health trend diagnosis method based on real-time equipment monitoring according to claim 1 is characterized in that: Generating a pressure drop deviation trend factor according to the dry filtration state data includes: Extracting refrigerant status data, filter status data and filter inlet and outlet pressure data according to the dry filter status data, wherein the filter inlet and outlet pressure data include the inlet pressure and outlet pressure of the dry filter; generating a refrigerant influence coefficient according to the refrigerant state data; generating a dryer influence coefficient according to the filter status data; The pressure drop deviation trend factor is generated based on the following formula: ； in, is the pressure drop deviation trend factor at the data sampling time point t, is the outlet pressure of the filter drier at the data sampling time point t, is the inlet pressure of the filter drier at the data sampling time point t, is the dynamic viscosity of the refrigerant at the data sampling time point t, L is the length of the filter filling layer of the dry filter, is the permeability coefficient of the filter drier, is the filtration cross-sectional area of ​​the filter drier, is the flow rate of the refrigerant at the data sampling time point t, is the refrigerant influence coefficient at the data sampling time point t, is the dryer influence coefficient at the data sampling time point t.

7. The refrigeration facility health trend diagnosis method based on real-time equipment monitoring according to claim 6 is characterized in that: The refrigerant status data includes refrigerant mass flow, refrigerant density, and refrigerant dynamic viscosity; The refrigerant influence factor is generated based on the following formula: ； in, is the refrigerant influence coefficient at the data sampling time point t, is the basic influence coefficient, is the refrigerant mass flow rate at the data sampling time point t, is the mass flow reference value, is the flow index, is the refrigerant density at the data sampling time point t, is the density reference value, is the density index, is the dynamic viscosity of the refrigerant at the data sampling time point t, is the reference value of dynamic viscosity, is the viscosity index.

8. The refrigeration facility health trend diagnosis method based on real-time equipment monitoring according to claim 7, characterized in that: The refrigerant status data also includes refrigerant flow rate; Generating a dryer influence coefficient according to the filter status data includes: Acquire the usage time of the drying filter according to the filter status data; Generate filter element influence coefficient and filter medium influence coefficient according to the used time; updating the standard deviation of the historical flow rate and the mean of the historical flow rate according to the refrigerant flow rate, and generating the standard deviation of the real-time flow rate and the mean of the real-time flow rate; The dryer influence factor is generated based on the following formula: ； in, is the dryer influence coefficient, is the filter medium influence coefficient, is the filter element influence coefficient, is the roughness of the filter element surface in the dry filter, is the particle diameter of the filter medium in the dry filter, G is the filter shape influence coefficient of the filter, is the porosity of the filter element in the filter drier, is the real-time flow rate standard deviation, is the real-time flow rate average.

9. The refrigeration facility health trend diagnosis method based on real-time equipment monitoring according to claim 8, characterized in that: Determining whether the pressure drop deviation trend factor is greater than or equal to a standard pressure drop trend threshold value also includes: If the judgment is no, a normal pressure drop deviation trend instruction is generated; A current health assessment report is generated according to the pressure drop deviation trend normal instruction, and the current health assessment report is stored.

10. A refrigeration facility health trend diagnosis system based on real-time equipment monitoring, characterized in that: The system comprises: A vibration health generation module, used for acquiring vibration data of a filtration process of a dry filter in a refrigeration system, and generating a filtration vibration health index according to the vibration data of the filtration process; A vibration abnormality judgment module, used for judging whether the drying filter has abnormal vibration according to the filter vibration health index; A pressure deviation generating module, for obtaining drying and filtering state data of a drying filter in the refrigeration system if the judgment is no, and generating a pressure drop deviation trend factor according to the drying and filtering state data; The health diagnosis generation module is used to determine whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold. If it is judged to be yes, a filter health diagnosis abnormality instruction is generated, and the refrigeration facility management personnel are instructed to perform maintenance according to the filter health diagnosis abnormality instruction.

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