Refrigeration Facility Health Trend Diagnosis Method and System Based on Real-Time Device Monitoring

Abstract

This application relates to the technical field of refrigeration facility operation monitoring, and specifically relates to a method and system for diagnosing the health trend of refrigeration facilities based on real-time equipment monitoring. The method includes obtaining the filtering process vibration data of the dryer filter in the refrigeration system and generating a filtering vibration health index; judging whether the dryer filter vibrates abnormally according to the filtering vibration health index; if the judgment is negative, obtaining the drying and filtering state data of the dryer filter in the refrigeration system and generating a pressure drop deviation trend factor according to the drying and filtering state data; judging whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold, if the judgment is positive, generating a filter health diagnosis abnormal instruction, and instructing the refrigeration facility management personnel to perform maintenance according to the filter health diagnosis abnormal instruction. This application realizes rapid health diagnosis based on vibration monitoring and comprehensively considers the pressure drop factor to achieve multi-dimensional judgment of the health status of refrigeration system facilities.

Description

Technical Field

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

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

[0003] 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 each component of the system, especially the dryer filter. In the prior art, the dryer filter is generally inspected and replaced regularly. However, the health status of the filter may not immediately show problems, and manual monitoring is not suitable for high-risk, long-running refrigeration systems.

[0004] In addition, there are also solutions in the prior art that detect by setting sensors, but most of them have the problem of low data analysis accuracy, so that small fault changes will be ignored, resulting in premature or late maintenance and health warnings, thus causing the problem of low efficiency in monitoring the health trend 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 diagnosing the health trend of refrigeration facilities based on real-time equipment monitoring, which can solve the problem of low efficiency in monitoring the health trend of the refrigeration system caused by inaccurate and untimely health diagnosis, realize rapid health diagnosis based on vibration monitoring, and then achieve multi-dimensional judgment of the health status of the refrigeration system facilities by integrating the pressure drop factor.

[0006] The technical solution of the present invention is as follows:

[0007] A method for diagnosing the health trend of refrigeration facilities based on real-time equipment monitoring, the method comprising:

[0008] Obtaining the vibration data of the filtration process of the dryer filter in the refrigeration system, and generating a filtration vibration health index according to the filtration process vibration data;

[0009] Judging whether the dryer filter vibrates abnormally according to the filtration vibration health index;

[0010] If the judgment is negative, obtaining the drying and filtering state data of the dryer filter in the refrigeration system, and generating a pressure drop deviation trend factor according to the drying and filtering state data;

[0011] Determine whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold. If the determination is yes, generate a filter health diagnosis abnormal instruction, and according to the filter health diagnosis abnormal instruction, instruct the refrigeration facility management personnel to perform maintenance.

[0012] Optionally, the filter vibration health index includes a fault frequency deviation value and a vibration frequency fluctuation index;

[0013] Generating a filter vibration health index based on the filter process vibration data includes:

[0014] Extract data from the filter process vibration data according to a preset sampling window, and generate a plurality of vibration process window data;

[0015] 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;

[0016] Perform data sampling from the vibration process window data according to the frequency sampling number, and generate 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;

[0017] 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;

[0018] Generate a vibration frequency fluctuation index based on the fault frequency deviation values corresponding to the sampling frequency segments in the vibration process window data according to the following formula:

[0019] ;

[0020] where VFC is the vibration frequency fluctuation index, N is the number of fault frequency deviation values corresponding to the sampling frequency segments, 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 values corresponding to the sampling frequency segments.

[0021] Optionally, calculate the fault frequency deviation value DVF according to the following formula:

[0022] ;

[0023] where DVF is the fault frequency deviation value, n is the number of calibration points, is the frequency value of the ith sampling frequency point, is the frequency value of the ith pre-calibrated frequency point.

[0024] Optionally, determining whether the drying filter vibrates abnormally according to the filtered vibration health index includes:

[0025] Determining whether the fault frequency deviation value is less than or equal to a preset first deviation threshold;

[0026] If the determination result is yes, determining that the drying filter vibrates abnormally;

[0027] If the determination result is no, determining whether the vibration frequency fluctuation index is greater than or equal to a preset second deviation threshold;

[0028] If the determination result is yes, determining that the drying filter vibrates abnormally;

[0029] If the determination result is no, determining that the drying filter does not vibrate abnormally.

[0030] Optionally, after determining whether the drying filter vibrates abnormally according to the filtered vibration health index, it further includes:

[0031] If the determination result is yes, generating a vibration health abnormality instruction;

[0032] Sending the vibration health abnormality instruction to the refrigeration facility management personnel, where the vibration health abnormality instruction is used to instruct the refrigeration facility management personnel to perform vibration abnormality maintenance on the drying filter.

[0033] Optionally, generating a pressure drop deviation trend factor according to the drying filter status data includes:

[0034] Extracting refrigerant status data, filter status data, and filter inlet and outlet pressure data according to the drying filter status data, where the filter inlet and outlet pressure data includes the inlet pressure and outlet pressure of the drying filter;

[0035] Generating a refrigerant influence coefficient according to the refrigerant status data;

[0036] Generating a dryer influence coefficient according to the filter status data;

[0037] Generating a pressure drop deviation trend factor based on the following formula:

[0038] ;

[0039] Wherein, is the pressure drop deviation trend factor at the data sampling time point t, is the outlet pressure of the drying filter at the data sampling time point t, is the inlet pressure of the drying filter 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 dryer filter, is the permeability coefficient of the dryer filter, is the cross-sectional area of the filter of the dryer filter, 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.

[0040] Optionally, 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:

[0041] ;

[0042] where, 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 rate reference value, is the flow rate index, is the refrigerant density at the data sampling time point t, is the density reference value, is the density index, is the refrigerant dynamic viscosity at the data sampling time point t, is the dynamic viscosity reference value, is the viscosity index.

[0043] Optionally, the refrigerant state data further includes refrigerant flow rate;

[0044] Generating the dryer influence coefficient according to the filter state data includes:

[0045] Obtaining the used time of the dryer filter according to the filter state data;

[0046] Generating the filter element influence coefficient and the filter medium influence coefficient according to the used time;

[0047] Updating the standard deviation and the mean value of the historical flow rate according to the refrigerant flow rate, and generating the real-time flow rate standard deviation and the real-time flow rate mean value;

[0048] Generating the dryer influence coefficient based on the following formula:

[0049] ;

[0050] where, is the dryer influence coefficient, is the filter medium influence coefficient, is the filter element influence coefficient, is the surface roughness of the filter element in the dryer filter, is the particle diameter of the filter medium in the dryer filter, and G is the filter shape influence coefficient of the filter, is the porosity of the filter element in the dryer filter, is the standard deviation of the real-time flow rate, is the mean value of the real-time flow rate.

[0051] Optionally, determining whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold further includes:

[0052] If the judgment is negative, a pressure drop deviation trend normal instruction is generated;

[0053] Generate the current health assessment report according to the pressure drop deviation trend normal instruction and store the current health assessment report.

[0054] Optionally, the present application further provides a refrigeration facility health trend diagnosis system based on real-time device monitoring, and the system includes:

[0055] A vibration health generation module, configured to obtain the vibration data of the filtration process of the dryer filter in the refrigeration system and generate a filtration vibration health index according to the filtration process vibration data;

[0056] A vibration abnormality judgment module, configured to judge whether the dryer filter vibrates abnormally according to the filtration vibration health index;

[0057] A pressure deviation generation module, configured to, if the judgment is negative, obtain the drying and filtering state data of the dryer filter in the refrigeration system and generate a pressure drop deviation trend factor according to the drying and filtering state data;

[0058] A health diagnosis generation module, configured to judge whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold. If the judgment is positive, 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.

[0059] Optionally, the filtered vibration health index includes a fault frequency deviation value and a vibration frequency fluctuation index; the vibration health generation module is further configured to: extract data from the filtered process vibration data according to a preset sampling window, and generate a plurality of vibration process window data; obtain the number of calibration points of the pre-calibrated frequency points in the standard fault frequency band, and set the number of calibration points as the frequency sampling number; perform data sampling from the vibration process window data according to the frequency sampling number, and generate a plurality of sampling frequency bands, wherein the number of sampling frequency points in the sampling frequency band is the same as the number of pre-calibrated frequency points in the standard fault frequency band; generate a fault frequency deviation value according to each sampling frequency point in the sampling frequency band and each pre-calibrated frequency point in the standard fault frequency band, wherein each sampling frequency band corresponds to a fault frequency deviation value; based on the following formula, generate a vibration frequency fluctuation index according to the fault frequency deviation values corresponding to the sampling frequency bands in the vibration process window data:

[0060] ;

[0061] wherein, VFC is the vibration frequency fluctuation index, N is the number of fault frequency deviation values corresponding to the sampling frequency band, DVF is the fault frequency deviation value, is the fault frequency deviation value corresponding to the jth sampling frequency band, and j is the serial number of the sampling frequency band, is the mean value of the fault frequency deviation values corresponding to the sampling frequency band.

[0062] Optionally, the vibration health generation module is further configured to calculate the fault frequency deviation value DVF based on the following formula: ;

[0063] wherein, DVF is the fault frequency deviation value, n is the number of calibration points, is the frequency value of the ith sampling frequency point, is the frequency value of the ith pre-calibrated frequency point.

[0064] Optionally, the vibration anomaly judgment module is further configured to: judge whether the fault frequency deviation value is less than or equal to a preset first deviation threshold; if the judgment result is yes, judge that the dry filter vibrates abnormally; if the judgment result is no, judge whether the vibration frequency fluctuation index is greater than or equal to a preset second deviation threshold; if the judgment result is yes, judge that the dry filter vibrates abnormally; if the judgment result is no, judge that the dry filter does not vibrate abnormally.

[0065] Optionally, the vibration anomaly determination module is further configured to: if the determination result is positive, generate a vibration health anomaly instruction; and send the vibration health anomaly instruction to the refrigeration facility management personnel, where the vibration health anomaly instruction is used to instruct the refrigeration facility management personnel to perform vibration anomaly maintenance on the drying filter.

[0066] Optionally, the pressure deviation generation module is further configured to: extract refrigerant state data, filter state data, and filter inlet and outlet pressure data according to the drying filter state data, where the filter inlet and outlet pressure data includes the inlet pressure and outlet pressure of the drying 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:

[0067] ;

[0068] where is the pressure drop deviation trend factor at the data sampling time point t, is the outlet pressure of the drying filter at the data sampling time point t, is the inlet pressure of the drying filter at the data sampling time point t, is the refrigerant dynamic viscosity at the data sampling time point t, L is the length of the filter filling layer of the drying filter, is the permeability coefficient of the drying filter, is the filter cross-sectional area of the drying filter, is the refrigerant flow rate 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.

[0069] Optionally, the refrigerant state data includes refrigerant mass flow rate, refrigerant density, and refrigerant dynamic viscosity; the pressure deviation generation module is further configured to: generate a refrigerant influence coefficient based on the following formula: ;

[0070] where 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 rate reference value, is the flow rate 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 the dynamic viscosity, is the viscosity index.

[0071] Optionally, the refrigerant state data further includes the refrigerant flow rate; the pressure deviation generation module is further configured to: obtain the used 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 used time; update the standard deviation of the historical flow rate and the historical flow rate average value according to the refrigerant flow rate, and generate a real-time flow rate standard deviation and a real-time flow rate average value; generate a dryer influence coefficient based on the following formula:

[0072] ;

[0073] where, is the dryer influence coefficient, is the filter medium influence coefficient, is the filter element influence coefficient, is the roughness of the surface of the filter element in the drying filter, is the particle diameter of the filter medium in the drying filter, G is the filter shape influence coefficient of the filter, is the porosity of the filter element in the drying filter, is the real-time flow rate standard deviation, is the real-time flow rate average value.

[0074] Optionally, the health diagnosis generation module is further configured to: if the judgment is negative, generate a normal pressure drop deviation trend instruction; generate a current health assessment report according to the normal pressure drop deviation trend instruction, and store the current health assessment report.

[0075] Optionally, a computer device is further provided, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned refrigeration facility health trend diagnosis method based on real-time device monitoring are implemented.

[0076] Optionally, a computer-readable storage medium is further 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 device monitoring are implemented.

[0077] The technical effects achieved by the present invention are as follows:

[0078] The above-mentioned refrigeration facility health trend diagnosis method and system based on real-time device monitoring obtain the vibration data of the filtration process of the dryer filter in the refrigeration system, and generate a filtration vibration health index according to the filtration process vibration data; judge whether the dryer filter vibrates abnormally according to the filtration vibration health index; if the judgment is negative, obtain the drying and filtration state data of the dryer filter in the refrigeration system, and generate a pressure drop deviation trend factor according to the drying and filtration state data; judge whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold, and if the judgment is positive, generate a filter health diagnosis abnormal instruction, and instruct the refrigeration facility management personnel to perform maintenance according to the filter health diagnosis abnormal instruction. In this application, in order to quickly detect the refrigeration facilities in the refrigeration system, the dryer filter between the condenser and the expansion valve is selected as the core of device monitoring. In order to quickly perform health judgment, vibration detection is first performed on the dryer filter. Specifically, the vibration data of the filtration process of the dryer 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 represent the influence degree of the vibration of the dryer filter during the filtration process on its working state. Furthermore, it is possible to judge whether the dryer filter vibrates abnormally through the filtration vibration health index. When judging whether the dryer filter vibrates abnormally, even if vibration maintenance is performed, thus realizing rapid health diagnosis based on vibration monitoring, meeting the requirements of rapid diagnosis in health trend diagnosis. If it is judged that there is no abnormality in vibration, further state monitoring and health diagnosis of other dimensions are performed on the dryer filter to realize a health diagnosis method of vibration first and then multi-dimensional monitoring. Specifically, it is considered from the dimension of pressure drop, including obtaining the drying and filtration state data of the dryer filter in the refrigeration system, and generating a pressure drop deviation trend factor according to the drying and filtration state data. Based on the principle that when the dryer filter works normally, its pressure drop should be maintained within a certain range, the pressure drop deviation trend factor is generated to represent the fluctuation state of the pressure drop of the dryer filter during operation and the deviation between the pressure drop corresponding to the normal working state. The larger the pressure drop deviation trend factor, the greater the fluctuation state of the pressure drop of the dryer filter during operation and the greater the deviation between the pressure drop corresponding to the normal working state, and the worse the health trend. Therefore, judge whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold. If the judgment is positive, generate a filter health diagnosis abnormal instruction, and instruct the refrigeration facility management personnel to perform maintenance according to the filter health diagnosis abnormal instruction. Description of the Drawings

[0079] Figure 1 It is a schematic flow chart of a refrigeration facility health trend diagnosis method based on real-time device monitoring in an embodiment;

[0080] Figure 2It is a structural block diagram of a refrigeration facility health trend diagnosis system based on real-time device monitoring in an embodiment. Detailed implementation manners

[0081] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to 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.

[0082] In one embodiment, as Figure 1 shown, a refrigeration facility health trend diagnosis method based on real-time device monitoring is provided. The method includes:

[0083] Step S100: Obtain the filtration process vibration data of the dryer filter in the refrigeration system, and generate a filtration vibration health index according to the filtration process vibration data;

[0084] Step S200: Judge whether the dryer filter vibrates abnormally according to the filtration vibration health index;

[0085] Step S300: If the judgment result is negative, obtain the drying and filtering state data of the dryer filter in the refrigeration system, and generate a pressure drop deviation trend factor according to the drying and filtering state data;

[0086] Step S400: Judge whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold. If the judgment result is positive, generate a filter health diagnosis abnormal instruction, and instruct the refrigeration facility management personnel to perform maintenance according to the filter health diagnosis abnormal instruction.

[0087] In this embodiment, in order to quickly detect the refrigeration facilities in the refrigeration system, the drier filter between the condenser and the expansion valve is selected as the core of equipment monitoring. In order to quickly perform a health judgment, vibration detection is first performed on the drier filter. Specifically, vibration data during the filtering process of the drier filter in the refrigeration system is obtained, and a filtering vibration health index is generated based on the vibration data during the filtering process. The filtering vibration health index is used to represent the degree of influence of the vibration of the drier filter during the filtering process on its working state. Furthermore, it is possible to determine whether the drier filter vibrates abnormally through the filtering vibration health index. When determining whether the drier filter vibrates abnormally, even if vibration maintenance is performed, this realizes rapid health diagnosis based on vibration monitoring and meets the requirements of rapid diagnosis in health trend diagnosis. If it is determined that there is no abnormal vibration, further state monitoring and health diagnosis of other dimensions are performed on the drier filter to implement a health diagnosis method of first vibration and then multi-dimensional monitoring. Specifically, considering from the dimension of pressure drop, it includes obtaining the dry filtering state data of the drier filter in the refrigeration system and generating a pressure drop deviation trend factor based on the dry filtering state data. Based on the principle that when the drier filter is working properly, its pressure drop should be maintained within a certain range, the pressure drop deviation trend factor is generated to represent the fluctuation state of the pressure drop of the drier filter during operation and the deviation between the pressure drop corresponding to the normal working state. The larger the pressure drop deviation trend factor, the greater the fluctuation state of the pressure drop of the drier filter during operation and the greater the deviation between the pressure drop corresponding to the normal working state, and the worse the health trend. Therefore, it is determined whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold. If the determination is yes, a filter health diagnosis abnormal instruction is generated, and based on the filter health diagnosis abnormal instruction, the refrigeration facility management personnel are instructed to perform maintenance, thereby realizing efficient monitoring and highly accurate monitoring.

[0088] In one embodiment, the filtering vibration health index includes a fault frequency deviation value and a vibration frequency fluctuation index;

[0089] In step S100, generating the filtering vibration health index based on the vibration data during the filtering process includes:

[0090] Step S110: Extract data according to the vibration data during the filtering process according to a preset sampling window, and generate a plurality of vibration process window data;

[0091] In this step, in order to perform refined analysis on the data, data extraction is performed on the vibration data of the filtering process according to a preset sampling window, and multiple vibration process window data are generated. Among them, the preset sampling window includes two cases. The first case is that adjacent preset sampling windows do not overlap, and the second case is that adjacent preset sampling windows partially overlap. When adjacent preset sampling windows partially overlap, the refinement degree of vibration data analysis is higher. For example, taking the vibration data of the filtering process as a vibration sequence, specifically {f1, f2, f3, f4, f5, f6, f7, f8, f9}, and 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, which are {f1, f2, f3}, {f4, f5, f6}, and {f7, f8, f9} respectively. When adjacent preset sampling windows partially overlap, one of the cases 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 number of the vibration process window data is more, the vibration analysis is more refined and comprehensive, and the obtained vibration anomaly conclusion is more accurate.

[0092] Step S120: Obtain the number of calibration points of the pre-calibrated frequency points in the standard fault frequency band, and set the number of calibration points as the frequency sampling quantity;

[0093] Step S130: Perform data sampling from the vibration process window data according to the frequency sampling quantity, and generate multiple sampling frequency bands, where the number of sampling frequency points in the sampling frequency band is the same as the number of pre-calibrated frequency points in the standard fault frequency band;

[0094] In steps S120 - S130, the standard fault frequency band is pre-stored, pre-calibrated frequency points are set in the standard fault frequency band, and the number of pre-calibrated frequency points is also preset in advance. In this way, it is possible to perform more refined frequency band splitting on the vibration process window data to be analyzed currently based on the number of calibration points of the pre-calibrated frequency points in the standard fault frequency band. Specifically, obtain the number of calibration points of the pre-calibrated frequency points in the standard fault frequency band, set the number of calibration points as the frequency sampling quantity, perform data sampling from the vibration process window data according to the frequency sampling quantity, and generate multiple sampling frequency bands. By presetting the frequency sampling quantity, the number of sampling frequency points in the sampling frequency band is the same as the number of pre-calibrated frequency points in the standard fault frequency band, which facilitates subsequent similarity calculation between the actual frequency band and the standard fault frequency band, and improves the vibration analysis efficiency.

[0095] 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;

[0096] 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:

[0097] ;

[0098] 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.

[0099] 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.

[0100] In one embodiment, in step S140, the fault frequency deviation value DVF is calculated based on the following formula:

[0101] ;

[0102] Wherein, 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-calibration frequency point.

[0103] In this embodiment, by calculating to represent the difference between the specific frequency values in the sampling frequency band and the standard fault frequency band, and then calculating to represent the degree to which the sampling frequency band approaches the standard fault frequency band. The smaller the fault frequency deviation value DVF, the more similar the sampling frequency band is to the standard fault frequency band, and the greater the probability of a fault. Therefore, by calculating the difference between the actual frequency band and the pre-stored frequency band where a fault occurs, the probability that the current drying filter may have abnormal vibration is measured. Through the comparison of frequency bands, a more comprehensive comparison is achieved, improving the accuracy of the vibration comparison result.

[0104] In one embodiment, step S200: judging whether the drying filter vibrates abnormally according to the filter vibration health index, includes:

[0105] Step S210: judging whether the fault frequency deviation value is less than or equal to a preset first deviation threshold;

[0106] Step S220: if the judgment is yes, then judge that the drying filter vibrates abnormally;

[0107] Step S230: if the judgment is no, then judge whether the vibration frequency fluctuation index is greater than or equal to a preset second deviation threshold;

[0108] Step S240: if the judgment is yes, then judge that the drying filter vibrates abnormally;

[0109] Step S250: if the judgment is no, then judge that the drying filter does not vibrate abnormally.

[0110] In this embodiment, by comprehensively considering the fault frequency deviation value and the vibration frequency fluctuation index to comprehensively judge whether the drying filter vibrates abnormally. First, judge whether the fault frequency deviation value is less than or equal to a preset first deviation threshold. If the judgment is yes, it means that the sampling frequency band is more similar to the standard fault frequency band, so the probability of a fault is greater, so it is judged that the drying filter vibrates abnormally. If the judgment is 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. When the judgment is yes, it is judged that the fluctuation is too large at this time, so it is judged that the drying filter vibrates abnormally, otherwise it is judged that the drying filter does not vibrate abnormally.

[0111] In one embodiment, in step S200: determining whether the drying filter vibrates abnormally according to the filtered vibration health index, and then further comprising:

[0112] Step S281: If the determination is yes, generate a vibration health abnormality instruction;

[0113] Step S282: Send the vibration health abnormality instruction to the refrigeration facility management personnel, where the vibration health abnormality instruction is used to instruct the refrigeration facility management personnel to perform vibration abnormality maintenance on the drying filter.

[0114] In this embodiment, when the determination is yes, that is, it is determined that the drying filter vibrates abnormally. At this time, immediate maintenance is required. Specifically, a vibration health abnormality instruction is generated, and based on the generated vibration health abnormality instruction, the refrigeration facility management personnel are instructed to perform vibration abnormality maintenance on the drying filter.

[0115] Specifically, the vibration abnormality maintenance includes checking whether the pipe support is loose, checking whether there is excessive vibration transmission, checking whether the pipe interface of the filter is tightened, and checking whether the pipe interface is loose or has poor sealing, etc.

[0116] In one embodiment, in step S300, generating a pressure drop deviation trend factor according to the drying filter state data includes:

[0117] Step S310: Extract refrigerant state data, filter state data, and filter inlet and outlet pressure data according to the drying filter state data, where the filter inlet and outlet pressure data includes the inlet pressure and outlet pressure of the drying filter;

[0118] Step S320: Generate a refrigerant influence coefficient according to the refrigerant state data;

[0119] Step S330: Generate a dryer influence coefficient according to the filter state data;

[0120] Step S340: Generate a pressure drop deviation trend factor based on the following formula:

[0121] ;

[0122] where, is the pressure drop deviation trend factor at the data sampling time point t, is the outlet pressure of the drying filter at the data sampling time point t, is the inlet pressure of the drying filter at the data sampling time point t, is the refrigerant dynamic viscosity at the data sampling time point t, L is the length of the filter filling layer of the drying filter, is the permeability coefficient of the drying filter, is the filtration cross-sectional area of the drying filter, 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.

[0123] In this embodiment, the permeability coefficient of the drying filter reflects the permeation ability of the drying filter and is preset according to the drying and filtering structure in the drying filter.

[0124] Specifically, when calculating the pressure drop deviation trend factor, first calculate represents the difference between the outlet pressure and the inlet pressure of the drying filter and is used to represent the actually measured pressure drop. Then calculate represents the theoretical pressure drop of the refrigerant in the porous medium of the drying filter. Finally, calculate the difference between the two and perform further calculations. 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 can be used more accurately and comprehensively to evaluate the health state of the current drying refrigerator.

[0125] 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:

[0126] ;

[0127] Wherein, 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 rate reference value, is the flow rate index, is the refrigerant density at the data sampling time point t, is the density reference value, is the density index, is the refrigerant dynamic viscosity at the data sampling time point t, is the dynamic viscosity reference value, is the viscosity index.

[0128] In this embodiment, the refrigerant influence coefficient is used to represent at the data sampling time point t, is used to represent the benchmark influence degree of factors such as flow rate, density, and viscosity related to the refrigerant on the pressure drop deviation. The flow rate index density index and the viscosity index are respectively used to adjust the sensitivity of the refrigerant influence coefficient to the flow rate change, density, and dynamic viscosity. The flow rate index , the density index , and the viscosity index are all preset. The reference value of the mass flow rate , the reference value of the density , and the reference value of the dynamic viscosity are all preset, representing the values of the mass flow rate, refrigerant density, and refrigerant dynamic viscosity under standard working conditions.

[0129] Specifically, by calculating represents the mass flow rate ratio, indicating the degree of change of the current flow rate relative to the reference flow rate. By calculating represents the density ratio. By calculating represents the dynamic viscosity ratio, indicating the degree of change of the current refrigerant dynamic viscosity relative to the reference viscosity. Then, in combination with the flow rate index , the density index , and the viscosity index to adjust the sensitivity of the flow rate change, density, and dynamic viscosity to the refrigerant influence coefficient. Finally, in combination with the basic influence coefficient to generate the refrigerant influence coefficient corresponding to the current sampling time point t, so as to realize the adjustment in combination with the actual state of the current refrigerant to improve the accuracy and sensitivity of the generated pressure drop deviation trend factor.

[0130] In this embodiment, the refrigerant density is obtained by referring to the physical property table of the refrigerant. For the refrigerant dynamic viscosity, first obtain the density of the refrigerant, and then look up the corresponding dynamic viscosity value at the current temperature and current pressure in the physical property table. The refrigerant mass flow rate is calculated by the following formula:

[0131] ;

[0132] where is the refrigerant mass flow rate at the data sampling time point t, is the flow coefficient of the drying filter, is the filtering cross-sectional area of the drying filter, is the outlet pressure of the drying filter at the data sampling time point t, is the inlet pressure of the drying filter at the data sampling time point t, is the refrigerant density at the data sampling time point t.

[0133] Among them, the flow coefficient of the drying filter is associated with the fluid characteristics of the drying filter and the refrigerant and is preset. The filtering cross-sectional area of the drying filter It is a fixed value set based on the dryer filter.

[0134] In one embodiment, the refrigerant state data further includes the refrigerant flow rate;

[0135] In step S330, generating a dryer influence coefficient according to the filter state data includes:

[0136] Step S331: Obtain the used time of the dryer filter according to the filter state data;

[0137] Step S332: Generate a filter element influence coefficient and a filter medium influence coefficient according to the used time;

[0138] Step S333: Update the standard deviation of the historical flow rate and the historical flow rate average according to the refrigerant flow rate, and generate a real-time flow rate standard deviation and a real-time flow rate average;

[0139] Step S334: Generate a dryer influence coefficient based on the following formula:

[0140] ;

[0141] Wherein, is the dryer influence coefficient, is the filter medium influence coefficient, is the filter element influence coefficient, is the roughness of the surface of the filter element in the dryer filter, is the particle diameter of the filter medium in the dryer filter, G is the filter shape influence coefficient of the filter, is the porosity of the filter element in the dryer filter, is the real-time flow rate standard deviation, is the real-time flow rate average.

[0142] In this embodiment, the filter element influence coefficient and the filter medium influence coefficient respectively represent the influence degree of the used time on the roughness of the surface of the filter element and the particle diameter of the filter medium of the dryer filter. The porosity of the filter element in the dryer filter, the roughness of the surface of the filter element in the dryer filter, and the particle diameter of the filter medium in the dryer filter are all preset based on the basic situation of the dryer filter. G is a preset value used to represent the influence of the geometric shape of the dryer filter on the flow.

[0143] Specifically, by calculating to represent the influence of the surface roughness of the dryer filter on the flow resistance, represents the influence of the porosity on the flow resistance, and by to represent the influence of the flow velocity non-uniformity in the dryer filter on the flow resistance.

[0144] Furthermore, the used time of the dryer filter affects the roughness of the filter element surface and the particle diameter of the filter medium of the dryer filter, and the roughness of the filter element surface and the particle diameter of the filter medium of the dryer filter in turn affect the pressure drop. Therefore, the filter element influence coefficient and the filter medium influence coefficient are generated by considering the used time. Specifically, different used 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 dryer filter, the standard deviation and the mean value of the historical flow rate are pre-stored. After the refrigerant flow rate is obtained, the standard deviation and the mean value 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 value are generated. Finally, the dryer influence coefficient is generated. The dryer influence coefficient is a coefficient used to describe the influence of the surface roughness, porosity, shape, and flow non-uniformity of the filter element on the flow resistance when the refrigerant flows through the filter element. By updating the dryer influence coefficient, more accurate and more current-state-conforming pressure drop information can be generated.

[0145] In one embodiment, in step S400, when judging whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold, it further includes:

[0146] Step S421: If the judgment is negative, a pressure drop deviation trend normal instruction is generated;

[0147] Step S422: A current health assessment report is generated according to the pressure drop deviation trend normal instruction, and the current health assessment report is stored.

[0148] In this embodiment, when the judgment is negative, that is, when it is judged that the pressure drop deviation trend factor is less than the standard pressure drop trend threshold, it indicates that the fluctuation state of the pressure drop during the operation of the dryer filter is greater and the deviation from the pressure drop corresponding to the normal operation state is normal. Therefore, a pressure drop deviation trend normal 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. By generating multiple current health assessment reports for archival records, it can also be used for subsequent analysis of the overall usage state of the dryer filter, which is beneficial for providing auxiliary decision-making information for the diagnosis of dryer filters in other refrigeration systems.

[0149] In one embodiment, as Figure 2 shown, the present invention further provides a refrigeration facility health trend diagnosis system based on real-time device monitoring. The system includes:

[0150] A vibration health generation module, configured to obtain the filtering process vibration data of the dryer filter in the refrigeration system and generate a filtering vibration health index according to the filtering process vibration data;

[0151] An abnormal vibration judgment module, configured to judge whether the drying filter vibrates abnormally according to the filtered vibration health index;

[0152] A pressure deviation generation module, configured to, if the judgment result is negative, obtain the drying filtration state data of the drying filter in the refrigeration system, and generate a pressure drop deviation trend factor according to the drying filtration state data;

[0153] A health diagnosis generation module, configured to judge whether the pressure drop deviation trend factor is greater than or equal to a standard pressure drop trend threshold. If the judgment result is positive, generate a filter health diagnosis abnormal instruction, and instruct the refrigeration facility management personnel to perform maintenance according to the filter health diagnosis abnormal instruction.

[0154] In another embodiment, the filtered vibration health index includes a fault frequency deviation value and a vibration frequency fluctuation index; the vibration health generation module is further configured to: extract data from the filtered process vibration data according to a preset sampling window, and generate a plurality of vibration process window data; obtain the number of calibration points of the pre-calibrated frequency points in the standard fault frequency band, and set the number of calibration points as the frequency sampling number; perform data sampling from the vibration process window data according to the frequency sampling number, and generate a plurality of sampling frequency bands, wherein the number of sampling frequency points in the sampling frequency band is the same as the number of pre-calibrated frequency points in the standard fault frequency band; generate a fault frequency deviation value according to each sampling frequency point in the sampling frequency band and each pre-calibrated frequency point in the standard fault frequency band, wherein each sampling frequency band corresponds to a fault frequency deviation value; based on the following formula, generate a vibration frequency fluctuation index according to the fault frequency deviation values corresponding to the sampling frequency bands in the vibration process window data:

[0155] ;

[0156] wherein, VFC is the vibration frequency fluctuation index, N is the number of fault frequency deviation values corresponding to the sampling frequency band, DVF is the fault frequency deviation value, is the fault frequency deviation value corresponding to the j-th sampling frequency band, j is the serial number of the sampling frequency band, is the mean value of the fault frequency deviation values corresponding to the sampling frequency band.

[0157] In another embodiment, the vibration health generation module is further configured to calculate the fault frequency deviation value DVF based on the following formula: ;

[0158] wherein, 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.

[0159] In another embodiment, the vibration anomaly determination module is further configured to: determine whether the fault frequency deviation value is less than or equal to a preset first deviation threshold; if the determination result is yes, determine that the vibration of the drying filter is abnormal; if the determination result is no, determine whether the vibration frequency fluctuation index is greater than or equal to a preset second deviation threshold; if the determination result is yes, determine that the vibration of the drying filter is abnormal; if the determination result is no, determine that the drying filter is not vibrating abnormally.

[0160] In another embodiment, the vibration anomaly determination module is further configured to: if the determination result is yes, generate a vibration health anomaly instruction; send the vibration health anomaly instruction to the refrigeration facility management personnel, where the vibration health anomaly instruction is used to instruct the refrigeration facility management personnel to perform vibration anomaly maintenance on the drying filter.

[0161] In another embodiment, the pressure deviation generation module is further configured to: extract refrigerant state data, filter state data, and filter inlet and outlet pressure data according to the drying filter state data, where the filter inlet and outlet pressure data includes the inlet pressure and outlet pressure of the drying filter; generate a refrigerant influence coefficient according to the refrigerant state data; generate a dryer influence coefficient according to the filter state data; generate a pressure drop deviation trend factor based on the following formula:

[0162] ;

[0163] where is the pressure drop deviation trend factor at the data sampling time point t, is the outlet pressure of the drying filter at the data sampling time point t, is the inlet pressure of the drying filter at the data sampling time point t, is the refrigerant dynamic viscosity at the data sampling time point t, L is the length of the filter filling layer of the drying filter, is the permeability coefficient of the drying filter, is the filter cross-sectional area of the drying filter, is the refrigerant flow rate 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.

[0164] In another embodiment, the refrigerant state data includes refrigerant mass flow rate, refrigerant density, and refrigerant dynamic viscosity; the pressure deviation generation module is further configured to: generate a refrigerant influence coefficient based on the following formula: ;

[0165] Among them, 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 rate 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 refrigerant dynamic viscosity at the data sampling time point t, is the dynamic viscosity reference value, is the viscosity index.

[0166] In another embodiment, the refrigerant state data further includes the refrigerant flow velocity; the pressure deviation generation module is further configured to: obtain the used 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 used time; update the standard deviation and the mean value of the historical flow velocity according to the refrigerant flow velocity, and generate a real-time flow velocity standard deviation and a real-time flow velocity mean value; generate a dryer influence coefficient based on the following formula:

[0167] ;

[0168] Among them, is the dryer influence coefficient, is the filter medium influence coefficient, is the filter element influence coefficient, is the roughness of the surface of the filter element in the drying filter, is the particle diameter of the filter medium in the drying filter, G is the filter shape influence coefficient of the filter, is the porosity of the filter element in the drying filter, is the real-time flow velocity standard deviation, is the real-time flow velocity mean value.

[0169] In another embodiment, the health diagnosis generation module is further configured to: if the judgment is negative, generate a normal pressure drop deviation trend instruction; generate a current health assessment report according to the normal pressure drop deviation trend instruction, and store the current health assessment report.

[0170] In one embodiment, a computer device is further provided, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned refrigeration facility health trend diagnosis method based on real-time device monitoring are implemented.

[0171] 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 refrigeration facility health trend diagnosis method based on real-time device monitoring are implemented.

[0172] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can 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 (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0173] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope described in this specification.

[0174] The above-described embodiments only represent several implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for diagnosing the health trend of refrigeration facilities based on real-time equipment monitoring, characterized in that: Methods include: Acquire the filtration process vibration data of the filter drier in the refrigeration system, and generate a filtration vibration health index according to the filtration process vibration data; Determine whether the filter drier is vibrating abnormally based on the filter vibration health index; If the judgment is no, then 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; Determine whether the pressure drop deviation trend factor is greater than or equal to the standard pressure drop trend threshold, and if so, generate a filter health diagnosis abnormality instruction, and instruct the refrigeration facility management personnel to perform maintenance according to the filter health diagnosis abnormality instruction; Generate pressure drop deviation trend factors based on dry filtration status data, including: 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 refrigerant status data; Generate dryer influence coefficients based on 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.

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: 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.

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 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.

8. The refrigeration facility health trend diagnosis method based on real-time equipment monitoring according to claim 7, 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.

9. 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; A health diagnosis generation module, used to determine whether the pressure drop deviation trend factor is greater than or equal to a standard pressure drop trend threshold, and if so, generate a filter health diagnosis abnormality instruction, and instruct a refrigeration facility manager to perform maintenance according to the filter health diagnosis abnormality instruction; 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.

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