Intelligent heat exchanger monitoring system and method based on sensing data analysis

Abstract

The invention relates to the technical field of heat exchanger monitoring, and particularly discloses an intelligent heat exchanger monitoring system and method based on sensing data analysis, and the method comprises the steps: S1, collecting the temperature, pressure and flow data of a primary side fluid loop and a secondary side fluid loop of a heat exchanger at a preset collection frequency through a data collection module; s2, the collected data are subjected to standardization processing through a calculation processing module, and the heat exchange efficiency, the flow resistance deviation and the flow deviation of the heat exchanger are obtained through analysis and calculation; through multi-parameter collaborative analysis and rapid matching of the fault feature library, the limitation of empirical judgment is broken through, real-time diagnosis and early warning of sudden faults such as internal leakage, external leakage and blockage are realized, performance trend prediction and analysis based on historical data are realized, a traditional passive maintenance mode is thoroughly changed, and the maintenance efficiency is improved. A quantitative tracking mechanism for scaling, corrosion and other progressive performance degradation is established, and a predictive maintenance strategy based on the equipment health state is formed.

Description

Technical Field

[0001] This invention relates to the field of heat exchanger monitoring technology, specifically to an intelligent monitoring system and method for heat exchangers based on sensor data analysis. Background Technology

[0002] Heat exchangers, as core equipment for achieving efficient heat transfer between different fluids, are widely used in many industrial fields such as urban district heating, chemical industry, metallurgy, and energy. Especially in urban district heating systems, various types of heat exchangers, such as plate and shell-and-tube heat exchangers, in heat exchange stations undertake the key task of exchanging heat between the primary heat source and the secondary user heating circulating water. The stability and energy efficiency of their operation are directly related to the safety, economy, and user experience of the entire heating network.

[0003] Currently, the monitoring and maintenance of heat exchangers largely rely on traditional periodic inspections and manual judgment based on experience. Operators estimate heat exchange efficiency by reading the temperature, pressure, and flow meter readings installed on the primary and secondary pipes, combined with empirical formulas, to determine the equipment's operating status. This model has significant limitations: First, it is difficult to achieve real-time diagnosis and early warning of sudden faults (such as internal leaks, external leaks, and blockages). For example, when a heat exchanger experiences a minor internal leak, the parameter changes are not significant in the early stages, but traditional methods are difficult to detect in time. Often, the fault is only discovered when it expands and affects the quality of heating, resulting in energy waste and increased maintenance costs. Second, for the slow performance degradation of heat exchangers caused by scaling, corrosion, etc., traditional methods lack effective quantitative tracking and trend prediction means. Maintenance decisions are usually passive responses made after efficiency has seriously declined or a fault has occurred, rather than proactive early warnings based on the equipment's health status, making it difficult to perform effective and timely maintenance of heat exchangers. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent monitoring system and method for heat exchangers based on sensor data analysis, and to solve the following technical problems: The question of how to accurately diagnose sudden failures in heat exchangers in real time and accurately predict slow performance degradation.

[0005] The objective of this invention can be achieved through the following technical solutions: A method for intelligent monitoring of heat exchangers based on sensor data analysis, the method comprising: S1. Collect temperature, pressure and flow data on the primary and secondary fluid circuits of the heat exchanger at a preset acquisition frequency through the data acquisition module; S2. The collected data is standardized through the calculation and processing module, and the heat exchange efficiency, flow resistance deviation and flow rate deviation of the heat exchanger are obtained by analysis and calculation. S3. The heat exchange efficiency, flow resistance deviation and flow rate deviation of the heat exchanger are quickly matched with the preset fault feature library through the fault analysis module to determine whether there is a sudden fault and generate an alarm signal. The sudden fault includes at least internal leakage, external leakage and blockage. S4. The predictive analysis module reads the heat exchange efficiency and flow resistance deviation datasets from the historical database at a preset analysis frequency, comprehensively analyzes them to obtain the performance change trend of the heat exchanger, judges the status of the heat exchanger based on the performance change trend, and generates predictive maintenance reminders.

[0006] Furthermore, the process of obtaining the heat exchange efficiency, flow resistance deviation, and flow rate deviation of the heat exchanger includes: The heat exchange efficiency of the heat exchanger is obtained by analysis and calculation using formula (1). ; The flow resistance deviation is obtained by analyzing and calculating using formula (2). ; The flow deviation is obtained by analyzing and calculating using formula (3). ; in, This refers to the temperature of the fluid at the secondary side inlet of the heat exchanger. This refers to the temperature of the fluid at the secondary side outlet of the heat exchanger. This refers to the temperature of the fluid at the primary side inlet of the heat exchanger. This refers to the pressure measured at the primary side inlet of the heat exchanger. The pressure measured at the primary side outlet of the heat exchanger. This is the pressure measured at the secondary side inlet of the heat exchanger. This is the pressure measured at the secondary side outlet of the heat exchanger. This represents the average flow rate of the fluid on the primary side of the heat exchanger. This represents the average flow rate of the fluid on the secondary side of the heat exchanger. The flow regime index is obtained through calibration under reference conditions. This represents the pipe resistance coefficient on the primary side of the heat exchanger under reference conditions. This represents the pipe resistance coefficient on the secondary side of the heat exchanger under reference conditions. This refers to the fluid flow rate at the primary side inlet of the heat exchanger. This refers to the fluid flow rate at the primary side outlet of the heat exchanger. This refers to the fluid flow rate at the secondary side inlet of the heat exchanger. This represents the fluid flow rate at the secondary side outlet of the heat exchanger.

[0007] Furthermore, the process of determining whether a sudden fault exists and generating an alarm signal includes: Flow deviation Deviation from preset traffic warning Perform a comparison; like If the risk of external leakage is detected, an external leakage alarm signal will be generated. Conversely, for the deviation of flow resistance and heat exchange efficiency Further analysis is needed.

[0008] Furthermore, the deviation from the flow resistance and heat exchange efficiency The process of further analysis includes: The change in flow resistance deviation obtained from the analysis of the two most recent data collections is obtained by formula (4). ; The change in heat exchange efficiency obtained from the analysis of the two most recent data collections is obtained by formula (5). ; in, The flow resistance deviation is obtained from the analysis of the most recently collected data. This refers to the flow resistance deviation obtained from the analysis of the previously collected data. The heat exchange efficiency is obtained from the analysis of the most recently collected data. The heat exchange efficiency obtained from the analysis of the previously collected data; like and If the fault is determined to be a flow channel blockage, a blockage alarm signal will be generated. like and If the fault is determined to be a blockage in a local flow section, a local blockage alarm signal will be generated. like and If the fault is determined to be a risk of internal leakage in the heat exchanger, an internal leakage alarm signal will be generated. like and If not, no alarm signal will be generated; in, The allowable threshold for the change in flow resistance deviation. This is the allowable threshold for changes in heat exchange efficiency.

[0009] Further, in step S4, the process of generating predictive maintenance reminders includes: The slope of the heat transfer efficiency change within the preset sliding time window is obtained by analyzing and calculating using formula (6). ; The slope of the change in flow resistance deviation within the preset sliding time window is obtained by analyzing and calculating using formula (7). ; Where N is the number of data points within the preset sliding time window. , It is a time series. This represents the heat exchange efficiency at the corresponding time point. This represents the flow resistance deviation value at the corresponding time point; The slope of the change in heat transfer efficiency The slope of the change in flow resistance deviation And the correlation coefficient between the heat exchanger's heat exchange efficiency and flow resistance deviation. A comprehensive analysis was conducted to obtain the abnormal performance parameters of the heat exchanger. ; Performance anomaly parameters Comparison value with preset performance standard Perform a comparison; like If this is detected, the heat exchanger's performance is deemed to be abnormal, and a performance degradation warning signal is generated. Conversely, if the performance changes of the heat exchanger are within the expected range, then predictive maintenance can be performed. The heat exchange efficiency of the heat exchanger is predicted to reach the preset heat exchange efficiency maintenance threshold by analyzing and calculating using formula (8). Required remaining time ; like Less than the preset time advance Then, predictive maintenance reminders will be generated.

[0010] Furthermore, the process of obtaining the abnormal performance parameters of the heat exchanger includes: The abnormal performance parameters of the heat exchanger are obtained by analysis and calculation using formulas (9) and (10). ; in, This represents the average slope of the heat transfer efficiency change within a preset sliding time window. This represents the average slope of the change in flow resistance deviation within a preset sliding time window.

[0011] Furthermore, the method also includes: S5. Based on the calculated heat transfer efficiency Flow resistance deviation and flow deviation The preset acquisition frequency of the data acquisition module is dynamically adjusted; The preset acquisition frequency is obtained by calculation and analysis using formulas (11)-(12). ; in, To collect frequency adjustment factors, This represents the initial maximum heat exchange efficiency of the heat exchanger. This is the preset maximum allowable value for flow resistance deviation. The preset maximum allowable flow deviation value, , , As the first weighting coefficient, To preset the minimum sampling frequency, This is the preset maximum sampling frequency.

[0012] Furthermore, the method also includes: S6. Based on performance anomaly parameters And predicting that the heat exchanger's heat exchange efficiency will reach the preset heat exchange efficiency maintenance threshold. Required remaining time The preset analysis frequency of the predictive analysis module is dynamically adjusted. The preset analysis frequency is obtained by analysis and calculation using formulas (13)-(16). ; in, To analyze urgency factors, As the performance anomaly urgency factor, To maintain the urgency of the maintenance period, , This is the second weighting coefficient. For dynamic reference time, To preset the minimum analysis frequency, To preset the highest analysis frequency, To take the minimum value within the parentheses.

[0013] A heat exchanger intelligent monitoring system based on sensor data analysis, the system being used for an intelligent monitoring method of heat exchangers based on sensor data analysis, the system comprising: The data acquisition module is used to acquire temperature, pressure and flow rate data on the primary and secondary fluid loops of the heat exchanger at a preset acquisition frequency. The calculation and processing module is used to standardize the data collected by the data acquisition module and analyze and calculate the heat exchange efficiency, flow resistance deviation and flow deviation of the heat exchanger. The fault analysis module is used to quickly match the heat exchange efficiency, flow resistance deviation and flow rate deviation obtained by the calculation and processing module with the preset fault feature library to determine whether there is a sudden fault and generate an alarm signal. The predictive analysis module is used to read heat exchange efficiency and flow resistance deviation datasets from the historical database at a preset analysis frequency, comprehensively analyze them to obtain the performance change trend of the heat exchanger, and judge the status of the heat exchanger based on the performance change trend to generate predictive maintenance reminders. The acquisition frequency adjustment module is used to dynamically adjust the preset acquisition frequency of the data acquisition module based on the heat exchange efficiency, flow resistance deviation and flow deviation calculated by the calculation and processing module. The analysis frequency adjustment module is used to dynamically adjust the preset analysis frequency of the predictive analysis module based on the performance anomaly parameters obtained by the predictive analysis module and the predicted remaining time. A historical database is used to store the data collected by the data acquisition module, the calculation results of the calculation processing module, the fault feature library of the fault analysis module, and the historical data required by the predictive analysis module.

[0014] The beneficial effects of this invention are: (1) This invention achieves in-depth perception and precise control of the heat exchanger's operating status by establishing a monitoring system from data acquisition and real-time calculation to intelligent diagnosis: First, through high-frequency data acquisition and standardized processing, it solves the defect of traditional manual inspection being insensitive to changes in small parameters, and can accurately capture subtle abnormalities such as the initial stage of internal leakage; Second, through multi-parameter collaborative analysis and rapid matching of fault feature database, it breaks through the limitations of experience judgment and realizes real-time diagnosis and early warning of sudden faults such as internal leakage, external leakage, and blockage; Finally, based on the performance trend prediction analysis of historical data, it completely changes the traditional passive maintenance mode, establishes a quantitative tracking mechanism for progressive performance degradation such as scaling and corrosion, and forms a predictive maintenance strategy based on the health status of the equipment, which effectively improves the operating safety, energy utilization efficiency and maintenance economy of the heat exchanger.

[0015] (2) By analyzing the deviation of key performance parameters in real time and dynamically adjusting the data acquisition frequency, the present invention achieves intelligent matching between monitoring intensity and equipment status. Under the premise of ensuring the safety monitoring effect of the system, it effectively reduces unnecessary data acquisition, transmission and storage overhead during stable operation, and significantly improves the resource utilization efficiency and economy of the monitoring system.

[0016] (3) By comprehensively considering the degree of performance abnormality and the urgency of maintenance time, this invention dynamically adjusts the execution frequency of the predictive analysis module, thereby achieving a precise match between analysis resources and equipment status requirements. This not only saves computing resources when the equipment status is stable, but also automatically increases the monitoring frequency when abnormalities occur or maintenance is imminent, ensuring the timeliness and accuracy of predictive analysis, while optimizing the overall resource utilization efficiency of the system. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart of the steps of an intelligent monitoring method for heat exchangers based on sensor data analysis proposed in this invention. Figure 2 This is a schematic diagram of a heat exchanger intelligent monitoring system based on sensor data analysis proposed in this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 As shown, in one embodiment, a method for intelligent monitoring of a heat exchanger based on sensor data analysis is provided, the method comprising: S1. The data acquisition module collects temperature, pressure, and flow rate data on the primary and secondary fluid loops of the heat exchanger at a preset acquisition frequency. The preset acquisition frequency can be set according to the heat exchanger type and operating environment, with a typical range of 1 time / second to 1 time / minute to ensure data real-time performance and representativeness. The data acquisition module integrates a temperature sensor, a pressure sensor, and a flow meter, and is installed on the inlet and outlet pipes of the primary and secondary sides for continuous monitoring of the fluid state. S2. The collected data is standardized through the calculation and processing module, including data cleaning, normalization and noise reduction, to eliminate dimensional differences and random errors. Then, based on the standardized data, the key performance parameters of the heat exchanger are analyzed and calculated, including heat exchange efficiency, flow resistance deviation and flow rate deviation. S3. The heat exchange efficiency, flow resistance deviation, and flow rate deviation of the heat exchanger are quickly matched with a preset fault feature library through the fault analysis module. The fault feature library is constructed based on historical fault data, experimental data, and expert knowledge, and includes the threshold range of characteristic parameters for various fault modes. Then, it is determined whether there is a sudden fault, and an alarm signal is generated when the match is successful. The alarm signal is output through the human-machine interface or communication module to prompt the operation and maintenance personnel to intervene in time. The sudden fault includes at least internal leakage, external leakage, and blockage. S4. The predictive analysis module reads the heat exchange efficiency and flow resistance deviation dataset from the historical database at a preset analysis frequency. The preset analysis frequency is usually set to 1 time / hour to 1 time / day. Then, the performance change trend of the heat exchanger is obtained through comprehensive analysis, and the status of the heat exchanger is judged by the performance change trend, generating predictive maintenance reminders.

[0021] Through the above technical solution, this embodiment provides a heat exchanger intelligent monitoring method based on sensor data analysis. This method establishes a monitoring system from data acquisition and real-time calculation to intelligent diagnosis, achieving deep perception and precise control of the heat exchanger's operating status. First, through high-frequency data acquisition and standardized processing, it overcomes the shortcomings of traditional manual inspections in being insensitive to minor parameter changes, enabling precise capture of subtle anomalies such as initial internal leaks. Second, through multi-parameter collaborative analysis and rapid matching of fault feature databases, it overcomes the limitations of experience-based judgment, achieving real-time diagnosis and early warning of sudden faults such as internal leaks, external leaks, and blockages. Finally, based on historical data performance trend prediction analysis, it completely changes the traditional passive maintenance mode, establishing a quantitative tracking mechanism for progressive performance degradation such as scaling and corrosion, forming a predictive maintenance strategy based on equipment health status, effectively improving the heat exchanger's operational safety, energy efficiency, and maintenance economy.

[0022] In one embodiment, the process of obtaining the heat exchange efficiency, flow resistance deviation, and flow rate deviation of the heat exchanger includes: The heat exchange efficiency of the heat exchanger is obtained by analysis and calculation using formula (1). It reflects the instantaneous state of the heat transfer performance of the core of the heat exchanger; The flow resistance deviation is obtained by analyzing and calculating using formula (2). It quantifies the smoothness of the system flow path; The flow deviation is obtained by analyzing and calculating using formula (3). It can detect potential leaks or measurement misalignments; in, This refers to the temperature of the fluid at the secondary side inlet of the heat exchanger. This refers to the temperature of the fluid at the secondary side outlet of the heat exchanger. This refers to the temperature of the fluid at the primary side inlet of the heat exchanger. , , All of these data can be directly collected in real time by temperature sensors installed at the corresponding pipe locations. This refers to the pressure measured at the primary side inlet of the heat exchanger. The pressure measured at the primary side outlet of the heat exchanger. This is the pressure measured at the secondary side inlet of the heat exchanger. This is the pressure measured at the secondary side outlet of the heat exchanger. , , , All of these data can be directly acquired in real time by pressure sensors or pressure transmitters installed at the corresponding pipeline locations. This represents the average flow rate of the fluid on the primary side of the heat exchanger, typically taken as the arithmetic mean of the inlet and outlet flow rates of the primary side flow path. This represents the average flow rate of the fluid on the secondary side of the heat exchanger, typically taken as the arithmetic mean of the inlet and outlet flow rates of the secondary side flow path. The flow index, obtained through calibration under baseline conditions, is fitted using experimental data or set according to fluid dynamics theory. It is used to correct for the nonlinear effect of flow rate on pressure drop, ensuring that under different operating conditions, the flow resistance deviation accurately reflects the cleanliness of the flow path, rather than the flow rate. This represents the pipe resistance coefficient on the primary side of the heat exchanger under reference conditions. This represents the pipe resistance coefficient on the secondary side of the heat exchanger under reference conditions. , These figures can be obtained by back-calculation from measured data during the initial operation of the heat exchanger or after thorough cleaning. This refers to the fluid flow rate at the primary side inlet of the heat exchanger. This refers to the fluid flow rate at the primary side outlet of the heat exchanger. This refers to the fluid flow rate at the secondary side inlet of the heat exchanger. This represents the fluid flow rate at the secondary side outlet of the heat exchanger. , , , All of these can be directly measured using flow meters (such as electromagnetic flow meters or ultrasonic flow meters) installed on the corresponding pipelines.

[0023] Through the above technical solution, this embodiment provides a method for accurately quantifying the key performance and fault indicators of heat exchangers. By introducing benchmark state parameters and thermodynamic and fluid dynamic formulas, the method not only achieves high-precision calculation of heat exchange efficiency, flow resistance deviation, and flow deviation, but also effectively eliminates the influence of operating condition fluctuations on diagnostic parameters. This allows subsequent fault diagnosis and predictive maintenance to be based on reliable and quantitative data, significantly improving the accuracy of condition judgment and the timeliness of early warning.

[0024] In one embodiment, the process of determining whether a sudden fault exists and generating an alarm signal includes: Flow deviation Deviation from preset traffic warning The preset traffic warning deviation was compared. The value can be set according to the system's allowable normal measurement error, small fluctuation range, and safety margin. It is usually obtained through statistical analysis of historical normal operation data and is typically set to 3% to 5%. For systems with extremely high requirements or low flow conditions, the value may be even smaller. like If the heat exchanger is found to have an external leakage risk, an external leakage alarm signal will be generated. This is because in a closed system, a significant flow imbalance usually means that the medium has leaked into the external environment. Conversely, if the flow rate deviation is within the allowable range, then the flow resistance deviation will be less affected. and heat exchange efficiency Further analysis is needed.

[0025] The deviation of flow resistance and heat exchange efficiency The process of further analysis includes: The change in flow resistance deviation obtained from the analysis of the two most recent data collections is obtained by formula (4). ; The change in heat exchange efficiency obtained from the analysis of the two most recent data collections is obtained by formula (5). ; in, The flow resistance deviation is obtained from the analysis of the most recently collected data. This refers to the flow resistance deviation obtained from the analysis of the previously collected data. The heat exchange efficiency is obtained from the analysis of the most recently collected data. The heat exchange efficiency obtained from the analysis of the previously collected data; like and If the flow channel is blocked, an alarm signal for blockage will be generated. This is because flow channel blockage will rapidly increase flow resistance, and at the same time, the heat exchange efficiency will drop rapidly due to the heat insulation effect of the dirt and the reduction of the effective heat transfer area. like and If the fault is determined to be a local blockage of the flow section, a local blockage alarm signal is generated. In this state, the flow resistance has increased significantly, but the instantaneous change in heat exchange efficiency is not significant. This usually corresponds to the initial stage of blockage or occurs in a non-main flow channel. like and If the fault is identified as a risk of internal leakage in the heat exchanger, an internal leakage alarm signal is generated. Internal leakage causes some hot fluid to short-circuit and directly mix into the cold fluid, reducing the effective heat transfer temperature and pressure, thereby causing a sharp drop in heat exchange efficiency, but usually does not cause a simultaneous significant increase in flow resistance. like and If the system is stable and shows no signs of sudden failure, no alarm signal will be generated. in, This is the allowable threshold for the change in flow resistance deviation. It can be set based on the maximum allowable range of normal resistance fluctuations, and must be greater than the fluctuations under normal operating conditions. It is usually determined based on historical data or simulations. The allowable threshold for heat exchange efficiency variation can be set based on the normal efficiency decay rate and measurement noise level.

[0026] Through the above technical solution, this embodiment provides a method for accurate diagnosis of sudden faults in heat exchangers. The method constructs a two-level diagnostic logic from flow balance screening to flow resistance and heat exchange efficiency change trend analysis, and introduces a comparison mechanism between dynamic changes and static thresholds. It can clearly distinguish the characteristics of different fault modes such as external leakage, blockage and internal leakage, significantly reducing the false alarm and false alarm rates of single parameter diagnosis, realizing rapid and accurate identification and classification early warning of sudden faults, providing operation and maintenance personnel with clear fault handling directions, and greatly improving the safety and reliability of system operation.

[0027] In one embodiment, step S4, the process of generating predictive maintenance reminders, includes: The slope of the heat transfer efficiency change within the preset sliding time window is obtained by analyzing and calculating using formula (6). ; The slope of the change in flow resistance deviation within the preset sliding time window is obtained by analyzing and calculating using formula (7). ; The preset sliding time window can be manually set according to equipment characteristics and maintenance needs. It typically contains enough data points to reflect trends (e.g., 30-90 points), and the corresponding time span can be several weeks to several months. N is the number of data points within the preset sliding time window. , It is a time series. This represents the heat exchange efficiency at the corresponding time point. This represents the flow resistance deviation value at the corresponding time point; The slope of the change in heat transfer efficiency The slope of the change in flow resistance deviation And the correlation coefficient between the heat exchanger's heat exchange efficiency and flow resistance deviation. A comprehensive analysis was conducted to obtain the abnormal performance parameters of the heat exchanger. ; Performance anomaly parameters Comparison value with preset performance standard The preset performance standard reference value was compared. It can be set based on statistical analysis of a large amount of normal historical data, such as taking the 95th percentile of the normal distribution; like If the heat exchanger's performance changes abnormally, a performance degradation warning signal will be generated, indicating that there may be accelerated degradation or unknown fault modes, and an inspection should be carried out first. Conversely, if the performance changes of the heat exchanger are within the expected range, then predictive maintenance can be performed. The heat exchange efficiency of the heat exchanger is predicted to reach the preset heat exchange efficiency maintenance threshold by analyzing and calculating using formula (8). Required remaining time The preset heat exchange efficiency maintenance threshold It can be set based on experience, and is usually set to the minimum heat exchange efficiency that does not affect the purpose of heat exchange; like Less than the preset time advance The preset time advance amount Predictive maintenance reminders can be generated by manually setting the time required for preparation of maintenance work (such as ordering spare parts and arranging downtime).

[0028] The process of obtaining the abnormal performance parameters of the heat exchanger includes: The abnormal performance parameters of the heat exchanger are obtained by analysis and calculation using formulas (9) and (10). ; in, This represents the average slope of the heat transfer efficiency change within a preset sliding time window. This represents the average slope of the change in flow resistance deviation within a preset sliding time window.

[0029] Through the above technical solution, this embodiment provides a predictive maintenance decision-making method for heat exchangers. The method first performs performance anomaly detection to identify atypical accelerated degradation, and then predicts normal maintenance time. It can not only accurately predict the remaining service life of the heat exchanger based on historical trends and trigger maintenance reminders at the optimal time, but also effectively identify abnormal performance degradation patterns and issue early warnings. This achieves dual protection from routine predictive maintenance and early detection of anomalies, significantly improving the accuracy, foresight and reliability of maintenance strategies.

[0030] In one embodiment, the method further includes: S5. Based on the calculated heat transfer efficiency Flow resistance deviation and flow deviation The preset acquisition frequency of the data acquisition module is dynamically adjusted to achieve intelligent matching between monitoring intensity and system status. The preset acquisition frequency is obtained by calculation and analysis using formulas (11)-(12). ; in, To acquire the frequency adjustment factor, when A value close to 0 indicates stable system operation, allowing for a reduction in the data acquisition frequency; when... A value close to 1 indicates an abnormal system state, requiring an increase in the data collection frequency for closer monitoring. The initial maximum heat exchange efficiency of a heat exchanger is the optimal value obtained through continuous monitoring during the initial operation of the heat exchanger or after thorough cleaning and maintenance. It represents the best performance level of the equipment under ideal conditions and serves as a reference benchmark for assessing the current degree of performance degradation. The value is determined based on the equipment type and design parameters; for plate heat exchangers, it is typically 90%–95%, and for shell-and-tube heat exchangers, it is typically 85%–92%. The preset maximum allowable value for flow resistance deviation can be determined based on a comprehensive analysis of equipment safety operation specifications, historical fault data, and engineering experience. The preset maximum allowable flow deviation value can be determined based on equipment safety operation specifications, historical fault data statistical analysis, and engineering experience. , , The first weighting coefficient can be determined based on the degree of influence of each factor on the system's safety and economic operation, using methods such as expert scoring, analytic hierarchy process (AHP), or historical failure data analysis. To preset the minimum sampling frequency, To preset the highest sampling frequency, , The settings can be comprehensively configured based on hardware limitations such as system data processing capabilities, storage resources, and communication bandwidth, combined with monitoring accuracy requirements. Typically, it is administered once every 1 to 5 minutes, for a stable condition. Typically 1 to 10 times per second, used for abnormal or faulty conditions.

[0031] Through the above technical solution, this embodiment provides a method for optimizing the monitoring and acquisition frequency of a heat exchanger. The method dynamically adjusts the data acquisition frequency by analyzing the deviation of key performance parameters in real time, thereby achieving intelligent matching between monitoring intensity and equipment status. Under the premise of ensuring the safety monitoring effect of the system, it effectively reduces unnecessary data acquisition, transmission and storage overhead during stable operation, and significantly improves the resource utilization efficiency and economy of the monitoring system.

[0032] The method further includes: S6. Based on performance anomaly parameters And predicting that the heat exchanger's heat exchange efficiency will reach the preset heat exchange efficiency maintenance threshold. Required remaining time The preset analysis frequency of the predictive analysis module is dynamically adjusted. The preset analysis frequency is obtained by analysis and calculation using formulas (13)-(16). ; in, To analyze urgency factors, As the performance anomaly urgency factor, To maintain the urgency of the maintenance period, , The second weighting factor can be assigned based on the importance of performance anomalies and maintenance time in practical applications, and is usually determined through historical data analysis. The dynamic baseline time can be defined as the maximum value among all predicted remaining times over a past period, representing the longest maintenance cycle expected for the system in the near future. To preset the minimum analysis frequency, To preset the highest analysis frequency, , The settings can be comprehensively configured based on the computational complexity of predictive analysis, system resource constraints, and actual monitoring requirements. Typically once every 4 to 24 hours. Typically, it's once every 15 to 60 minutes. To take the minimum value within the parentheses.

[0033] Through the above technical solution, this embodiment provides a method for adaptive adjustment of the predictive analysis frequency of a heat exchanger. The method dynamically adjusts the execution frequency of the predictive analysis module by comprehensively considering two dimensions: the degree of performance anomaly and the urgency of maintenance time. This achieves a precise match between analysis resources and equipment status requirements. It can save computing resources when the equipment status is stable, and automatically increase the monitoring frequency when anomalies occur or maintenance is imminent, ensuring the timeliness and accuracy of predictive analysis, while optimizing the overall resource utilization efficiency of the system.

[0034] Please see Figure 2 As shown, in one embodiment, a heat exchanger intelligent monitoring system based on sensor data analysis is provided, characterized in that the system is used for a heat exchanger intelligent monitoring method based on sensor data analysis, the system comprising: The data acquisition module is used to acquire temperature, pressure and flow rate data on the primary and secondary fluid loops of the heat exchanger at a preset acquisition frequency. The calculation and processing module is used to standardize the data collected by the data acquisition module and analyze and calculate the heat exchange efficiency, flow resistance deviation and flow deviation of the heat exchanger. The fault analysis module is used to quickly match the heat exchange efficiency, flow resistance deviation and flow rate deviation obtained by the calculation and processing module with the preset fault feature library to determine whether there is a sudden fault and generate an alarm signal. The predictive analysis module is used to read heat exchange efficiency and flow resistance deviation datasets from the historical database at a preset analysis frequency, comprehensively analyze them to obtain the performance change trend of the heat exchanger, and judge the status of the heat exchanger based on the performance change trend to generate predictive maintenance reminders. The acquisition frequency adjustment module is used to dynamically adjust the preset acquisition frequency of the data acquisition module based on the heat exchange efficiency, flow resistance deviation and flow deviation calculated by the calculation and processing module. The analysis frequency adjustment module is used to dynamically adjust the preset analysis frequency of the predictive analysis module based on the performance anomaly parameters obtained by the predictive analysis module and the predicted remaining time. A historical database is used to store the data collected by the data acquisition module, the calculation results of the calculation processing module, the fault feature library of the fault analysis module, and the historical data required by the predictive analysis module.

[0035] Through the above technical solution, this embodiment provides an intelligent monitoring system for heat exchangers based on sensor data analysis. The system constructs a complete intelligent monitoring system from data acquisition to decision output through multi-module collaborative work and dual-frequency adaptive adjustment mechanism. It not only realizes real-time and accurate diagnosis of sudden failures, but also establishes a predictive maintenance system based on performance degradation trends. At the same time, it optimizes system operating efficiency through adaptive resource allocation. Ultimately, it realizes the intelligent operation and maintenance transformation of heat exchangers from passive maintenance to proactive prevention, and from experience-based judgment to data analysis, significantly improving equipment reliability, energy efficiency, and maintenance economy.

[0036] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for intelligent monitoring of heat exchangers based on sensor data analysis, characterized in that, The method includes: S1. Collect temperature, pressure and flow data on the primary and secondary fluid circuits of the heat exchanger at a preset acquisition frequency through the data acquisition module; S2. The collected data is standardized through the calculation and processing module, and the heat exchange efficiency, flow resistance deviation and flow rate deviation of the heat exchanger are obtained by analysis and calculation. S3. The heat exchange efficiency, flow resistance deviation and flow rate deviation of the heat exchanger are quickly matched with the preset fault feature library through the fault analysis module to determine whether there is a sudden fault and generate an alarm signal. The sudden fault includes at least internal leakage, external leakage and blockage. S4. The predictive analysis module reads the heat exchange efficiency and flow resistance deviation datasets from the historical database at a preset analysis frequency, comprehensively analyzes them to obtain the performance change trend of the heat exchanger, judges the status of the heat exchanger based on the performance change trend, and generates predictive maintenance reminders.

2. The intelligent monitoring method for heat exchangers based on sensor data analysis according to claim 1, characterized in that, The process of obtaining the heat exchange efficiency, flow resistance deviation, and flow rate deviation of the heat exchanger includes: The heat exchange efficiency of the heat exchanger is obtained by analysis and calculation using formula (1). ; The flow resistance deviation is obtained by analyzing and calculating using formula (2). ; The flow deviation is obtained by analyzing and calculating using formula (3). ; in, This refers to the temperature of the fluid at the secondary side inlet of the heat exchanger. This refers to the temperature of the fluid at the secondary side outlet of the heat exchanger. This refers to the temperature of the fluid at the primary side inlet of the heat exchanger. This refers to the pressure measured at the primary side inlet of the heat exchanger. The pressure measured at the primary side outlet of the heat exchanger. This is the pressure measured at the secondary side inlet of the heat exchanger. This is the pressure measured at the secondary side outlet of the heat exchanger. This represents the average flow rate of the fluid on the primary side of the heat exchanger. This represents the average flow rate of the fluid on the secondary side of the heat exchanger. The flow regime index is obtained through calibration under reference conditions. This represents the pipe resistance coefficient on the primary side of the heat exchanger under reference conditions. This represents the pipe resistance coefficient on the secondary side of the heat exchanger under reference conditions. This refers to the fluid flow rate at the primary side inlet of the heat exchanger. This refers to the fluid flow rate at the primary side outlet of the heat exchanger. This refers to the fluid flow rate at the secondary side inlet of the heat exchanger. This represents the fluid flow rate at the secondary side outlet of the heat exchanger.

3. The intelligent monitoring method for heat exchangers based on sensor data analysis according to claim 2, characterized in that, The process of determining whether a sudden fault exists and generating an alarm signal includes: Flow deviation Deviation from preset traffic warning Perform a comparison; like If the risk of external leakage is detected, an external leakage alarm signal will be generated. Conversely, for the deviation of flow resistance and heat exchange efficiency Further analysis is needed.

4. The intelligent monitoring method for heat exchangers based on sensor data analysis according to claim 3, characterized in that, The deviation of flow resistance and heat exchange efficiency The process of further analysis includes: The change in flow resistance deviation obtained from the analysis of the two most recent data collections is obtained by formula (4). ; The change in heat exchange efficiency obtained from the analysis of the two most recent data collections is obtained by formula (5). ; in, The flow resistance deviation is obtained from the analysis of the most recently collected data. This refers to the flow resistance deviation obtained from the analysis of the previously collected data. The heat exchange efficiency is obtained from the analysis of the most recently collected data. The heat exchange efficiency was obtained from the analysis of the previously collected data. like and If the fault is determined to be a flow channel blockage, a blockage alarm signal will be generated. like and If the fault is determined to be a blockage in a local flow section, a local blockage alarm signal will be generated. like and If the fault is determined to be a risk of internal leakage in the heat exchanger, an internal leakage alarm signal will be generated. like and If not, no alarm signal will be generated; in, The allowable threshold for the change in flow resistance deviation. This is the allowable threshold for changes in heat exchange efficiency.

5. The intelligent monitoring method for heat exchangers based on sensor data analysis according to claim 4, characterized in that, In step S4, the process of generating predictive maintenance reminders includes: The slope of the heat transfer efficiency change within the preset sliding time window is obtained by analyzing and calculating using formula (6). ; The slope of the change in flow resistance deviation within the preset sliding time window is obtained by analyzing and calculating using formula (7). ; Where N is the number of data points within the preset sliding time window. , It is a time series. This represents the heat exchange efficiency at the corresponding time point. This represents the flow resistance deviation value at the corresponding time point; The slope of the change in heat transfer efficiency The slope of the change in flow resistance deviation And the correlation coefficient between the heat exchanger's heat exchange efficiency and flow resistance deviation. A comprehensive analysis was conducted to obtain the abnormal performance parameters of the heat exchanger. ; Performance anomaly parameters Comparison value with preset performance standard Perform a comparison; like If this is detected, the heat exchanger's performance is deemed to be abnormal, and a performance degradation warning signal is generated. Conversely, if the performance changes of the heat exchanger are within the expected range, then predictive maintenance can be performed. The heat exchange efficiency of the heat exchanger is predicted to reach the preset heat exchange efficiency maintenance threshold by analyzing and calculating using formula (8). Required remaining time ; like Less than the preset time advance Then, predictive maintenance reminders will be generated.

6. The intelligent monitoring method for heat exchangers based on sensor data analysis according to claim 5, characterized in that, The process of obtaining the abnormal performance parameters of the heat exchanger includes: The abnormal performance parameters of the heat exchanger are obtained by analysis and calculation using formulas (9) and (10). ; in, This represents the average slope of the heat transfer efficiency change within a preset sliding time window. This represents the average slope of the change in flow resistance deviation within a preset sliding time window.

7. The intelligent monitoring method for heat exchangers based on sensor data analysis according to claim 6, characterized in that, The method further includes: S5. Based on the calculated heat transfer efficiency Flow resistance deviation and flow deviation The preset acquisition frequency of the data acquisition module is dynamically adjusted; The preset acquisition frequency is obtained by calculation and analysis using formulas (11) and (12). ; in, To collect frequency adjustment factors, This represents the initial maximum heat exchange efficiency of the heat exchanger. This is the preset maximum allowable value for flow resistance deviation. The preset maximum allowable flow deviation value, , , As the first weighting coefficient, To preset the minimum sampling frequency, This is the preset maximum sampling frequency.

8. The intelligent monitoring method for heat exchangers based on sensor data analysis according to claim 7, characterized in that, The method further includes: S6. Based on performance anomaly parameters And predicting that the heat exchanger's heat exchange efficiency will reach the preset heat exchange efficiency maintenance threshold. Required remaining time The preset analysis frequency of the predictive analysis module is dynamically adjusted. The preset analysis frequency is obtained by analysis and calculation using formulas (13)-(16). ; in, To analyze urgency factors, As the performance anomaly urgency factor, To maintain the urgency of the maintenance period, , This is the second weighting coefficient. For dynamic reference time, To preset the minimum analysis frequency, To preset the highest analysis frequency, To take the minimum value within the parentheses.

9. A heat exchanger intelligent monitoring system based on sensor data analysis, characterized in that, The system is used in the intelligent monitoring method for heat exchangers based on sensor data analysis as described in claim 8, and the system includes: The data acquisition module is used to acquire temperature, pressure and flow rate data on the primary and secondary fluid loops of the heat exchanger at a preset acquisition frequency. The calculation and processing module is used to standardize the data collected by the data acquisition module and analyze and calculate the heat exchange efficiency, flow resistance deviation and flow deviation of the heat exchanger. The fault analysis module is used to quickly match the heat exchange efficiency, flow resistance deviation and flow rate deviation obtained by the calculation and processing module with the preset fault feature library to determine whether there is a sudden fault and generate an alarm signal. The predictive analysis module is used to read heat exchange efficiency and flow resistance deviation datasets from the historical database at a preset analysis frequency, comprehensively analyze them to obtain the performance change trend of the heat exchanger, and judge the status of the heat exchanger based on the performance change trend to generate predictive maintenance reminders. The acquisition frequency adjustment module is used to dynamically adjust the preset acquisition frequency of the data acquisition module based on the heat exchange efficiency, flow resistance deviation and flow deviation calculated by the calculation and processing module. The analysis frequency adjustment module is used to dynamically adjust the preset analysis frequency of the predictive analysis module based on the performance anomaly parameters obtained by the predictive analysis module and the predicted remaining time. A historical database is used to store the data collected by the data acquisition module, the calculation results of the calculation processing module, the fault feature library of the fault analysis module, and the historical data required by the predictive analysis module.

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