Fault diagnosis method and apparatus for heat exchangers in nuclear power plant, and electronic device and medium

Abstract

The present application relates to the technical field of nuclear power plants, and particularly relates to a fault diagnosis method and apparatus for heat exchangers in a nuclear power plant, and an electronic device and a medium. The fault diagnosis method for heat exchangers in a nuclear power plant that is provided in the embodiments of the present application comprises: performing detection on a heat exchanger in a nuclear power plant, so as to obtain actual working condition data; acquiring health working condition data which matches the heat exchanger in the nuclear power plant; performing coarse qualitative fault analysis on the actual working condition data on the basis of the health working condition data, so as to determine a heat exchange operating state of the heat exchanger in the nuclear power plant; and in response to the heat exchange operating state being determined to be a faulty operating state, performing fine qualitative fault analysis on the basis of the actual working condition data, so as to determine a fault diagnosis type which matches the heat exchange operating state. In this way, the diagnosis efficiency and accuracy can further be improved during fault diagnosis performed on heat exchangers in a nuclear power plant.

Description

Nuclear power plant heat exchanger fault diagnosis method and device, electronic equipment and medium TECHNICAL FIELD

[0001] The present application relates to the technical field of nuclear power plants, in particular to a nuclear power plant heat exchanger fault diagnosis method and device, electronic equipment and medium. BACKGROUND

[0002] The equipment cooling water heat exchanger of the nuclear power plant is used to guide the heat dissipation of the equipment to meet the operating conditions of the user equipment and ensure the safety of the nuclear power plant during normal operation or accidents. The cold and hot side parameters of the equipment cooling water heat exchanger of the nuclear power plant change all the time during normal operation, and the operating conditions are complex. The common fault diagnosis method does not consider the influence of the change of the operating conditions, and is easy to diagnose wrong results.

[0003] In related technologies, for the nuclear power plant heat exchanger, some fault diagnosis methods depend on the completeness and richness of the preset knowledge base; some fault diagnosis methods depend on the past sample data, and once the sample data deviates from the actual situation, the accuracy of the diagnosis may be affected. The calculation amount of some fault diagnosis methods is usually large, and the time consumption is long. Therefore, how to further improve the diagnosis efficiency and accuracy in the process of fault diagnosis of the nuclear power plant heat exchanger has become a technical problem to be solved in the industry. SUMMARY

[0004] The present application aims to at least solve one of the technical problems existing in the prior art. To this end, the present application provides a nuclear power plant heat exchanger fault diagnosis method and device, electronic equipment and medium, which can further improve the diagnosis efficiency and accuracy in the process of fault diagnosis of the nuclear power plant heat exchanger.

[0005] The nuclear power plant heat exchanger fault diagnosis method according to the first aspect of the present application comprises:

[0006] Detecting the nuclear power plant heat exchanger to obtain actual operating condition data;

[0007] Obtaining health operating condition data matched with the nuclear power plant heat exchanger;

[0008] Performing fault rough qualitative analysis on the actual operating condition data according to the health operating condition data to determine a heat exchange operating state of the nuclear power plant heat exchanger;

[0009] In response to the heat exchange operating state being determined as a fault operating state, performing fault fine qualitative analysis based on the actual operating condition data to determine a fault diagnosis type matched with the heat exchange operating state.

[0010] According to some embodiments of the present application, in response to the heat exchange operation state being determined as a fault operation state, performing fault fine qualitative analysis based on the actual working condition data, determining a fault diagnosis type matched to the heat exchange operation state, comprising:

[0011] extracting a leakage diagnosis parameter from the actual working condition data;

[0012] substituting the leakage diagnosis parameter into a preset leakage diagnosis model for real-time calculation to obtain a leakage diagnosis calculation result;

[0013] in response to the heat exchange operation state being determined as a fault operation state, determining a fault detection time of the nuclear power plant heat exchanger;

[0014] in response to the leakage diagnosis calculation result corresponding to the fault detection time satisfying a leakage diagnosis condition, determining that the fault diagnosis type includes a leakage fault type.

[0015] According to some embodiments of the present application, the leakage diagnosis parameter includes a hot side mass flow rate, a cold side mass flow rate, a hot side inlet and outlet enthalpy value, and a cold side inlet and outlet enthalpy value, and the leakage diagnosis parameter is substituted into a preset leakage diagnosis model for real-time calculation to obtain a leakage diagnosis calculation result, comprising:

[0016] substituting the hot side mass flow rate, the cold side mass flow rate, the hot side inlet and outlet enthalpy value, and the cold side inlet and outlet enthalpy value into the corresponding leakage diagnosis model for real-time calculation to obtain a leakage factor as the leakage diagnosis calculation result;

[0017] in response to the leakage diagnosis calculation result corresponding to the fault detection time satisfying a leakage diagnosis condition, determining that the fault diagnosis type includes a leakage fault type, comprising:

[0018] calculating according to the leakage factor before the fault detection time to obtain a corresponding fluctuation variance value;

[0019] determining the leakage diagnosis condition based on the fluctuation variance value;

[0020] in response to the leakage factor corresponding to the fault detection time satisfying the leakage diagnosis condition, determining that the fault diagnosis type includes the leakage fault type.

[0021] According to some embodiments of the present application, the leakage diagnosis parameter includes a cold medium volume flow rate, a cold medium constant-pressure specific heat, a medium density, a hot side inlet temperature, a hot side outlet temperature, a cold side inlet temperature, and a cold side outlet temperature, and the leakage diagnosis parameter is substituted into a preset leakage diagnosis model for real-time calculation to obtain a leakage diagnosis calculation result, comprising:

[0022] The cold medium volume flow rate, the cold medium constant-pressure specific heat, the medium density, the hot side inlet temperature, the hot side outlet temperature, the cold side inlet temperature, and the cold side outlet temperature are substituted into the corresponding leakage diagnosis model for real-time calculation to obtain a leakage calculation amount as the leakage diagnosis calculation result;

[0023] The leakage diagnosis threshold is obtained, and the leakage diagnosis threshold is used to determine the leakage diagnosis condition.

[0024] The leakage diagnosis threshold is obtained, and the leakage diagnosis threshold is used to determine the leakage diagnosis condition.

[0025] The leakage diagnosis threshold is obtained, and the leakage diagnosis threshold is used to determine the leakage diagnosis condition.

[0026] According to some embodiments of the present application, the cold medium volume flow rate, the cold medium constant-pressure specific heat, the medium density, the hot side inlet temperature, the hot side outlet temperature, the cold side inlet temperature, and the cold side outlet temperature are substituted into the corresponding leakage diagnosis model for real-time calculation to obtain a leakage calculation amount as the leakage diagnosis calculation result, including:

[0027] According to the cold side outlet temperature and the cold side inlet temperature, a first leakage diagnosis element is constructed.

[0028] The cold medium volume flow rate is determined as a second leakage diagnosis element.

[0029] The hot side outlet temperature is determined as a third leakage diagnosis element.

[0030] According to the first leakage diagnosis element, the second leakage diagnosis element, and the third leakage diagnosis element, real-time calculation is performed in the leakage diagnosis model to obtain the leakage calculation amount as the leakage diagnosis calculation result.

[0031] According to some embodiments of the present application, the first leakage diagnosis element is constructed according to the cold side outlet temperature and the cold side inlet temperature, including:

[0032] The cold side outlet temperature and the cold side inlet temperature are processed by difference to obtain the first leakage diagnosis element.

[0033] According to some embodiments of the present application, the first leakage diagnosis element, the second leakage diagnosis element, and the third leakage diagnosis element are used to perform real-time calculation in the leakage diagnosis model to obtain the leakage calculation amount as the leakage diagnosis calculation result, including:

[0034] A product of the first leakage diagnosis element and the second leakage diagnosis element is determined as a model numerator term;

[0035] A model denominator term is determined according to the first leakage diagnosis element and the third leakage diagnosis element;

[0036] Real-time calculation is performed on the model numerator term and the model denominator term to obtain the leakage calculation amount as the leakage diagnosis calculation result.

[0037] According to some embodiments of the present application, the model denominator term is determined according to the first leakage diagnosis element and the third leakage diagnosis element, including:

[0038] The third leakage diagnosis element is determined as a minuend of the model denominator term;

[0039] Half of the first leakage diagnosis element is determined as a subtrahend of the model denominator term;

[0040] Subtraction is performed on the minuend and the subtrahend to obtain the model denominator term.

[0041] According to some embodiments of the present application, in response to the heat exchange operation state being determined as a fault operation state, performing fault fine qualitative analysis based on the actual working condition data to determine a fault diagnosis type matched with the heat exchange operation state, including:

[0042] A fouling diagnosis parameter is extracted from the actual working condition data;

[0043] The fouling diagnosis parameter is substituted into a preset fouling diagnosis model to perform real-time calculation to obtain a fouling diagnosis calculation result;

[0044] In response to the heat exchange operation state being determined as a fault operation state, a fault detection time of the nuclear power plant heat exchanger is determined;

[0045] In response to the fouling diagnosis calculation result corresponding to the fault detection time satisfying a fouling diagnosis condition, the fault diagnosis type is determined to include a fouling fault type.

[0046] According to some embodiments of the present application, the fouling diagnosis calculation result includes an actual heat transfer efficiency, and the fouling diagnosis parameter is substituted into a preset fouling diagnosis model to perform real-time calculation to obtain a fouling diagnosis calculation result, including:

[0047] A clean heat transfer efficiency and a reference heat transfer efficiency of the nuclear power plant heat exchanger are obtained;

[0048] The cleaning heat transfer efficiency, the reference heat transfer efficiency and the actual heat transfer efficiency are substituted into the corresponding fouling diagnosis model for real-time calculation to obtain a fouling factor as the fouling diagnosis calculation result;

[0049] The fouling diagnosis calculation result corresponding to the fault detection time point satisfies a fouling diagnosis condition, and the fault diagnosis type is determined to include a fouling fault type, including:

[0050] A fouling diagnosis threshold is obtained, and the fouling diagnosis condition is determined according to the fouling diagnosis threshold;

[0051] The fouling factor satisfies the fouling diagnosis condition, and the fault diagnosis type is determined to include the fouling fault type.

[0052] According to some embodiments of the present application, the fouling diagnosis parameters include medium flow rate and flow pressure loss, and the fouling diagnosis parameters are substituted into a preset fouling diagnosis model for real-time calculation to obtain a fouling diagnosis calculation result, including:

[0053] The medium flow rate and the flow pressure loss are substituted into the corresponding fouling diagnosis model for real-time calculation to obtain a scale layer thickness as the fouling diagnosis calculation result;

[0054] The fouling diagnosis calculation result corresponding to the fault detection time point satisfies a fouling diagnosis condition, and the fault diagnosis type is determined to include a fouling fault type, including:

[0055] A scale layer thickness threshold is obtained, and the fouling diagnosis condition is determined according to the scale layer thickness threshold;

[0056] The scale layer thickness satisfies the fouling diagnosis condition, and the fault diagnosis type is determined to include the fouling fault type.

[0057] According to some embodiments of the present application, the fouling diagnosis parameters include hot side mass flow rate and cold side mass flow rate, and before the fouling diagnosis parameters are substituted into a preset fouling diagnosis model for real-time calculation to obtain a fouling diagnosis calculation result, the fouling diagnosis model is further preset, specifically including:

[0058] A hot side medium thermal conductivity sequence, a cold side medium thermal conductivity sequence, a hot side medium viscosity sequence and a cold side medium viscosity sequence of the nuclear power plant heat exchanger are obtained;

[0059] Based on the health working condition data, a clean heat transfer coefficient of the nuclear power plant heat exchanger under a clean working condition is calculated;

[0060] The clean heat transfer coefficient is used to determine a corresponding clean total thermal resistance;

[0061] determine model hyperparameters according to the health working condition data, the thermal side medium thermal conductivity sequence, the cold side medium thermal conductivity sequence, the thermal side medium viscosity sequence, the cold side medium viscosity sequence, and the cleaning total thermal resistance;

[0062] According to the model hyperparameters, preset the fouling diagnosis model;

[0063] The fouling diagnosis parameter is substituted into the preset fouling diagnosis model for real-time calculation to obtain a fouling diagnosis calculation result, including:

[0064] The thermal side mass flow rate and the cold side mass flow rate are substituted into the fouling diagnosis model for real-time calculation to obtain a fouling thermal resistance value as the fouling diagnosis calculation result.

[0065] According to some embodiments of the present application, the method further comprises:

[0066] Performing multivariate state assessment on the health working condition data to obtain a multi-dimensional health memory matrix;

[0067] Performing multivariate state assessment on the actual working condition data to obtain a multi-dimensional actual working condition matrix;

[0068] Performing similarity comparison based on the multi-dimensional health memory matrix and the multi-dimensional actual working condition matrix to obtain a similarity comparison result;

[0069] According to the similarity comparison result, determining the heat exchange running state of the nuclear power plant heat exchanger.

[0070] In a second aspect, the embodiments of the present application provide an electronic device, including a memory and a processor, the memory stores a computer program, and the processor implements the nuclear power plant heat exchanger fault diagnosis method according to any one of the embodiments of the first aspect of the present application when executing the computer program.

[0071] In a third aspect, the embodiments of the present application provide a computer readable storage medium, the storage medium stores a program, and the program is executed by a processor to implement the nuclear power plant heat exchanger fault diagnosis method according to any one of the embodiments of the first aspect of the present application.

[0072] The nuclear power plant heat exchanger fault diagnosis method and device, electronic device, and medium according to the embodiments of the present application have at least the following beneficial effects:

[0073] According to the method for diagnosing the fault of the heat exchanger of the nuclear power plant provided in the embodiments of the present application, the actual working condition data of the heat exchanger of the nuclear power plant is obtained by detecting the heat exchanger of the nuclear power plant; the health working condition data matched with the heat exchanger of the nuclear power plant is obtained; the actual working condition data is analyzed for rough qualitative fault according to the health working condition data, and the heat exchange running state of the heat exchanger of the nuclear power plant is determined; and in response to the heat exchange running state being determined as a fault running state, the actual working condition data is analyzed for precise qualitative fault, and the fault diagnosis type matched with the heat exchange running state is determined. In this way, the diagnosis efficiency and accuracy can be further improved in the process of diagnosing the fault of the heat exchanger of the nuclear power plant.

[0074] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0075] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the appended drawings.

[0076] Fig. 1 is a flowchart of a method for diagnosing the fault of a heat exchanger of a nuclear power plant according to an embodiment of the present application;

[0077] Fig. 2 is another flowchart of a method for diagnosing the fault of a heat exchanger of a nuclear power plant according to an embodiment of the present application;

[0078] Fig. 3 is another flowchart of a method for diagnosing the fault of a heat exchanger of a nuclear power plant according to an embodiment of the present application;

[0079] Fig. 4 is another flowchart of a method for diagnosing the fault of a heat exchanger of a nuclear power plant according to an embodiment of the present application;

[0080] Fig. 5 is another flowchart of a method for diagnosing the fault of a heat exchanger of a nuclear power plant according to an embodiment of the present application;

[0081] Fig. 6 is another flowchart of a method for diagnosing the fault of a heat exchanger of a nuclear power plant according to an embodiment of the present application;

[0082] Fig. 7 is another flowchart of a method for diagnosing the fault of a heat exchanger of a nuclear power plant according to an embodiment of the present application;

[0083] Fig. 8 is another flowchart of a method for diagnosing the fault of a heat exchanger of a nuclear power plant according to an embodiment of the present application;

[0084] Fig. 9 is an example of a qualitative change trend library according to an embodiment of the present application;

[0085] Fig. 10 is a hardware structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0086] Embodiments of the present application are described below in detail with reference to the accompanying drawings, in which like or similar elements are denoted by the same or similar reference symbols throughout the drawings. The embodiments described below are examples in all aspects and merely for the purpose of illustrations, and should not be construed as limiting the scope of the present application.

[0087] In the description of the present application, the meaning of one or more is one or more, the meaning of multiple is two or more, greater than, less than, more than, etc. are understood as not including the number, above, below, within, etc. are understood as including the number. If it is described as first, second, it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or the sequence of indicated technical features.

[0088] In the description of the present application, it should be understood that the description of the position, such as up, down, left, right, front, back, etc. indicates the position or location relationship based on the position or location relationship shown in the drawings, which is only for the purpose of describing the present application and simplifying the description, and does not indicate or imply that the device or element indicated must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present application.

[0089] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "exemplary embodiment", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the exemplary description of the above terms does not necessarily mean the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0090] In the description of the present application, it should be noted that, unless otherwise explicitly limited, the words such as setting, installing, connecting, etc. should be broadly understood, and those skilled in the art can reasonably determine the specific meaning of the above words in the present application in combination with the specific content of the technical solution. In addition, the identification of the specific steps in the following does not represent the limitation of the order and execution logic of the steps, and the execution order and execution logic between the steps should be understood and inferred with reference to the content expressed in the embodiments.

[0091] The equipment cooling water heat exchanger of a nuclear power plant bears important operation and safety functions. It is used to export heat of equipment to meet the operation conditions of user equipment and ensure the safety of the nuclear power plant during normal operation or accidents of the nuclear power plant. It should be pointed out that as a passive device, the internal components of the heat exchanger may age, deform, corrode and scale, which may cause equipment failure, resulting in unplanned shutdown of the heat exchange system, causing huge safety problems and economic losses.

[0092] The equipment cooling water heat exchanger of a nuclear power plant has many users on the hot side, and the operation of the users changes with the working condition of the nuclear power plant. Therefore, the flow and inlet temperature of the heat exchanger on the hot side change with the working condition of the nuclear power plant. At the same time, the flow of the equipment cooling water heat exchanger of the nuclear power plant on the cold side changes with the sea level, and the sea water temperature on the cold side of the heat exchanger also changes with the season. In summary, the cold and hot side parameters of the equipment cooling water heat exchanger of the nuclear power plant change all the time during normal operation, and the operating conditions are complex. The common fault diagnosis method does not consider the influence of the change of the working condition, and is easy to diagnose the wrong result. In addition, the operation data of the equipment cooling water heat exchanger is sent to the distributed control system in real time, and the collection frequency reaches millisecond level. The amount of collected data is large, and the collected data has certain fluctuation due to the influence of instruments and transmission path.

[0093] Distributed Control System (DCS) is an advanced control system for industrial process control. It improves reliability and flexibility by distributing control functions to multiple control nodes instead of concentrating them in a central control room. In a distributed control system, each control node can independently perform specific control tasks while communicating and exchanging data with other nodes and the central monitoring system through a network.

[0094] In existing fault diagnosis techniques, there are some widely recognized methods, each of which has its advantages but also some shortcomings.

[0095] Knowledge-based methods, such as expert systems, mainly rely on expert experience and knowledge base for reasoning analysis. The advantage of this method is that it does not need to build a complex system analysis model, and the diagnosis result is usually easy to understand, with good robustness. However, it also has obvious shortcomings, such as the acquisition of expert knowledge is often difficult, and the accuracy of diagnosis is highly dependent on the completeness and richness of the knowledge base. In addition, when there are many reasoning rules involved, there may be matching conflicts, resulting in reduced reasoning efficiency.

[0096] The data-driven based method builds a model by analyzing historical data, and the modeling process of this method is relatively simple, has good universality and real-time performance. Machine learning algorithms are widely used in this kind of method, which can train the model using historical data and perform fault diagnosis on new input data. However, this method needs to rely on sample data after failure, and is very sensitive to data changes. Once the sample data deviates from the actual situation, it may affect the accuracy of diagnosis. At the same time, this kind of method is usually regarded as a "black box" model, and its calculation process lacks interpretability, which makes it difficult for operators to fully trust its diagnosis results.

[0097] The method based on mathematical analytical model analyzes the physical equipment by establishing a mathematical model. The advantage of this method is that it does not need fault sample data, and the physical meaning is clear and easy to explain. However, it needs to build a complex mathematical model, which may not be applicable to devices with complex structure or difficult to model. At the same time, the calculation amount of this kind of method is usually large, which may affect the efficiency of diagnosis.

[0098] In summary, although the existing fault diagnosis methods perform well in some aspects, they also have some limitations, such as dependence on expert knowledge, need for sample data, and complexity and interpretability of the calculation process. These shortcomings may lead to a decrease in diagnosis efficiency and accuracy in practical applications, so research and development of new fault diagnosis technology to overcome these limitations is still an important research direction.

[0099] The present application aims to at least solve one of the technical problems existing in the prior art. To this end, the present application provides a nuclear power plant heat exchanger fault diagnosis method and device, electronic equipment and medium, which can further improve the diagnosis efficiency and accuracy in the process of nuclear power plant heat exchanger fault diagnosis.

[0100] The following is further illustrated with reference to the accompanying drawings.

[0101] Referring to FIG. 1, the nuclear power plant heat exchanger fault diagnosis method provided by the embodiment of the present application can include:

[0102] Step S101, detecting the nuclear power plant heat exchanger to obtain actual working condition data;

[0103] Step S102, obtaining health working condition data matched with the nuclear power plant heat exchanger;

[0104] Step S103, performing fault rough qualitative analysis on the actual working condition data according to the health working condition data to determine the heat exchange running state of the nuclear power plant heat exchanger;

[0105] Step S104, in response to the heat exchange operation state being determined as a fault operation state, performing fault fine qualitative analysis based on the actual working condition data to determine a fault diagnosis type matching the heat exchange operation state.

[0106] According to the nuclear power plant heat exchanger fault diagnosis method provided by the embodiments of the present application, the actual working condition data is obtained by detecting the nuclear power plant heat exchanger; the health working condition data matching the nuclear power plant heat exchanger is obtained; the heat exchange operation state of the nuclear power plant heat exchanger is determined by performing fault coarse qualitative analysis on the actual working condition data according to the health working condition data; and in response to the heat exchange operation state being determined as a fault operation state, the fault fine qualitative analysis is performed based on the actual working condition data to determine a fault diagnosis type matching the heat exchange operation state. In this way, the diagnosis efficiency and accuracy can be further improved in the process of diagnosing the fault of the nuclear power plant heat exchanger.

[0107] Step S101 of some embodiments, detecting the nuclear power plant heat exchanger to obtain actual working condition data.

[0108] It should be noted that step S101 is the starting point of the nuclear power plant heat exchanger fault diagnosis process, which involves comprehensive detection of the heat exchanger to collect actual working condition data. This step is crucial because it provides basic information for subsequent fault analysis and diagnosis. The detection process usually includes real-time detection of multiple key parameters of the heat exchanger, which can include but are not limited to inlet and outlet temperatures, pressures, flow rates of the heat exchange fluid, and vibrations and sounds of the heat exchanger. In order to obtain these data, various sensors such as temperature sensors, pressure sensors and flow meters can be installed at key positions of the heat exchanger. These sensors can accurately measure and record the physical state of the heat exchanger during operation, thereby collecting real-time data.

[0109] Step S102 of some embodiments, obtaining health working condition data matching the nuclear power plant heat exchanger.

[0110] It should be noted that the health working condition data refers to the working condition parameters of the nuclear power plant heat exchanger in an ideal or fault-free state, including inlet and outlet temperatures, pressures, flow rates of the heat exchange fluid, etc.

[0111] In some more specific embodiments, the health working condition data can come from multiple sources, for example:

[0112] Design parameters: The design documents of the heat exchanger usually provide the parameter range under the expected working condition, which can be used as a reference for health working condition.

[0113] Historical data: Extract data from the past operation records of the heat exchanger, especially the data collected during the period when the device is known to be running well.

[0114] Similar equipment data: data from other heat exchangers of the same model or under similar operating conditions as reference.

[0115] Experimental data: data obtained through laboratory tests or simulated experiments, which are generated in a controlled environment and can provide performance indicators of heat exchangers under ideal conditions.

[0116] It should be understood that health condition data is the benchmark for the diagnostic process, used for comparison with actual condition data to identify and assess whether there is a deviation or failure in the operation of the heat exchanger. Some embodiments can perform detailed analysis and processing of these health condition data after obtaining them to ensure that they accurately reflect the health status of the heat exchanger. This can include cleaning, standardizing and parameterizing the data to facilitate effective comparison of health condition data with actual condition data.

[0117] Step S103 of some embodiments, according to the health condition data, performs a rough qualitative analysis of the actual condition data to determine the heat exchange operating status of the heat exchanger in the nuclear power plant.

[0118] In some embodiments, step S103 is a key link in the fault diagnosis process of the heat exchanger in the nuclear power plant, which involves using pre-acquired health condition data to perform a rough qualitative analysis of the actual condition data. The purpose of this step is to quickly assess whether the heat exchanger is in normal operation or whether there is some kind of failure.

[0119] It should be noted that the rough qualitative analysis of the fault is usually based on a comparative analysis method. In this process, the real-time detected actual condition data of the heat exchanger (such as inlet and outlet temperature, pressure, flow rate, etc.) is first compared with the health condition data. The health condition data represents the ideal operating parameters of the heat exchanger under no-fault conditions, so actual condition data that deviates significantly from these health condition data may indicate a problem with the heat exchanger in the nuclear power plant. By analyzing these deviations, the operating status of the heat exchanger can be initially determined. For example, if the actual temperature or pressure data consistently exceeds the normal range defined by the health condition data, it may indicate that the heat exchanger has a blockage, fouling or other heat transfer efficiency problem. Conversely, if the actual condition data is consistent with the health condition data, then the heat exchanger can be considered to be currently operating normally.

[0120] In addition, the rough qualitative analysis of the fault can also include trend analysis, i.e. observing whether the trend of the operating parameters over time is consistent with the historical health trend. Sudden or unusual trend changes can be a precursor to a fault.

[0121] It should be understood that the rough qualitative analysis of faults provides a preliminary assessment of the heat exchange operating state of the heat exchanger, which is crucial for deciding whether further detailed analysis is needed or immediate maintenance measures should be taken. If the rough qualitative analysis indicates that there may be a fault in the heat exchanger, the next step will be to conduct a more in-depth qualitative analysis to determine the specific type and cause of the fault. This hierarchical analysis method not only improves the efficiency of the diagnostic process, but also helps to quickly respond to potential operational problems, thereby ensuring the stable and safe operation of the nuclear power plant.

[0122] According to some embodiments of the present application, the rough qualitative analysis of faults based on the health condition data to determine the heat exchange operating state of the heat exchanger of the nuclear power plant comprises:

[0123] Performing multivariate state assessment on the health condition data to obtain a multi-dimensional health memory matrix;

[0124] Performing multivariate state assessment on the actual condition data to obtain a multi-dimensional actual condition matrix;

[0125] Based on the multi-dimensional health memory matrix and the multi-dimensional actual condition matrix, similarity comparison is performed to obtain a similarity comparison result;

[0126] According to the similarity comparison result, the heat exchange operating state of the heat exchanger of the nuclear power plant is determined.

[0127] In some embodiments of the present application, the determination of the heat exchange operating state of the heat exchanger of the nuclear power plant is achieved through multivariate state assessment, which involves multidimensional evaluation and comparison of health condition data and actual condition data. First, a multi-dimensional health memory matrix is constructed by performing multivariate state assessment on the health condition data. This multi-dimensional health memory matrix is a key tool that stores the multi-aspect performance parameters of the heat exchanger in normal or healthy state, which may include temperature, pressure, flow rate, etc., which are considered as normal operation characteristics in historical data.

[0128] Then, a similar method is applied to the actual condition data, and a multi-dimensional actual condition matrix is obtained by multivariate state assessment. This multi-dimensional actual condition matrix reflects the current operating state of the heat exchanger in real time, providing a benchmark for comparison with the multi-dimensional health memory matrix.

[0129] A key step in the analysis process is similarity comparison, which involves comparing the multidimensional health memory matrix with the multidimensional actual operating condition matrix. This step assesses whether the current operating state of the heat exchanger deviates from a healthy baseline state by calculating the similarity or difference between the two matrices. The similarity comparison results can reveal the degree of deviation between actual operating conditions and healthy operating conditions, thus providing a basis for fault diagnosis.

[0130] Finally, based on the similarity comparison results, the heat exchange operation status of the nuclear power plant's heat exchangers can be determined. If the similarity comparison results show that the actual operating conditions are highly consistent with the healthy operating conditions, then the heat exchangers can be considered to be operating normally. Conversely, if there are significant differences, it indicates that the heat exchangers may have some problems and require further inspection and maintenance.

[0131] This multi-dimensional analysis and comparison allows for a more accurate assessment of the heat exchanger's operating status, enabling timely detection and prevention of potential faults, thereby improving the operational efficiency and safety of nuclear power plants. This approach not only enhances the accuracy of fault diagnosis but also facilitates predictive maintenance, reduces unexpected downtime, and optimizes maintenance costs.

[0132] In some embodiments, step S104 involves performing a fault qualitative analysis based on actual operating data in response to the heat exchange operating state being determined to be a fault operating state, and determining a fault diagnosis type that matches the heat exchange operating state.

[0133] In some embodiments, step S104 is a key follow-up step in the nuclear power plant heat exchanger fault diagnosis process, specifically for heat exchangers that are identified as potentially faulty in the coarse qualitative analysis. Once the operating state of the heat exchanger is determined to be a faulty operating state, the purpose of step S104 is to perform a more refined qualitative analysis of the fault to accurately determine the type of fault.

[0134] This step begins with a thorough qualitative analysis of the fault based on actual operating data. This stage may involve a more detailed examination and evaluation of the heat exchanger's operating parameters, such as temperature, pressure, and flow rate. The qualitative fault analysis process may involve complex data processing and pattern recognition techniques to identify specific patterns or trends associated with particular fault types. For example, an abnormally large increase in the inlet and outlet temperature difference of the heat exchanger may indicate scaling or blockage; an abnormally small decrease in the temperature difference may indicate a reduction in the heat exchanger's heat transfer area or a decrease in heat transfer efficiency.

[0135] It should be understood that the fault fine qualitative analysis not only depends on the real-time working condition data, but also can be combined with historical data and experience knowledge to improve the accuracy of diagnosis. By comparing the actual working condition data with the historical data, the pattern and rule of fault occurrence can be found. In addition, by using an expert system or a machine learning algorithm, the most possible fault type can be determined according to the existing fault case library.

[0136] Referring to FIG. 2, according to some embodiments of the present application, step S104, in response to the heat exchange operating state being determined as a fault operating state, performs fault fine qualitative analysis based on the actual working condition data, determines a fault diagnosis type matched with the heat exchange operating state, including:

[0137] Step S201, extracting a leakage diagnosis parameter from the actual working condition data;

[0138] Step S202, substituting the leakage diagnosis parameter into a preset leakage diagnosis model for real-time calculation to obtain a leakage diagnosis calculation result;

[0139] Step S203, in response to the heat exchange operating state being determined as a fault operating state, determining a fault detection time of the nuclear power plant heat exchanger;

[0140] Step S204, in response to the leakage diagnosis calculation result corresponding to the fault detection time meeting a leakage diagnosis condition, determining that the fault diagnosis type includes a leakage fault type.

[0141] Step S201 of some embodiments involves extracting a leakage diagnosis parameter from the actual working condition data. These leakage diagnosis parameters are key indicators for evaluating whether the heat exchanger has a leakage, and can include the inlet and outlet flow, pressure, temperature, etc. of the heat exchange fluid. By real-time detection of these leakage diagnosis parameters, the required data for evaluating whether the heat exchanger has a leakage working condition can be collected.

[0142] Step S202 of some embodiments uses these leakage diagnosis parameters to substitute them into a preset leakage diagnosis model for real-time calculation. It should be noted that the leakage diagnosis model is a mathematical model established based on physical laws and empirical data, which is used to predict and evaluate the possibility and severity of leakage. By substituting the actual working condition data into the model, a leakage diagnosis calculation result can be obtained, which reflects the leakage condition of the heat exchanger under the current operating state.

[0143] Step S203 of some embodiments determines a fault detection time after the operating state of the heat exchanger is determined as a fault operating state. This fault detection time refers to the specific time point when the fault is determined to exist during the operation of the heat exchanger.

[0144] In step S204 of some embodiments, the leakage diagnosis calculation result corresponding to the fault detection time is evaluated to determine whether the leakage diagnosis condition is met. If the leakage diagnosis calculation result indicates that there is leakage and the preset leakage diagnosis condition is met, the fault diagnosis type includes the leakage fault type.

[0145] In some embodiments, the severity of the leakage and the possible leakage location can be further analyzed to take appropriate maintenance and repair measures.

[0146] Therefore, step S104 extracts key parameters from actual operating data through a series of refined analysis steps, applies a leakage diagnosis model, determines the fault detection time, and finally determines the fault type. This process not only improves the accuracy of fault diagnosis, but also helps to quickly respond and handle leakage faults, ensuring the safe and reliable operation of the heat exchanger in the nuclear power plant.

[0147] Referring to FIG. 3, according to some embodiments of the present application, the leakage diagnosis parameters can include the hot side mass flow rate, the cold side mass flow rate, the hot side inlet and outlet enthalpy values, and the cold side inlet and outlet enthalpy values. Step S202 substitutes the leakage diagnosis parameters into the preset leakage diagnosis model for real-time calculation to obtain the leakage diagnosis calculation result, which can include:

[0148] In step S301, the hot side mass flow rate, the cold side mass flow rate, the hot side inlet and outlet enthalpy values, and the cold side inlet and outlet enthalpy values are substituted into the corresponding leakage diagnosis model for real-time calculation to obtain the leakage factor as the leakage diagnosis calculation result.

[0149] In step S204, in response to the leakage diagnosis calculation result corresponding to the fault detection time meeting the leakage diagnosis condition, it is determined that the fault diagnosis type includes the leakage fault type, which can include:

[0150] In step S302, the corresponding fluctuation variance value is calculated according to the leakage factor before the fault detection time.

[0151] In step S303, the leakage diagnosis condition is determined based on the fluctuation variance value.

[0152] In step S304, in response to the leakage factor corresponding to the fault detection time meeting the leakage diagnosis condition, it is determined that the fault diagnosis type includes the leakage fault type.

[0153] In some embodiments of the present application, the selection and application of leakage diagnosis parameters are to accurately diagnose the leakage fault of the heat exchanger in the nuclear power plant. The leakage diagnosis parameters can include the mass flow rate of the hot side medium, the mass flow rate of the cold side medium, and the inlet and outlet enthalpy values of the hot side and the cold side. These parameters comprehensively reflect the flow characteristics and heat energy transfer of the fluid in the heat exchanger, and are key indicators for evaluating whether the heat exchanger has leakage.

[0154] In step S301 of some embodiments, the leakage diagnosis parameters are substituted into a preset leakage diagnosis model for real-time calculation to obtain a leakage factor as the leakage diagnosis calculation result. In this embodiment, the leakage diagnosis model is a mathematical model established based on physical and thermodynamic principles, which can calculate the leakage factor according to the input parameters. The leakage factor is a dimensionless parameter representing the severity of leakage. By comparing the current leakage factor with the leakage factor during normal operation, the leakage condition of the heat exchanger can be evaluated. If the value of the leakage factor deviates significantly from the normal range, it may indicate that there is a leakage.

[0155] In step S302 of some embodiments, in order to determine the confirmed diagnosis condition of leakage, the fluctuation variance value of the leakage factor before the fault detection time is calculated. The fluctuation variance value reflects the stability of the leakage factor over time. Under normal operating conditions, the fluctuation variance value of the leakage factor is usually small, while when leakage occurs, the fluctuation variance value of the leakage factor increases.

[0156] In step S303 of some embodiments, the confirmed diagnosis condition of leakage is determined based on the fluctuation variance value. The confirmed diagnosis condition of leakage can be that the fluctuation variance value of the leakage factor exceeds a certain preset threshold, which indicates that the fluctuation of the leakage factor exceeds the normal fluctuation range, thereby confirming the leakage fault.

[0157] In step S304 of some embodiments, if the leakage factor corresponding to the fault detection time meets the confirmed diagnosis condition of leakage, i.e., the fluctuation variance value of the leakage factor exceeds the preset threshold, then the fault diagnosis type will be determined as the leakage fault type. This diagnosis result can be used to guide subsequent maintenance and repair work to solve the leakage problem and restore the normal operation of the heat exchanger.

[0158] Based on this, by selecting the leakage diagnosis parameters and the preset leakage diagnosis model, the embodiments of the present application can realize accurate diagnosis of the leakage fault of the heat exchanger of the nuclear power plant. This method not only improves the accuracy of fault diagnosis, but also helps to discover and handle the leakage problem in a timely manner, thereby ensuring the safe and reliable operation of the nuclear power plant.

[0159] In some more specific embodiments, the leakage diagnosis parameters can include the hot-side mass flow rate, the cold-side mass flow rate, the hot-side inlet and outlet enthalpy values, and the cold-side inlet and outlet enthalpy values, wherein:

[0160] q l represents the change in fluid mass flow rate due to leakage;

[0161] q hi represents the mass flow rate of the hot-side fluid;

[0162] q ci represents the mass flow rate of the cold-side fluid;

[0163] H1 and H2: represent the enthalpy values of the hot-side fluid inlet and outlet, respectively;

[0164] h1 and h2: represent the enthalpy values of the cold-side fluid inlet and outlet, respectively.

[0165] Therefore, a leakage diagnosis model for calculating the leakage factor Δ can be defined as follows:

[0166] In this way, the leakage diagnosis model can be used to calculate the leakage factor in real time.

[0167] Referring to FIG. 4, according to some embodiments of the present application, the leakage diagnosis parameters include the cold medium volume flow rate, the cold medium constant-pressure specific heat, the medium density, the hot-side inlet temperature, the hot-side outlet temperature, the cold-side inlet temperature, and the cold-side outlet temperature. In step S202, the leakage diagnosis parameters are substituted into a pre-set leakage diagnosis model for real-time calculation to obtain a leakage diagnosis calculation result, which can include:

[0168] In step S401, the cold medium volume flow rate, the cold medium constant-pressure specific heat, the medium density, the hot-side inlet temperature, the hot-side outlet temperature, the cold-side inlet temperature, and the cold-side outlet temperature are substituted into the corresponding leakage diagnosis model for real-time calculation to obtain a leakage calculation amount as the leakage diagnosis calculation result.

[0169] In step S204, in response to the leakage diagnosis calculation result corresponding to the fault detection time meeting the leakage diagnosis condition, it is determined that the fault diagnosis type includes the leakage fault type, which can include:

[0170] In step S402, a leakage diagnosis threshold is obtained, and the leakage diagnosis condition is determined according to the leakage diagnosis threshold.

[0171] In step S403, in response to the leakage calculation amount meeting the leakage diagnosis condition, it is determined that the fault diagnosis type includes the leakage fault type.

[0172] In some embodiments of the present application, the leakage diagnosis process is realized by comprehensively analyzing a series of leakage diagnosis parameters. These parameters can include the volume flow rate, the constant-pressure specific heat, the medium density of the cold medium, and the inlet and outlet temperatures of the hot side and the cold side.

[0173] In step S401 of some embodiments, the leakage diagnosis parameters are substituted into a pre-set leakage diagnosis model for real-time calculation. In this embodiment, the leakage diagnosis model is a mathematical model established based on physical and thermodynamic principles, which can calculate the leakage calculation amount according to the input parameters. The leakage calculation amount is a quantitative index for measuring the severity of leakage, which can include the change of mass flow rate, the estimation of energy loss, or other physical quantities related to leakage.

[0174] In step S402 of some embodiments, a leakage diagnosis threshold is obtained for determining the confirmed condition of leakage. The leakage diagnosis threshold is pre-set according to the design parameters of the heat exchanger, operation experience and historical data, which defines an acceptable range of the calculated leakage amount. When the calculated leakage amount exceeds this threshold, it indicates that the heat exchanger may have a leakage.

[0175] In step S403 of some embodiments, the confirmed condition of leakage is determined based on the calculated leakage amount and the leakage diagnosis threshold. If the calculated leakage amount at the fault detection time exceeds the leakage diagnosis threshold, i.e., the confirmed condition of leakage is met, the fault diagnosis type will be determined as the leakage fault type. This diagnosis result will guide the subsequent maintenance and repair work to solve the leakage problem and restore the normal operation of the heat exchanger.

[0176] In some more specific embodiments, the leakage diagnosis parameters can include the cold medium volume flow rate, the cold medium constant-pressure specific heat, the medium density, the hot side inlet temperature, the hot side outlet temperature, the cold side inlet temperature and the cold side outlet temperature, wherein:

[0177] V c is the cold medium volume flow rate;

[0178] c is the cold medium constant-pressure specific heat;

[0179] p is the fluid density;

[0180] t hi is the hot side inlet temperature;

[0181] t ho is the hot side outlet temperature;

[0182] t ci is the cold side inlet temperature;

[0183] t co is the cold side outlet temperature;

[0184] According to some embodiments of the present application, a first leakage diagnosis element is constructed according to the cold side outlet temperature and the cold side inlet temperature. According to some embodiments of the present application, a first leakage diagnosis element is constructed according to the cold side outlet temperature and the cold side inlet temperature, including:

[0185] The cold side outlet temperature and the cold side inlet temperature are subtracted to obtain the first leakage diagnosis element, i.e., (△t co -△t ci );

[0186] The cold medium volume flow rate is determined as the second leakage diagnosis element, i.e., V c ;

[0187] The hot side outlet temperature is determined as the third leakage diagnosis element, i.e., tho ;

[0188] According to the first leakage diagnosis element (△t co -△t ci ), the second leakage diagnosis element V c and the third leakage diagnosis element t ho , real-time calculation is performed in the leakage diagnosis model to obtain a leakage calculation amount as a leakage diagnosis calculation result. Specifically, it can include:

[0189] The product of the first leakage diagnosis element (△t co -△t ci ) and the second leakage diagnosis element V c is determined as a model numerator, that is, V c (△t co -△t ci );

[0190] According to the first leakage diagnosis element and the third leakage diagnosis element, a model denominator is determined; wherein, according to some embodiments of the present application, according to the first leakage diagnosis element and the third leakage diagnosis element, the model denominator can be determined, which can include:

[0191] The third leakage diagnosis element is determined as a subtracted element t ho of the model denominator;

[0192] Half of the first leakage diagnosis element is determined as a subtracted element

[0193] Difference is performed according to the subtracted element and the subtracted element to obtain the model denominator, that is,

[0194] Real-time calculation is performed on the model numerator and the model denominator to obtain the leakage calculation amount as the leakage diagnosis calculation result. Therefore, the leakage diagnosis model for calculating the leakage calculation amount x can be defined as:

[0195] In this way, the leakage diagnosis model can be used to determine the fault diagnosis type including the leakage fault type.

[0196] In some more specific embodiments, the leakage diagnosis calculation result can also be determined by other ways. For example, a combined model is established using the heat transfer efficiency ε and the leakage factor Δ to diagnose the leakage fault of the heat exchanger:

[0197] The variables and Wherein, ε1 represents the heat transfer efficiency of the hot side fluid corresponding to the current detection time, ε0 represents the reference heat transfer efficiency of the hot side fluid, Δ1 represents the leakage factor of the hot side fluid corresponding to the current detection time, Δ0 represents the reference leakage factor of the hot side fluid, and σ represents the standard deviation corresponding to the calculation of the heat transfer efficiency.

[0198] According to the weight size β1 and β2 of the two parameters in judging the performance of the heat exchanger, a failure coefficient λ defined as the heat exchanger failure coefficient is calculated:

[0199] λ = α1β1 + α2β2

[0200] The stable value range of the failure coefficient λ before leakage is extracted from the actual working condition data, and the failure coefficient λ value is calculated after the leakage data as the leakage diagnosis parameter. According to the change of the failure coefficient λ, the leakage of the heat exchanger and the corresponding leakage degree can be diagnosed.

[0201] Based on the embodiments of the present application shown in steps S401 to S403, the leakage diagnosis parameter can be calculated in real time and compared with the leakage diagnosis threshold, and the embodiments of the present application can realize accurate diagnosis of the leakage failure of the heat exchanger of the nuclear power plant. This method not only improves the accuracy of fault diagnosis, but also helps to find and handle leakage problems in time, so as to ensure the safe and reliable operation of the nuclear power plant. Through this fine diagnosis process, false positives and false negatives can be effectively reduced, and the maintenance efficiency of the heat exchanger can be improved.

[0202] Referring to FIG. 5, according to some embodiments of the present application, step S104, in response to the heat exchange operating state being determined as a failure operating state, performs failure fine qualitative analysis based on actual working condition data, and determines the failure diagnosis type matched to the heat exchange operating state, which can include:

[0203] Step S501, extracting the fouling diagnosis parameter from the actual working condition data;

[0204] Step S502, substituting the fouling diagnosis parameter into the preset fouling diagnosis model to perform real-time calculation, and obtaining the fouling diagnosis calculation result;

[0205] Step S503, in response to the heat exchange operating state being determined as a failure operating state, determining the failure detection time of the heat exchanger of the nuclear power plant;

[0206] Step S504, in response to the fouling diagnosis calculation result corresponding to the failure detection time meeting the fouling diagnosis condition, determining that the failure diagnosis type includes the fouling failure type.

[0207] Step S501 of some embodiments extracts fouling diagnosis parameters from real-time detected actual operating condition data. These fouling diagnosis parameters can include inlet and outlet temperatures, pressures, flow rates, pH values, conductivities, or other chemical and physical properties of the fluid that are related to fouling formation. These data provide necessary input information for subsequent fouling analysis.

[0208] Step S502 of some embodiments substitutes the extracted fouling diagnosis parameters into a preset fouling diagnosis model for real-time calculation. This fouling diagnosis model can be developed based on physical and chemical principles, historical data, empirical formulas, or machine learning algorithms to predict and evaluate the likelihood and extent of fouling conditions. Through this fouling diagnosis model, fouling diagnosis calculation results can be calculated, which reflect the fouling conditions of the heat exchanger under the current operating conditions.

[0209] Step S503 of some embodiments involves determining the fault detection time of the nuclear power plant heat exchanger, i.e., determining the specific time point when the fault exists during the operation of the heat exchanger, i.e., the fault detection time. This fault detection time is crucial for timely response and handling of the fault, as it marks the specific time when the fault occurs and is identified by the system.

[0210] Step S504 of some embodiments evaluates whether the data meet the preset fouling diagnosis conditions according to the fouling diagnosis calculation results corresponding to the fault detection time. These fouling diagnosis conditions can include threshold values that exceed the normal range of fouling diagnosis calculation results or show similar characteristics to historical fouling events. If the fouling diagnosis calculation results meet these fouling diagnosis conditions, it means that the fault diagnosis type includes the fouling fault type.

[0211] Through steps S501 to S504, the embodiments of the present application can accurately diagnose the fouling fault of the heat exchanger, thereby helping to take timely measures to clean or prevent fouling and ensuring the efficient and stable operation of the heat exchanger. This refined fault diagnosis method not only improves the efficiency of fault handling, but also helps to reduce the performance degradation and potential safety risks caused by fouling.

[0212] Referring to FIG. 6, according to some embodiments of the present application, the fouling diagnosis calculation results include the actual heat transfer efficiency, and step S502 substitutes the fouling diagnosis parameters into the preset fouling diagnosis model for real-time calculation to obtain the fouling diagnosis calculation results, which can include:

[0213] Step S601 obtains the clean heat transfer efficiency and reference heat transfer efficiency of the heat exchanger of the nuclear power plant.

[0214] Step S602, the clean heat transfer efficiency, the reference heat transfer efficiency and the actual heat transfer efficiency are substituted into the corresponding fouling diagnosis model for real-time calculation, and the fouling factor as the fouling diagnosis calculation result is obtained;

[0215] In step S504, in response to the fouling diagnosis calculation result corresponding to the fault detection time meeting the fouling diagnosis condition, it is determined that the fault diagnosis type includes the fouling fault type, which can include:

[0216] Step S603, the fouling diagnosis threshold is obtained, and the fouling diagnosis threshold is used to determine the fouling diagnosis condition;

[0217] Step S604, in response to the fouling factor meeting the fouling diagnosis condition, it is determined that the fault diagnosis type includes the fouling fault type.

[0218] In some embodiments of the present application, the fouling diagnosis is a key process that identifies whether there is fouling phenomenon by analyzing the heat transfer efficiency of the heat exchanger. The diagnosis calculation result includes the actual heat transfer efficiency, which is an important indicator to measure the performance of the heat exchanger.

[0219] Step S601 of some embodiments, the clean heat transfer efficiency and the reference heat transfer efficiency of the heat exchanger of the nuclear power plant need to be obtained. The clean heat transfer efficiency refers to the heat transfer efficiency of the heat exchanger under the influence of no fouling, which is usually obtained based on design parameters or test results under known non-fouling conditions. The reference heat transfer efficiency is set based on historical data or operation data of similar equipment, representing the heat transfer efficiency that the heat exchanger should achieve under certain conditions.

[0220] Step S602 of some embodiments, the obtained clean heat transfer efficiency, reference heat transfer efficiency and actual heat transfer efficiency monitored in real time are substituted into the fouling diagnosis model for real-time calculation, and the fouling factor as the fouling diagnosis calculation result is obtained. This fouling diagnosis model can use physical or empirical formula to evaluate the influence of fouling on heat transfer efficiency. Through this calculation, the fouling factor as the fouling diagnosis calculation result can be obtained, which is a quantitative indicator representing the degree of fouling of the heat exchanger.

[0221] Step S603 of some embodiments, in order to diagnose the fouling fault, the fouling diagnosis threshold needs to be obtained. This fouling diagnosis threshold can be set based on experience, historical fault data or expert knowledge, to judge whether the fouling factor exceeds the normal range. It should be understood that the fouling diagnosis condition can include that the fouling factor exceeds a certain threshold, which indicates that the heat transfer efficiency of the heat exchanger has been significantly affected.

[0222] In step S604 of some embodiments, in response to the fouling factor satisfying the fouling diagnosis condition, it is determined that the fault diagnosis type includes the fouling fault type. In this case, if the fouling factor exceeds the preset fouling diagnosis threshold, it can be determined that the fault diagnosis type includes the fouling fault type. This means that the heat transfer efficiency of the heat exchanger is reduced due to fouling, and corresponding cleaning or maintenance measures need to be taken.

[0223] In some more specific embodiments, the fouling diagnosis calculation result includes the actual heat transfer efficiency, and in the process of substituting the fouling diagnosis parameter into the preset fouling diagnosis model for real-time calculation, the clean heat transfer efficiency and the reference heat transfer efficiency of the heat exchanger of the nuclear power plant need to be obtained, wherein:

[0224] ε clean ε is the heat transfer efficiency when the heat transfer surface is completely clean, i.e., the clean heat transfer efficiency;

[0225] ε dirt ε is the heat transfer efficiency that the heat exchanger should achieve under certain conditions, i.e., the reference heat transfer efficiency;

[0226] ε is the actual heat transfer efficiency.

[0227] Therefore, the fouling diagnosis model for calculating the fouling factor f can be defined as:

[0228] In this way, the fouling diagnosis model can be used to calculate the fouling factor in real time.

[0229] Through the steps S601 to S604 of the embodiments of the present application, by calculating and analyzing the heat transfer efficiency in real time and comparing it with the preset fouling diagnosis threshold, the embodiments of the present application can accurately diagnose the fouling fault of the heat exchanger. This method not only improves the accuracy of fault diagnosis, but also helps to take timely measures to reduce the performance decline and potential safety risks caused by fouling, and ensures the stable and efficient operation of the heat exchanger of the nuclear power plant.

[0230] Referring to FIG. 7, according to some embodiments of the present application, the fouling diagnosis parameter includes the medium flow rate and the flow pressure loss, and step S502 substitutes the fouling diagnosis parameter into the preset fouling diagnosis model for real-time calculation to obtain the fouling diagnosis calculation result, which can include:

[0231] In step S701, the medium flow rate and the flow pressure loss are substituted into the corresponding fouling diagnosis model for real-time calculation to obtain the scale layer thickness as the fouling diagnosis calculation result.

[0232] In step S504, in response to the fouling diagnosis calculation result corresponding to the fault detection time satisfying the fouling diagnosis condition, it is determined that the fault diagnosis type includes the fouling fault type, which can include:

[0233] Step S702, obtain a scale layer thickness threshold, and determine a fouling diagnosis condition according to the scale layer thickness threshold;

[0234] Step S703, in response to the scale layer thickness satisfying the fouling diagnosis condition, determine that the fault diagnosis type includes a fouling fault type.

[0235] In some embodiments of the present application, fouling diagnosis is a key step for evaluating and determining whether the heat exchanger has a fouling problem. This process involves specific fouling diagnosis parameters, including medium flow rate and flow pressure loss, which are important indicators for analyzing the internal fouling of the heat exchanger.

[0236] Step S701 of some embodiments substitutes the medium flow rate and flow pressure loss into a predetermined fouling diagnosis model for real-time calculation. This fouling diagnosis model can be based on the principles of fluid mechanics and heat transfer, and estimates the scale layer thickness by analyzing the changes in flow rate and pressure drop. The scale layer thickness is a direct indicator of the degree of fouling, reflecting the amount of accumulated dirt on the heat transfer surface of the heat exchanger. Through this calculation, the scale layer thickness as the result of fouling diagnosis calculation can be obtained, providing an important basis for subsequent fault diagnosis.

[0237] Step S702 of some embodiments, in order to diagnose fouling faults, needs to obtain a scale layer thickness threshold. This scale layer thickness threshold can be set based on experience, historical data or expert knowledge, to judge whether the scale layer thickness exceeds the normal range. The fouling diagnosis condition may include that the scale layer thickness exceeds a specific scale layer thickness threshold, indicating that the heat transfer efficiency of the heat exchanger has been significantly affected, and needs to be cleaned or maintained.

[0238] Step S703 of some embodiments, in response to the scale layer thickness satisfying the fouling diagnosis condition, determines that the fault diagnosis type includes a fouling fault type. If the scale layer thickness exceeds the preset scale layer thickness threshold, it can be determined that the fault diagnosis type includes the fouling fault type. This means that the performance decline of the heat exchanger is mainly caused by fouling, and appropriate measures need to be taken to remove the dirt and restore the normal operation of the heat exchanger.

[0239] In some more specific practical examples, the fouling diagnosis parameters include medium flow rate and flow pressure loss. In order to quantitatively analyze the influence of fouling, a functional relationship between the scale layer thickness and the flow pressure loss and the medium flow rate can be established.

[0240] It should be noted that the formation of scale layer increases the additional thermal resistance on the heat transfer surface of the heat exchanger, which not only reduces the heat transfer efficiency, but also may cause the increase of the flow pressure drop of the fluid. The flow pressure loss refers to the pressure drop of the fluid caused by friction and local resistance when passing through the heat exchanger. This pressure drop is related to the flow rate of the fluid, the geometry of the pipeline and the thickness of the scale layer on the heat transfer surface. Further, by substituting the monitored flow pressure loss and medium flow rate into the established functional relationship, the thickness of the scale layer is calculated.

[0241] Through this method, the scaling condition of the heat exchanger can be effectively monitored and controlled, the energy consumption can be reduced, the service life of the equipment can be prolonged, and the efficiency and reliability of the heat exchange process can be ensured.

[0242] Through the steps S701 to S703 shown in the embodiments of the present application, by calculating the medium flow rate and the flow pressure loss in real time and substituting them into the scaling diagnosis model, the embodiments of the present application can accurately diagnose the scaling failure of the heat exchanger. This method not only improves the accuracy of fault diagnosis, but also helps to take timely measures to reduce the performance degradation and potential safety risks caused by scaling, and ensures the stable and efficient operation of the heat exchanger. Through this refined diagnosis process, the performance of the heat exchanger can be effectively managed and optimized, and the service life of the equipment can be prolonged.

[0243] Referring to FIG. 8, according to some embodiments of the present application, the scaling diagnosis parameters include the hot side mass flow rate and the cold side mass flow rate, and before the scaling diagnosis parameters are substituted into the preset scaling diagnosis model for real-time calculation to obtain the scaling diagnosis calculation result, the scaling diagnosis model is further preset, specifically including:

[0244] Step S801, obtaining a sequence of thermal conductivity of hot side medium of the heat exchanger of the nuclear power plant, a sequence of thermal conductivity of cold side medium, a sequence of viscosity of hot side medium, and a sequence of viscosity of cold side medium;

[0245] Step S802, calculating a clean heat transfer coefficient of the heat exchanger of the nuclear power plant under a clean condition based on the health condition data;

[0246] Step S803, determining a corresponding clean total thermal resistance according to the clean heat transfer coefficient;

[0247] Step S804, determining model hyperparameters according to the health condition data, the sequence of thermal conductivity of hot side medium, the sequence of thermal conductivity of cold side medium, the sequence of viscosity of hot side medium, the sequence of viscosity of cold side medium, and the clean total thermal resistance;

[0248] Step S805, presetting the scaling diagnosis model according to the model hyperparameters;

[0249] In step S502, the fouling diagnosis parameter is substituted into the pre-set fouling diagnosis model for real-time calculation, obtaining a fouling diagnosis calculation result, which can include:

[0250] In step S806, the hot-side mass flow rate and the cold-side mass flow rate are substituted into the fouling diagnosis model for real-time calculation, obtaining a fouling thermal resistance value as the fouling diagnosis calculation result.

[0251] In some embodiments of the present application, the fouling diagnosis is a comprehensive process that not only includes real-time calculation of the fouling diagnosis parameter, but also involves meticulous pre-setting of the fouling diagnosis model. The purpose of this process is to ensure that the diagnosis model can accurately reflect the fouling condition of the heat exchanger under actual operating conditions.

[0252] In step S801 of some embodiments, the thermal conductivity sequence and the viscosity sequence of the hot-side medium and the cold-side medium of the nuclear power plant heat exchanger need to be obtained. These parameters are key indicators of heat transfer and fluid flow characteristics of the heat exchanger, and they change with temperature, pressure and medium composition, which are crucial for fouling diagnosis.

[0253] Hot-side medium thermal conductivity sequence: refers to a series of values of the thermal conductivity of the medium on the hot side of the heat exchanger (usually high-temperature fluid such as heated steam or high-temperature water) under clean operating conditions. Thermal conductivity is a physical quantity that measures the ability of a medium to transfer heat, which directly affects the heat exchange efficiency. The thermal conductivity may vary at different temperatures, pressures or fluid compositions.

[0254] Cold-side medium thermal conductivity sequence: similar to the hot side, this refers to a series of values of the thermal conductivity of the medium on the cold side of the heat exchanger (usually low-temperature fluid such as cooling water or air) under clean operating conditions. The thermal conductivity of the cold-side medium also affects the heat transfer efficiency of the heat exchanger.

[0255] Hot-side medium viscosity sequence: viscosity is a physical quantity that measures the resistance to fluid flow, which affects the fluid flow and pumping energy consumption. The viscosity of the hot-side medium changes with temperature, pressure and fluid composition, and these change data are very important for analyzing fluid flow characteristics and predicting possible flow problems (such as blockage).

[0256] Cold-side medium viscosity sequence: similarly, the viscosity of the cold-side medium also changes with operating conditions, which is also important for ensuring efficient operation of the heat exchanger and predicting potential flow problems.

[0257] In step S802 of some embodiments, based on the healthy operating condition data, the clean heat transfer coefficient of the nuclear power plant heat exchanger under clean operating conditions is calculated. The clean heat transfer coefficient refers to the heat transfer efficiency of the heat exchanger without the influence of fouling. This parameter provides a benchmark for subsequent fouling analysis.

[0258] Step S803 of some embodiments is to determine the corresponding clean total thermal resistance according to the calculated clean heat transfer coefficient. The clean total thermal resistance is the thermal resistance value of the heat exchanger in the non-fouling state, which is an important reference for evaluating the fouling impact.

[0259] Step S804 of some embodiments is to determine the hyperparameters of the fouling diagnosis model using the healthy operating data, the thermal conductivity sequence and the viscosity sequence of the hot-side medium and the cold-side medium, and the clean total thermal resistance.

[0260] Step S805 of some embodiments is to preset the fouling diagnosis model according to the determined model hyperparameters. This step ensures that the fouling diagnosis model can accurately predict the fouling condition according to the given operating conditions and medium characteristics.

[0261] In some more specific embodiments, the clean heat transfer coefficient K of the nuclear power plant heat exchanger under each clean operating condition can be calculated based on the healthy operating data, which can be expressed as:

[0262] where Q h and Q c are the powers of the hot side and the cold side, A is the heat exchange area, and ΔT m is the logarithmic mean temperature difference.

[0263] In addition, the current clean heat transfer coefficient K clean can be calculated under the condition that the heat exchanger has no fouling attached. Further, the clean total thermal resistance R clean is obtained:

[0264] Further, the hot-side flow rate sequence and the cold-side flow rate sequence of the past data are determined from the healthy operating data, the historical hot-side mass flow rate is determined according to the hot-side flow rate sequence, and the historical cold-side mass flow rate is determined according to the cold-side flow rate sequence. The thermal conductivity of the hot-side medium, the thermal conductivity of the cold-side medium, the viscosity of the hot-side medium, and the viscosity of the cold-side medium under different clean operating conditions are determined from the thermal conductivity sequence of the hot-side medium, the thermal conductivity sequence of the cold-side medium, the viscosity sequence of the hot-side medium, and the viscosity sequence of the cold-side medium.

[0265] It should be understood that the clean fouling thermal resistance equation is an important mathematical tool in heat exchanger performance analysis and fault diagnosis, which quantitatively describes the thermal resistance characteristics of the heat exchanger under different operating conditions, and provides a theoretical basis for the design, optimization and maintenance of the heat exchanger. The clean fouling thermal resistance equation is expressed as follows:

[0266] R clean = A · (μ x-y · λ y-1 · q -x )SEC + B · (μ x-y · λ y-1 · q -x ) RRI + C

[0267] (μ x-y · λ y-1 · q -x ) SEC represents a calculation element fused by the medium viscosity, medium thermal conductivity, and historical mass flow rate on the cold side;

[0268] (μ x-y · λ y-1 · q -x ) RRI represents a calculation element fused by the medium viscosity, medium thermal conductivity, and historical mass flow rate on the hot side;

[0269] wherein A, B, C, x, and y are hyperparameters.

[0270] It should be noted that the clean fouling resistance equation quantitatively describes the thermal resistance of the heat exchanger without the influence of fouling by combining the physical properties (viscosity, thermal conductivity) of the medium and the operating conditions (historical mass flow rate). This expression allows the model to capture the changes in the performance of the heat exchanger under different operating conditions.

[0271] By adjusting the values of x and y, the relative importance of different physical properties on the thermal resistance can be simulated, which is very useful for optimizing the design and operating conditions of the heat exchanger.

[0272] Furthermore, the values of the hyperparameters A, B, C, and x, y can be obtained by using the least squares method in the embodiments of the present application, only the model parameters that best fit the actual monitoring data are found. This involves constructing an objective function (usually the sum of squares of errors), and then minimizing this objective function through optimization algorithms (such as gradient descent, genetic algorithm, etc.), so as to obtain the model parameters that best fit the actual data. In this way, the fouling diagnosis model can be preset according to these model parameters.

[0273] It should be noted that after the fouling diagnosis model is preset according to the hyperparameters A, B, C, x, and y, the hot side mass flow rate and the cold side mass flow rate in the fouling diagnosis parameters can be substituted into the fouling diagnosis model for real-time calculation to obtain the fouling resistance value as the fouling diagnosis calculation result. The preset fouling diagnosis model is represented as:

[0274] wherein R total is the total fouling resistance, is the thermal resistance value after removing fouling under the current operating condition.

[0275] In this way, the fouling resistance value can be calculated in real time through the preset fouling diagnosis model

[0276] After the preset of the fouling diagnosis model is completed, step S502 substitutes the fouling diagnosis parameters, i.e., the hot-side mass flow rate and the cold-side mass flow rate, into the preset fouling diagnosis model for real-time calculation. This calculation process utilizes the model's hyperparameters and real-time monitoring data to assess the fouling condition of the heat exchanger.

[0277] Step S806 of some embodiments, through the real-time calculation of the hot-side and cold-side flow data, the fouling diagnosis model can calculate the fouling resistance value as the result of the fouling diagnosis calculation. The fouling resistance value is a key indicator for evaluating the impact of fouling, which reflects the actual impact of fouling on the heat transfer efficiency of the heat exchanger. The generated fouling diagnosis calculation result provides important information for the operation and maintenance of the heat exchanger, helping to take cleaning or optimization measures in a timely manner to ensure the efficient operation of the heat exchanger.

[0278] In some embodiments of the present application, the blockage fault is a common and serious fault type in heat exchangers. When part or all of the flow passage of the heat exchanger is blocked by impurities, deposits or other substances, it will significantly affect the flow characteristics of the fluid and the overall performance of the heat exchanger. The direct consequence of this fault is the increase in pressure drop and the decrease in heat exchange efficiency, which are important basis for blockage fault diagnosis.

[0279] Firstly, blockage leads to a decrease in the cross-sectional area of the fluid flow, thereby increasing the resistance of the fluid flow. This increase in resistance directly reflects as an increase in pressure drop, i.e., an increase in the pressure difference required for the fluid to pass through the heat exchanger. In the operation of the heat exchanger, the monitoring of pressure drop is one of the routine maintenance work, therefore, by monitoring the change of pressure drop, the occurrence of blockage can be found in time. Compared with leakage fault, the change of pressure drop caused by blockage fault is usually more dramatic and obvious, because leakage mainly affects the sealing of the fluid, while blockage directly affects the flow path of the fluid.

[0280] Secondly, blockage fault also affects the heat exchange efficiency of the heat exchanger. Due to the presence of blockage material, the effective heat transfer area of the heat exchanger is reduced, and at the same time, blockage may also lead to unevenness of fluid flow, further reducing the heat exchange efficiency. This decrease in efficiency may manifest as a change in outlet temperature, for example, the temperature drop of the hot fluid is smaller than expected, or the temperature rise of the cold fluid is smaller than expected.

[0281] In actual operation, by monitoring and analyzing the changes of pressure drop and heat exchange efficiency, combined with the design parameters and historical operation data of the heat exchanger, the blockage fault can be effectively distinguished. Once the existence of blockage is determined, corresponding cleaning or maintenance measures can be taken to restore the normal operation of the heat exchanger. In addition, regular maintenance and cleaning plan is also an important measure to prevent blockage fault. Through the monitoring and analysis of key parameters such as pressure drop and heat exchange efficiency, the blockage problem can be found and handled in time, so as to avoid performance decline and potential safety risk, and ensure the efficient and reliable operation of the heat exchanger.

[0282] Referring to FIG. 9, a feasible qualitative change trend library is shown, and in the embodiment of the present application, the qualitative change trend library of three typical faults of blockage, fouling and leakage can also be constructed to preliminarily analyze the actual working condition data. In FIG. 9, the upward arrow represents that the corresponding subdivision data in the actual working condition data gradually increases, and the downward arrow represents that the corresponding subdivision data in the actual working condition data gradually decreases.

[0283] In summary, step S104 provides accurate fault types for the fault diagnosis of the heat exchanger of the nuclear power plant through in-depth analysis based on the actual working condition data. This step ensures that the heat exchanger can be properly maintained and repaired in time, which helps to reduce unexpected downtime and improve the operation efficiency and safety of the nuclear power plant. Through this fine fault analysis, the performance of the heat exchanger can be more effectively managed and optimized, ensuring the stable operation of the nuclear power plant.

[0284] After step S104, the corresponding diagnosis results can be generated based on the fault diagnosis type of the heat exchange operation state. In the explanation and report of the diagnosis results, the diagnosis results need to be presented in a clear and accurate manner to ensure that the nature of the fault can be fully demonstrated. The detailed diagnosis report can include the fault type, possible causes, recommended maintenance measures and expected effects.

[0285] Referring to FIG. 10, FIG. 10 shows the hardware structure of an electronic device according to another embodiment, which includes:

[0286] The processor 1001 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, etc., for executing related programs to implement the technical solutions provided by the embodiments of the present application;

[0287] The memory 1002 can be implemented in the form of a Read-Only Memory (ROM), a static storage device, a dynamic storage device, or a Random Access Memory (RAM), etc. The memory 1002 can store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present specification are implemented by software or firmware, the related program codes are stored in the memory 1002 and are called and executed by the processor 1001 to implement the nuclear power plant heat exchanger fault diagnosis method of the embodiments of the present application;

[0288] The input / output interface 1003 is configured to realize information input and output.

[0289] The communication interface 1004 is configured to realize the communication interaction between the device and other devices, and the communication can be realized by a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.).

[0290] The bus 1005 is configured to transmit information between various components (for example, the processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004) of the device.

[0291] The processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004 are connected to each other through the bus 1005 to realize the communication connection between the device.

[0292] The present application also provides a computer program product, which includes a computer program. The processor of the computer device reads the computer program and executes it, so that the computer device executes the nuclear power plant heat exchanger fault diagnosis method.

[0293] The terms "first", "second", "third", "fourth" and the like (if any) in the specification of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "contain" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device containing a series of steps or units does not necessarily limit to the clearly listed steps or units, but can include other steps or units that are not clearly listed or inherent to the process, method, product or device.

[0294] It should be understood that, in the present application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, "A and / or B" can mean that there are three cases of A only, B only, and A and B at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after it. "At least one of the following" or similar expressions means any combination of these items, which can include any combination of single or multiple items. For example, at least one of a, b or c can mean a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0295] It should be understood that in the description of the embodiments of the present application, the meaning of multiple (or multiple) is two or more, greater than, less than, more than, etc. is not included in the number, and above, below, etc. is included in the number.

[0296] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division, and actual implementation can have another division manner. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed mutual ones can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0297] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment.

[0298] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically, or two or more units can be integrated into one unit. The above integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0299] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and can include a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0300] It should also be appreciated that the various embodiments provided by the present application can be combined arbitrarily to achieve different technical effects.

[0301] The above is a specific description of the embodiments of the present application, but the present application is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or replacements without departing from the spirit of the present application, and these equivalent modifications or replacements are included in the scope defined by the claims of the present application.

Claims

A method for diagnosing a failure of a heat exchanger of a nuclear power plant, characterized by, The method comprises the following steps: detecting a nuclear power plant heat exchanger to obtain actual working condition data; obtaining health working condition data matched with the nuclear power plant heat exchanger; performing rough fault qualitative analysis on the actual working condition data according to the health working condition data to determine a heat exchange running state of the nuclear power plant heat exchanger; in response to the heat exchange running state being determined as a fault running state, performing fine fault qualitative analysis based on the actual working condition data to determine a fault diagnosis type matched with the heat exchange running state. The method of claim 1, wherein The method of performing fine fault qualitative analysis based on the actual working condition data to determine a fault diagnosis type matched with the heat exchange running state in response to the heat exchange running state being determined as a fault running state comprises the following steps: extracting a leakage diagnosis parameter from the actual working condition data; substituting the leakage diagnosis parameter into a preset leakage diagnosis model to perform real-time calculation and obtain a leakage diagnosis calculation result; determining a fault detection time of the nuclear power plant heat exchanger in response to the heat exchange running state being determined as a fault running state; determining that the fault diagnosis type comprises a leakage fault type in response to the leakage diagnosis calculation result corresponding to the fault detection time satisfying a leakage diagnosis condition. The method according to claim 2, characterized in that The leakage diagnosis parameter comprises a hot side mass flow rate, a cold side mass flow rate, a hot side inlet and outlet enthalpy value and a cold side inlet and outlet enthalpy value, and the method of substituting the leakage diagnosis parameter into a preset leakage diagnosis model to perform real-time calculation and obtain a leakage diagnosis calculation result comprises the following steps: substituting the hot side mass flow rate, the cold side mass flow rate, the hot side inlet and outlet enthalpy value and the cold side inlet and outlet enthalpy value into the corresponding leakage diagnosis model to perform real-time calculation and obtain a leakage factor as the leakage diagnosis calculation result; The method of determining that the fault diagnosis type comprises a leakage fault type in response to the leakage diagnosis calculation result corresponding to the fault detection time satisfying a leakage diagnosis condition comprises the following steps: calculating a corresponding fluctuation variance value according to the leakage factor before the fault detection time; determining the leakage diagnosis condition based on the fluctuation variance value; determining that the fault diagnosis type comprises the leakage fault type in response to the leakage factor corresponding to the fault detection time satisfying the leakage diagnosis condition. The method according to claim 2, characterized in that The leakage diagnosis parameter comprises a cold medium volume flow rate, a cold medium constant-pressure specific heat, a medium density, a hot side inlet temperature, a hot side outlet temperature, a cold side inlet temperature and a cold side outlet temperature, and the method of substituting the leakage diagnosis parameter into a preset leakage diagnosis model to perform real-time calculation and obtain a leakage diagnosis calculation result comprises the following steps: substituting the cold medium volume flow rate, the cold medium constant-pressure specific heat, the medium density, the hot side inlet temperature, the hot side outlet temperature, the cold side inlet temperature and the cold side outlet temperature into the corresponding leakage diagnosis model to perform real-time calculation and obtain a leakage calculation amount as the leakage diagnosis calculation result; The method of determining that the fault diagnosis type comprises a leakage fault type in response to the leakage diagnosis calculation result corresponding to the fault detection time satisfying a leakage diagnosis condition comprises the following steps: obtaining a leakage diagnosis threshold value and determining the leakage diagnosis condition according to the leakage diagnosis threshold value; In response to the leakage calculation quantity satisfying the leakage diagnosis condition, the fault diagnosis type is determined to include the leakage fault type. The method according to claim 4, characterized in that The cold medium volume flow rate, the cold medium constant-pressure specific heat, the medium density, the hot side inlet temperature, the hot side outlet temperature, the cold side inlet temperature, and the cold side outlet temperature are substituted into the corresponding leakage diagnosis model for real-time calculation, to obtain a leakage calculation quantity as the leakage diagnosis calculation result, including: A first leakage diagnosis element is constructed according to the cold side outlet temperature and the cold side inlet temperature; The cold medium volume flow rate is determined as a second leakage diagnosis element; The hot side outlet temperature is determined as a third leakage diagnosis element; The leakage calculation quantity is obtained by real-time calculation in the leakage diagnosis model according to the first leakage diagnosis element, the second leakage diagnosis element, and the third leakage diagnosis element, as the leakage diagnosis calculation result. The method according to claim 5, characterized in that The first leakage diagnosis element is constructed according to the cold side outlet temperature and the cold side inlet temperature, including: The cold side outlet temperature and the cold side inlet temperature are subtracted to obtain the first leakage diagnosis element. The method according to claim 6, characterized in that The leakage calculation quantity is obtained by real-time calculation in the leakage diagnosis model according to the first leakage diagnosis element, the second leakage diagnosis element, and the third leakage diagnosis element, as the leakage diagnosis calculation result, including: The product of the first leakage diagnosis element and the second leakage diagnosis element is determined as a model numerator term; A model denominator term is determined according to the first leakage diagnosis element and the third leakage diagnosis element; The model numerator term and the model denominator term are solved to obtain the leakage calculation quantity as the leakage diagnosis calculation result. The method of claim 7, wherein The model denominator term is determined according to the first leakage diagnosis element and the third leakage diagnosis element, including: The third leakage diagnosis element is determined as a subtracted element of the model denominator term; Half of the first leakage diagnosis element is determined as a subtracted element of the model denominator term; The model denominator term is obtained by subtracting the subtracted element from the subtracted element. The method of claim 1, wherein In response to the heat exchange operating state being determined as a fault operating state, the actual operating condition data is used for fault precise qualitative analysis to determine a fault diagnosis type matched with the heat exchange operating state, including: A fouling diagnosis parameter is extracted from the actual operating condition data; The fouling diagnosis parameter is substituted into a preset fouling diagnosis model for real-time calculation to obtain a fouling diagnosis calculation result; In response to the heat exchange operating state being determined as a fault operating state, a fault detection time of the nuclear power plant heat exchanger is determined; In response to the fouling diagnosis calculation result corresponding to the fault detection time satisfying a fouling diagnosis condition, the fault diagnosis type is determined to include a fouling fault type. The method of claim 9, wherein The fouling diagnosis calculation result includes an actual heat transfer efficiency, and the fouling diagnosis parameter is substituted into a preset fouling diagnosis model for real-time calculation to obtain a fouling diagnosis calculation result, including: A clean heat transfer efficiency and a reference heat transfer efficiency of the nuclear power plant heat exchanger are obtained; The clean heat transfer efficiency, the reference heat transfer efficiency and the actual heat transfer efficiency are substituted into the corresponding fouling diagnosis model for real-time calculation to obtain a fouling factor as the fouling diagnosis calculation result; The fouling diagnosis parameter includes a medium flow rate and a flow pressure loss, and the fouling diagnosis parameter is substituted into a preset fouling diagnosis model for real-time calculation to obtain a fouling diagnosis calculation result, including: The medium flow rate and the flow pressure loss are substituted into the corresponding fouling diagnosis model for real-time calculation to obtain a scale layer thickness as the fouling diagnosis calculation result; The fouling diagnosis parameter includes a medium flow rate and a flow pressure loss, and the fouling diagnosis parameter is substituted into a preset fouling diagnosis model for real-time calculation to obtain a fouling diagnosis calculation result, including: The method of claim 10, wherein A scale layer thickness threshold is obtained, and the scale layer thickness threshold is used to determine the fouling diagnosis condition; The scale layer thickness satisfies the fouling diagnosis condition, and it is determined that the fault diagnosis type includes the fouling fault type. The fouling diagnosis parameter includes a hot side mass flow rate and a cold side mass flow rate, and before the fouling diagnosis parameter is substituted into a preset fouling diagnosis model for real-time calculation to obtain a fouling diagnosis calculation result, the fouling diagnosis model is preset, specifically including: A hot side medium thermal conductivity sequence, a cold side medium thermal conductivity sequence, a hot side medium viscosity sequence and a cold side medium viscosity sequence of the nuclear power plant heat exchanger are obtained; Based on the health working condition data, a clean heat transfer coefficient of the nuclear power plant heat exchanger under a clean working condition is calculated; The method of claim 10, wherein A corresponding clean total thermal resistance is determined according to the clean heat transfer coefficient; A model hyperparameter is determined according to the health working condition data, the hot side medium thermal conductivity sequence, the cold side medium thermal conductivity sequence, the hot side medium viscosity sequence, the cold side medium viscosity sequence and the clean total thermal resistance; The fouling diagnosis model is preset according to the model hyperparameter; The hot side mass flow rate and the cold side mass flow rate are substituted into the fouling diagnosis model for real-time calculation to obtain a fouling thermal resistance value as the fouling diagnosis calculation result. The health working condition data is used to perform a fault rough qualitative analysis on the actual working condition data to determine a heat exchange running state of the nuclear power plant heat exchanger, including: Multivariate state assessment is performed on the health working condition data to obtain a multi-dimensional health memory matrix; Multivariate state assessment is performed on the actual working condition data to obtain a multi-dimensional actual working condition matrix; Similarity comparison is performed based on the multi-dimensional health memory matrix and the multi-dimensional actual working condition matrix to obtain a similarity comparison result; The method of claim 1, wherein ​ ​ ​ ​ According to the similarity comparison result, the heat exchange operation state of the heat exchanger of the nuclear power plant is determined. An electronic device, characterized by comprising: The method comprises the steps of: The memory stores a computer program, and the processor executes the computer program to realize the nuclear power plant heat exchanger fault diagnosis method according to any one of claims 1 to 13. A computer-readable storage medium, characterized by The storage medium stores a program, and the processor executes the program to realize the nuclear power plant heat exchanger fault diagnosis method according to any one of claims 1 to 13.

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