Boiler fault early warning and diagnosis method, device and equipment and storage medium

Abstract

The invention discloses a boiler fault early warning and diagnosis method, device and equipment and a storage medium, relates to the technical field of fault diagnosis of industrial boiler equipment, and discloses a boiler fault early warning and diagnosis method which comprises the steps that main heating surface information, historical data and current boiler heating parameters of a boiler are obtained; establishing a boiler model according to the main heating surface information, and establishing a damage early warning library according to the historical data; obtaining a boiler area and a preset boundary condition according to the boiler model, the historical data and an adaptive grid strategy, and constructing a fluid model and a solid model according to the boiler area and the preset boundary condition; and obtaining an early warning result according to the current boiler heating parameter, the fluid model, the solid model and the damage early warning library, and performing safety diagnosis according to the early warning result. Through establishment of a damage early warning library and fluid-solid combined analysis, early warning and safety diagnosis of faults of the main heating surface of the boiler are realized.

Description

Technical Field

[0001] The present application relates to the technical field of fault diagnosis of industrial boiler equipment, and in particular to boiler fault early warning and diagnosis methods, devices, equipment and storage media. Background Art

[0002] Boilers are key equipment in the industrial field. Their main heating surfaces, such as water-cooled walls, convection tube bundles, and superheaters, are under the influence of heat loads and mechanical stresses for a long time under high temperature and high pressure operating environments, and are prone to damage such as thermal fatigue, corrosion, and wear. Once the damage to the boiler accumulates to a certain extent, it will lead to boiler failure and easily cause serious safety accidents, such as pipe bursts and leaks. Therefore, boiler heating surface status monitoring and fault warning are crucial in industrial safety assurance.

[0003] Traditional boiler monitoring methods mainly rely on periodic maintenance and inspection to detect equipment failures. However, this method often has the problem of discontinuous information. The maintenance data cannot reflect the real-time status of the boiler during dynamic operation, and it is difficult to timely identify the cumulative effect of heating surface damage. In addition, the manual analysis method is not only inefficient, but also prone to omissions and misjudgments due to the lack of systematic data management, and cannot provide accurate fault warnings. This lagging monitoring method has gradually exposed its shortcomings in industrial applications and cannot meet the needs of modern industry for efficient, safe, and continuous operation of boiler systems.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a device, equipment and storage medium, which aims to solve the technical problems of being unable to achieve real-time monitoring and advance warning of boiler heating surface damage, and difficult to predict failure risks in a timely and accurate manner.

[0006] To achieve the above objectives, the present application proposes a boiler fault early warning and diagnosis method, the method comprising:

[0007] Obtain the main heating surface information, historical data and current boiler heating parameters of the boiler;

[0008] Establishing a boiler model according to the main heating surface information, and establishing a damage warning library according to the historical data;

[0009] Obtaining a boiler region and preset boundary conditions according to the boiler model, the historical data, and an adaptive grid strategy, and constructing a fluid model and a solid model according to the boiler region and the preset boundary conditions;

[0010] An early warning result is obtained according to the current boiler heating parameters, the fluid model, the solid model and the damage early warning library, and a safety diagnosis is performed according to the early warning result.

[0011] In one embodiment, the step of establishing a damage warning library according to the historical data includes:

[0012] Performing data preprocessing on the historical data to obtain preprocessed boiler data, wherein the data preprocessing includes data cleaning, outlier removal and data format conversion;

[0013] According to the pre-processed boiler data, damage type, heating surface parameters and damaged empirical parameters are obtained;

[0014] Obtaining a damage mode according to the damage type, the heated surface parameter and the damaged empirical parameter;

[0015] Analyzing the critical point of the change of the heating surface parameters according to the damage mode, obtaining the associated parameter threshold, and obtaining the early warning triggering condition according to the associated parameter threshold;

[0016] A damage warning library is established according to the damage mode and the warning triggering condition.

[0017] In one embodiment, the step of obtaining the damage mode according to the damage type, the heated surface parameter and the damaged empirical parameter comprises:

[0018] constructing a transaction data set according to the damage type and the heating surface parameters;

[0019] Calculating the frequency of transactions in the transaction data set according to an association rule mining strategy, and combining the transactions according to the frequency to obtain fault transactions;

[0020] generating a frequent fault item set according to the frequency of the fault transaction;

[0021] Obtaining a frequent failure weight according to the damaged experience parameter;

[0022] Obtaining a high confidence parameter combination according to the frequent failure item set and the frequent failure weight;

[0023] A damage mode is obtained according to the high confidence parameter combination.

[0024] In one embodiment, the step of obtaining a boiler region and preset boundary conditions according to the boiler model, the historical data and the adaptive grid strategy, and constructing a fluid model and a solid model according to the boiler region and the preset boundary conditions includes:

[0025] Obtaining a heated area according to the boiler model, and obtaining a target boiler grid density according to the heated area and an adaptive grid strategy;

[0026] Dividing the boiler area according to the target boiler grid density;

[0027] Determining the interface and coupling mode between the fluid and the solid according to the historical data and the heated area;

[0028] Using the interface and the coupling mode as preset boundary conditions;

[0029] The thermal field parameters of the boiler area are obtained, and a fluid model and a solid model are constructed according to the thermal field parameters and the preset boundary conditions.

[0030] In one embodiment, the step of obtaining the thermodynamic field parameters of the boiler area and constructing the fluid model and the solid model according to the thermodynamic field parameters and the preset boundary conditions includes:

[0031] Acquiring thermal field parameters of the boiler region, wherein the thermal field parameters include fluid density, fluid velocity, fluid pressure, viscous stress tensor, total fluid energy, temperature, thermal conductivity, viscous dissipation, stress tensor, body force, displacement vector, and material density;

[0032] Constructing a mass conservation model according to the fluid density and the fluid velocity;

[0033] constructing a momentum conservation model according to the fluid pressure and the viscous stress tensor;

[0034] constructing an energy conservation model according to the total energy of the fluid, the temperature, the thermal conductivity and the viscous dissipation;

[0035] Constructing a fluid model according to the mass conservation model, the momentum conservation model, the energy conservation model and the preset boundary conditions;

[0036] A solid model is constructed according to the stress tensor, the body force, the displacement vector, the material density and the preset boundary conditions.

[0037] In one embodiment, the step of obtaining the warning result according to the current boiler heating parameter, the fluid model, the solid model and the damage warning library comprises:

[0038] Performing bidirectional coupling calculation according to the current boiler heating parameters, the fluid model and the solid model to obtain a fluid-solid interaction result;

[0039] According to the fluid-solid interaction results, the stress change rate, temperature fluctuation and deformation characteristics of the heated area of ​​the boiler are obtained;

[0040] According to the damage mode of the damage early warning library, the stress change rate, temperature fluctuation and deformation characteristics are analyzed to obtain the current boiler condition;

[0041] The current boiler condition is matched with the warning triggering condition of the damage warning library to obtain a warning matching result.

[0042] In one embodiment, the step of performing bidirectional coupling calculation according to the current boiler heating parameters, the fluid model and the solid model to obtain the fluid-solid interaction result includes:

[0043] Calculate the fluid temperature and fluid pressure generated by the fluid on the solid structure surface of the boiler according to the current boiler heating parameters and the fluid model;

[0044] Calculating solid stress and solid deformation according to the fluid temperature, the fluid pressure and the solid model;

[0045] The fluid model is updated by using the solid stress and the solid deformation to obtain an updated fluid model;

[0046] The fluid-solid interaction is calculated according to the updated fluid model and the solid model to obtain a fluid-solid interaction result.

[0047] In addition, to achieve the above-mentioned purpose, the present application also proposes a boiler fault warning and diagnosis device, the boiler fault warning and diagnosis device comprising: an acquisition module, used to obtain the main heating surface information, historical data and current boiler heating parameters of the boiler;

[0048] An establishment module is used to establish a boiler model according to the main heating surface information and to establish a damage warning library according to the historical data;

[0049] A construction module, used for obtaining a boiler region and preset boundary conditions according to the boiler model, the historical data and the adaptive grid strategy, and constructing a fluid model and a solid model according to the boiler region and the preset boundary conditions;

[0050] The early warning module is used to obtain early warning results according to the current boiler heating parameters, the fluid model, the solid model and the damage early warning library, and perform safety diagnosis according to the early warning results.

[0051] In addition, to achieve the above-mentioned purpose, the present application also proposes a boiler fault warning and diagnosis device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the boiler fault warning and diagnosis method as described above.

[0052] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the boiler fault warning and diagnosis method described above are implemented.

[0053] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the boiler fault warning and diagnosis method as described above.

[0054] One or more technical solutions proposed in this application have at least the following technical effects:

[0055] By establishing a damage warning library through the information and historical data of the main heating surface of the boiler, and dividing the boiler area in combination with the adaptive grid strategy, it is possible to set precise warning boundary conditions in different heating areas, conduct a comprehensive analysis and comparison of the real-time monitored heating parameters, and then trigger the corresponding warning conditions, thereby solving the problem of difficulty in real-time monitoring and accurate prediction of boiler heating surface damage due to discontinuous data and unclear fault modes in the existing technology. By matching real-time data with damage patterns, this solution can not only dynamically identify potential fault hazards on the heating surface, but also provide advance warning before the fault occurs. Compared with the existing technology, this solution significantly improves the accuracy and response speed of fault prediction, provides more reliable protection for the safe operation of the boiler, and effectively reduces the risk of sudden failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0058] Figure 1 A flow chart of the boiler fault warning and diagnosis method embodiment 1 provided in this application;

[0059] Figure 2 A data transmission diagram provided for the first embodiment of the boiler fault warning and diagnosis method of the present application;

[0060] Figure 3 A flow chart of the boiler fault warning and diagnosis method embodiment 2 provided in this application;

[0061] Figure 4 A schematic diagram of boiler grid density provided for Example 2 of the boiler fault early warning and diagnosis method of the present application;

[0062] Figure 5 A flow chart of the boiler fault warning and diagnosis method embodiment 3 provided in this application;

[0063] Figure 6 This is a schematic diagram of the module structure of the boiler fault warning and diagnosis device according to an embodiment of the present application;

[0064] Figure 7 Schematic diagram of the equipment structure of the hardware operating environment involved in the boiler fault warning and diagnosis method in the embodiment of the present application.

[0065] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0066] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0067] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0068] The main solution of the embodiment of the present application is to obtain the main heating surface information, historical data and current boiler heating parameters of the boiler;

[0069] Establishing a boiler model according to the main heating surface information, and establishing a damage warning library according to the historical data;

[0070] Obtaining a boiler region and preset boundary conditions according to the boiler model, the historical data, and an adaptive grid strategy, and constructing a fluid model and a solid model according to the boiler region and the preset boundary conditions;

[0071] An early warning result is obtained according to the current boiler heating parameters, the fluid model, the solid model and the damage early warning library, and a safety diagnosis is performed according to the early warning result.

[0072] In this embodiment, for the convenience of description, the following description is made by taking the identification of boiler fault warning and diagnosis equipment as the execution subject.

[0073] Since the existing technology cannot realize real-time monitoring and advance warning of boiler heating surface damage, it is difficult to predict the risk of failure in a timely and accurate manner. The present application provides a solution. Through the main heating surface information and historical data of the boiler, a damage warning library is established, and the boiler area is divided in combination with an adaptive grid strategy. It can set accurate warning boundary conditions in different heating areas, conduct a comprehensive analysis and comparison of the real-time monitored heating parameters, and then trigger the corresponding warning conditions, thereby solving the problem of difficulty in real-time monitoring and accurate prediction of boiler heating surface damage caused by discontinuous data and unclear fault modes in the existing technology. By matching real-time data with damage patterns, this solution can not only dynamically identify potential fault hazards on the heating surface, but also provide advance warnings before the fault occurs. Compared with the existing technology, this solution significantly improves the accuracy and response speed of fault prediction, provides more reliable protection for the safe operation of the boiler, and effectively reduces the risk of sudden failures.

[0074] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a boiler fault warning and diagnosis device, etc. The following takes the boiler fault warning and diagnosis device as an example to illustrate this embodiment and the following embodiments.

[0075] Based on this, the embodiment of the present application provides a boiler fault warning and diagnosis method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the boiler fault warning and diagnosis method of the present application.

[0076] In this embodiment, the boiler fault early warning and diagnosis method includes steps S10 to S40:

[0077] Step S10, obtaining the main heating surface information, historical data and current boiler heating parameters of the boiler;

[0078] It should be noted that the main heating surface can refer to the area in the boiler that is directly exposed to high-temperature flue gas or combustion products and absorbs heat, such as water-cooled walls, convection tube bundles, and superheaters. The main heating surface is subjected to high temperatures and pressures during boiler operation and is an important heating area of ​​the boiler equipment. The main heating surface information usually includes but is not limited to material properties, such as material, thickness, design pressure, design temperature, anti-oxidation performance and other information. The main heating surface information determines the thermal stability and loss resistance of the heating surface, which directly affects the working life and safety of the boiler.

[0079] In addition, it should be noted that historical data are the operating parameters and fault records accumulated by the boiler in the past operation, including temperature, pressure, operating time, maintenance records, etc. Historical data can reflect the fluctuations of various parameters during boiler operation, the time and frequency of heating surface damage, and the aging trend of boiler equipment. Historical data are usually obtained from boiler monitoring systems, such as safety instrument systems (SIS) or distributed control systems (DCS), on-site inspection records, and maintenance reports. The current boiler heating parameters may be real-time parameters of the boiler in the current operating state, including but not limited to temperature, pressure, fluid flow rate, oxidizability, etc. The current boiler heating parameters reflect the current load and operating status of the boiler, affecting the real-time stress condition and damage risk of the heating surface.

[0080] It is understandable that boiler safety monitoring requires a data update frequency of milliseconds or seconds, but multi-system data transmission from SIS or DCS systems may be delayed or interrupted, especially in high temperature and high pressure environments. The real-time transmission of sensors may be affected by the environment, causing data updates to lag and affecting real-time monitoring results. In this embodiment, an integrated intelligent data gateway is installed in the boiler monitoring area, which has data compression, priority processing and edge computing functions to intelligently process sensor data and upload key information in real time. The intelligent gateway can classify data according to its importance and real-time nature, such as uploading key parameters such as temperature and pressure in real time, while other data is processed in batches. In this way, low-latency transmission of key data can be prioritized.

[0081] Reference Figure 2 , Figure 2 This is a data transmission diagram of the first embodiment of the boiler fault early warning and diagnosis method of the present application.

[0082] like Figure 2As shown in the figure, the boiler area includes water cooler, superheater wall, reheater, economizer and air preheater, all of which are structures that are easily heated. The water wall is used to absorb the heat radiated by the flame in the boiler furnace and is one of the main heating surfaces of the boiler. The superheater further heats the saturated steam to a superheated state to increase the temperature and thermal enthalpy of the steam. The reheater reheats the steam coming out of the steam turbine to make it have a higher temperature to increase the cycle efficiency. The economizer uses the waste heat of the exhaust gas to preheat the feed water to improve the thermal efficiency of the boiler. The air preheater uses the waste heat of the flue gas to preheat the combustion air to improve the combustion efficiency and energy saving effect. The intelligent gateway can wirelessly connect the various heating surface components in the boiler area and collect and transmit the monitored temperature, pressure and other data in a hierarchical manner. The data is transmitted from the safety instrument system to the one-way isolation device to ensure the unidirectionality of data transmission, prevent external interference from entering the boiler system, and improve the system safety. The data passing through the one-way isolation device can be used for data analysis, parameter modeling, fault warning and safety diagnosis.

[0083] In addition, it is understandable that the data transmission of the sensor may be disturbed in a high temperature and high pressure environment. To ensure the data reliability of key parameters such as pressure and temperature, a redundant sensor layout strategy can be adopted in the boiler structure. For example, the water tube boiler is characterized by water flowing in the tube and flames and flue gases heating outside the tube. Its main heating surfaces include water-cooled walls, convection tube bundles and superheaters. Temperature and pressure sensors can be installed at different heights of the water-cooled wall, such as the upper, middle and lower parts. Each position adopts a double redundant layout, that is, two identical sensors are installed. For the flow tube bundle and superheater area, temperature sensors can be installed at the inlet and outlet of the flow tube bundle and superheater area, respectively, and a double redundant layout is adopted. At the same time, pressure sensors are added at key locations, such as the superheater outlet, and double redundancy is adopted. In addition, the fire tube boiler is characterized by flames and flue gases flowing in the tube and water absorbing heat outside the tube. Its main heating surfaces include furnace, smoke tube and reheating chamber. For the furnace area, temperature sensors are installed at the front, middle and rear parts of the furnace, respectively, and a double redundant layout is adopted. A pressure sensor is installed at the outlet of the furnace, and dual redundancy is also adopted. For the smoke tube area, temperature sensors can be installed at the inlet and outlet of the smoke tube respectively, and a dual redundant arrangement is adopted. A pressure sensor is added at the outlet of the smoke tube, and dual redundancy is adopted. In addition, the combined boiler combines the characteristics of water tube boilers and fire tube boilers, and has a complex heating surface structure. For the water tube part, you can refer to the layout plan of the water tube boiler and install dual redundant temperature and pressure sensors in the water wall, convection tube bundle and superheater area. For the fire tube part, you can refer to the layout plan of the fire tube boiler and install dual redundant temperature and pressure sensors in the furnace, smoke tube and reburner area. After installing redundant sensors, you can assign different weights to the data of different sensors, so that data can be obtained efficiently under normal conditions and accurately at high temperatures or when the sensors are corroded.

[0084] Step S20, establishing a boiler model according to the main heating surface information, and establishing a damage warning library according to the historical data;

[0085] It should be noted that the boiler model is a virtual physical or mathematical structure created based on the main heating surface information, which can reflect the temperature distribution, pressure distribution, heat flow transfer and stress conditions of the boiler in actual operation. The model is used to simulate the working state of the boiler heating surface and predict possible fault locations or damage risks. In this embodiment, the boiler can be modeled at a 1:1 scale based on the information of the main heating surface, such as material, thickness, design pressure, design temperature and other parameters, to simulate the heating changes of the boiler under different working conditions. The modeled model can simulate the heat flow and pressure distribution in the boiler, and can realize three-dimensional visualization to predict the damage risk of the heating surface under different working conditions.

[0086] Exemplarily, the boiler heat pipe failure types stored in the damage warning library can be expressed as in the following Table 1:

[0087] Table 1

[0088]

[0089] In addition, it should be noted that the damage warning library is a database containing historical boiler fault data, damage patterns and warning conditions. The library can store specific parameters of boiler damage in past operations, such as temperature, pressure, etc., as well as the types of these damages, such as corrosion, thermal fatigue, wear, etc., and can set warning thresholds accordingly. It can also record the historical maintenance status of the boiler heating surface, the current damage status, as well as future development trends and governance measures, to provide long-term boiler archive support for operation and maintenance, and can build an expert knowledge base based on the knowledge of experts to further guide operation and maintenance work.

[0090] In a feasible implementation, step S20 may include steps S21 to S25:

[0091] Step S21, performing data preprocessing on the historical data to obtain preprocessed boiler data, wherein the data preprocessing includes data cleaning, outlier removal and data format conversion;

[0092] It is understandable that historical data often contains noise, outliers, structured data, unstructured data, and inconsistent formats, which will affect the accuracy of data analysis and the accuracy of the model. Data preprocessing can remove invalid data, unify data formats, and ensure the reliability of subsequent analysis and model training.

[0093] In addition, it is understood that data cleaning refers to removing invalid or duplicate items in the data, filling missing values, and making the data set complete and consistent. The cleaned data is more accurate and reliable, which is conducive to subsequent pattern analysis. For example, some temperature records are missing in the boiler historical data. Through data cleaning, a reasonable interpolation method can be used to fill in the missing data, make the temperature data more complete, and provide more accurate input data for model building. Outlier removal is to identify and remove abnormal data that deviates significantly from the normal range in the data. These outliers may be caused by sensor failure or data acquisition errors. If they are not removed, they will interfere with the calculation accuracy of the subsequent model. For example, in the data, several extreme values ​​appear in the boiler pressure record, such as an abnormal value that suddenly jumps to 10MPa, which is obviously inconsistent with the actual working conditions. Through the outlier removal step, these data can be filtered out to make the data set more consistent with the actual operation of the boiler. Data format conversion is to unify data from different sources into a consistent format so that it can be effectively used in subsequent analysis and model training. Historical data can come from different systems or record formats and need to be unified into a format that the system can handle. For example, some temperature data are expressed in Celsius and some are expressed in Fahrenheit. After format conversion, they are unified into Celsius to facilitate standardized analysis of the model.

[0094] Step S22, obtaining damage type, heating surface parameters and damaged empirical parameters according to the pre-processed boiler data;

[0095] It should be noted that damage types refer to the forms of damage that may occur to the heating surface of the boiler under different operating conditions, such as high-temperature corrosion, thermal fatigue, wear, etc. The damage types are summarized based on the statistical characteristics of the boiler's historical data and are typical problems faced by different heating areas of the boiler during actual operation. Heating surface parameters refer to the key operating characteristics of the boiler's heating surface area, including temperature, pressure, material properties, etc., which reflect the operating conditions of different heating areas. The heating surface parameters are the basis for judging what kind of damage may occur in this area. Damage experience parameters: refers to parameter values ​​or conditions summarized based on the practical experience of experts in this field on the damage characteristics of the boiler heating surface, usually including key operating parameters, such as critical values ​​of temperature and pressure fluctuations and critical conditions of damage. These parameters are based on the engineering judgment of experts in this field and typical damage conditions accumulated in practice, and are used to identify and warn of potential boiler failures.

[0096] Step S23, obtaining a damage mode according to the damage type, the heating surface parameter and the damaged empirical parameter;

[0097] It should be noted that the damage mode is a specific damage behavior mode obtained by comprehensive analysis of damage type, heating surface parameters and damage experience parameters. The damage mode includes the conditions, operating characteristics and cumulative effects of various damages, and is used to describe the possible damage development trends and risk characteristics of different heating surface areas under various operating conditions. By establishing a damage mode, the potential damage risks of boiler heating surfaces under different environments can be more comprehensively identified.

[0098] In a feasible implementation, step S23 may include steps S231 to S236:

[0099] Step S231, constructing a transaction data set according to the damage type and the heating surface parameters;

[0100] It should be noted that the transaction data set is a data set that pairs damage types with related heating surface parameters. Each transaction contains an operating condition and the corresponding damage characteristics, providing raw data for subsequent analysis. The damage type can obtain the operating condition, and the heating surface parameters can obtain the damage characteristics, and then the transaction data set can be constructed based on the damage type and the heating surface parameters. For example, assuming that a transaction contains "damage type: high temperature corrosion" and related "heating surface parameters: temperature 580°C, pressure 2.9MPa", it can constitute a transaction data entry of high temperature corrosion.

[0101] It is understandable that the heating surface parameters can be discretized according to specific needs. For example, they can be divided into three categories: "sharp rise", "stable" and "sharp drop" according to the value of the change rate of the main steam temperature and the sensitivity of the boiler to the change of the main steam temperature; they can also be divided into three categories: "high fluctuation", "medium fluctuation" and "low fluctuation" according to the fluctuation of the tube wall temperature. This embodiment does not limit the specific values ​​of the discretization. The transaction data set can also be a damage type and a discretized heating surface parameter. For example, the transactions in the transaction data set can be a combination of "main steam temperature = sharp rise, tube wall temperature = high fluctuation, pressure = normal, air coefficient = high" and so on.

[0102] Step S232, calculating the frequency of transactions in the transaction data set according to the association rule mining strategy, and combining the transactions according to the frequency to obtain fault transactions;

[0103] It should be noted that faulty transactions are transactions with a high combination frequency in the transaction data set, that is, parameter combinations that often occur simultaneously are defined as faulty transactions, representing high-frequency combinations that may be associated with damage.

[0104] It can be understood that the association rule mining strategy represents the Apriori algorithm, which is a classic association rule algorithm that searches for frequent item sets, that is, parameter combinations that appear multiple times at the same time, by increasing the number of items layer by layer, and finds parameter combination relationships with statistical significance.

[0105] In addition, it is understandable that after calculating the frequency of transactions in the transaction data set, some frequently occurring parameter combinations can be combined according to the calculated frequency, that is, fault transactions, such as "sharp rise in main steam temperature + high fluctuation in tube wall temperature" or "high air coefficient + low feed water temperature", etc. These high-frequency combinations, that is, fault transactions, can indicate the precursor of boiler damage or abnormality.

[0106] Step S233, generating a frequent fault item set according to the frequency of the fault transaction;

[0107] It should be noted that the frequent fault item set is based on the frequency of fault transactions to filter out the transaction combinations with higher frequency. The frequent fault item set contains those parameter combinations that appear repeatedly in the data and are representative. If "sharp rise in main steam temperature + high fluctuation in tube wall temperature" appears many times, it will enter the frequent fault item set, indicating that this combination is a common and high-risk operating condition combination in boiler operation.

[0108] Step S234, obtaining a frequent failure weight according to the damaged experience parameter;

[0109] It should be noted that the frequent fault weight is to assign different weights to each parameter in the frequent fault item set to reflect its different degree of influence on the boiler damage. Parameters with higher weights have a stronger influence on the final damage pattern. Weights can be assigned to each parameter based on historical data or expert experience. When calculating frequent item sets, adding weight factors based on historical data or expert experience and giving higher priority to parameter combinations with higher weights can more accurately reflect the influencing factors of the boiler damage pattern and generate a more physically meaningful early warning pattern.

[0110] For example, expert experience indicates that the change of main steam temperature has a greater impact on damage, so "sharp rise of main steam temperature" is given a higher weight.

[0111] Step S235, obtaining a high confidence parameter combination according to the frequent failure item set and the frequent failure weight;

[0112] It can be understood that by adding the weights of each parameter in the frequent fault item set, the confidence value of each combination is obtained. The higher the confidence value, the more reliable the combination is in damage prediction. High confidence parameter combinations only retain those combinations with higher confidence values ​​to ensure the accuracy and reliability of the prediction. The confidence is calculated as follows:

[0113]

[0114] In the formula, C({x1,x2,…,x n}) represents the confidence of the frequent fault item set, f(x i ) represents x i The frequency in the frequent itemset, w(x i ) represents x i The weight in the frequent itemsets.

[0115] Step S236, obtaining a damage mode according to the high confidence parameter combination.

[0116] It is understandable that the high confidence parameter combination calculated in step S235 is converted into a damage pattern, and the parameter combination conditions contained in each damage pattern are highly correlated with the corresponding damage risk. These patterns are stored in the damage warning library, and can be compared with the actual parameters in real-time monitoring, and the warning is automatically triggered when the conditions are met.

[0117] Step S24, analyzing the critical point of the change of the heating surface parameters according to the damage mode, obtaining the associated parameter threshold, and obtaining the early warning triggering condition according to the associated parameter threshold;

[0118] It is understandable that based on the known damage mode, by observing the critical points of the changes in the parameters of the heated surface, the key parameter values ​​where the damage begins to accumulate or significantly intensify can be found. For example, in the high-temperature corrosion mode, when the temperature of the water-cooled wall reaches a certain value, the corrosion will rapidly intensify, and this temperature value is the critical point of corrosion. Analyzing these critical points can accurately locate the sensitive working conditions where damage occurs.

[0119] It should be noted that the associated parameter threshold refers to the upper or lower limit of the parameter associated with the damage critical point, that is, the key parameter range that triggers damage in the damage mode. The associated parameter threshold can help the system determine when the risk critical state is reached.

[0120] In addition, it is understood that by setting the critical point of the damage mode as the upper and lower limits of the parameter threshold, it is ensured that an early warning is triggered when the monitoring data exceeds the range. For example, by setting 590°C as the upper temperature threshold in the corrosion mode, when the temperature of the boiler heating surface approaches or exceeds the threshold, it enters a high-risk state.

[0121] In addition, it should be noted that the warning trigger condition is a monitoring condition set based on the associated parameter threshold, which is used to automatically trigger the system's damage warning. Once the currently monitored heating surface parameters reach or exceed the set threshold, an immediate warning will be issued. For example, in the high-temperature corrosion mode, the temperature threshold of 590°C and the pressure threshold of 2.9MPa of the water-cooled wall are used as warning trigger conditions. Once the monitoring data shows that the temperature or pressure of the water-cooled wall area reaches these conditions, the warning will be automatically triggered.

[0122] Step S25: establishing a damage warning library according to the damage pattern and the warning triggering condition.

[0123] It is understandable that each damage mode and its corresponding warning trigger conditions are systematically stored in a database to form a damage warning library. This library contains the pattern description of each damage type and its specific trigger conditions, which facilitates the system to quickly find and compare current data during actual operation. Once the operating parameters of a certain working condition meet the warning trigger conditions, the corresponding damage mode will be automatically called from the warning library to generate a warning signal.

[0124] Step S30, obtaining a boiler region and preset boundary conditions according to the boiler model, the historical data and the adaptive grid strategy, and constructing a fluid model and a solid model according to the boiler region and the preset boundary conditions;

[0125] It should be noted that the adaptive mesh strategy is an algorithm that dynamically adjusts the mesh density according to the computational requirements of a specific area. For areas in the boiler model where damage is more frequent or stress is complex, such as water-cooled walls and superheaters, finer mesh division is required, while for areas where damage is less frequent, coarse mesh division can be set to improve the model's computational accuracy in fine areas and improve the computational speed in coarse areas.

[0126] In addition, it should be noted that the boiler area is divided into multiple heating areas according to the boiler model and historical data, after analyzing the areas with more frequent damage or complex stress, such as water-cooled wall area, convection tube bundle area and superheater area, etc. through adaptive grid strategy according to different areas. The preset boundary conditions are boundary setting parameter constraints for fluid models and solid models, which can ensure that each area of ​​the fluid model and solid model in the calculation can correctly reflect the actual stress and heat transfer conditions. The preset boundary conditions include but are not limited to temperature, pressure, flow rate, etc., which can be set based on historical data and actual operating conditions in the boiler area.

[0127] It can be understood that the fluid model can represent the movement and heat transfer characteristics of the fluid in the boiler, including the changing laws of the density, velocity, pressure, etc. of the fluid. The fluid model reflects the fluid distribution state in the boiler through the mass conservation, momentum conservation and energy conservation equations, and analyzes the flow of high-temperature flue gas or water vapor in the boiler. The solid model can represent the stress, strain and temperature distribution of the boiler heating surface material, and is used to analyze the deformation of the boiler internal pipes and heating surface materials under different working conditions. The solid model includes material density, volume force, stress and temperature parameters to ensure that the real stress state of the boiler internal structure is reflected under high temperature and pressure. When the boiler is running, the interaction between fluid and solid is crucial. During the flow process, the fluid exerts pressure and temperature effects on the solid structure, such as the water-cooled wall and superheater, and the deformation of the solid structure in turn affects the flow of the fluid. Therefore, the fluid-solid coupling effect needs to be considered when constructing the fluid model and the solid model.

[0128] Step S40, obtaining a warning result according to the current boiler heating parameters, the fluid model, the solid model and the damage warning library, and performing safety diagnosis according to the warning result.

[0129] It should be noted that the warning result is a boiler damage warning obtained based on the current operating parameters of the boiler, and further damage diagnosis can be performed based on the warning result.

[0130] It is understandable that the real-time parameters of the current boiler can be input into the fluid model and the solid model to perform a two-way coupling calculation to obtain the fluid-solid interaction results, such as the stress change rate, temperature fluctuation and deformation characteristics of the heated area. These results are compared with the damage patterns in the damage warning library to determine whether the warning conditions are triggered.

[0131] In addition, it is understandable that intelligent robots can be used to perform safety diagnosis on the damaged parts corresponding to the early warning results, and the damage warning data can be uploaded in real time, and a graphical display can be given based on the modeling to guide the robot to perform rapid and accurate repairs. When entering the maintenance period, combined with the fault prediction results, the robot replaces manual labor for rapid and comprehensive inspections, and the inspection results are uploaded in real time and visualized for analysis. Maintenance guidance can also be given based on the damage warning to guide the staff to perform rapid, comprehensive, and accurate repairs to avoid missed inspections and missed repairs, and to eliminate boiler safety hazards to the greatest extent.

[0132] This embodiment provides a boiler fault warning and diagnosis method, which solves the technical problems of data real-time, accuracy and prediction reliability in boiler fault warning and diagnosis through an integrated intelligent data gateway, redundant sensor layout strategy, adaptive grid strategy and coupling calculation technology of fluid and solid models, and achieves the beneficial effects of improving boiler operation safety, reducing failure rate and extending equipment service life.

[0133] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3 The step S30 of the boiler fault early warning and diagnosis method further includes steps S31 to S35:

[0134] Step S31, obtaining a heated area according to the boiler model, and obtaining a target boiler grid density according to the heated area and an adaptive grid strategy;

[0135] It should be noted that the heated area refers to those areas in the boiler that directly bear the heat load of high-temperature flue gas or water vapor, such as water-cooled walls, superheaters, etc. The heated area is often a high-risk area for damage accumulation. The target grid density is the degree of refinement of the division under the adaptive grid strategy to ensure the calculation accuracy of the high-risk area.

[0136] It can be understood that the adaptive grid strategy is an algorithm that adjusts the grid density according to the heated area. By adjusting the grid density, the calculation is more concentrated on the heated area and key parts to ensure the calculation accuracy of the model. The adaptive grid strategy can flexibly divide the grid according to the complexity of the heated area. For example, in the water-cooled wall area, due to the large temperature change and complex structure, the adaptive grid strategy will make the grid density of this area higher than other areas to ensure the accuracy of the simulation.

[0137] In addition, it should be noted that the target boiler mesh density is the mesh distribution refinement result generated by the adaptive mesh strategy, which is used to define the number of mesh cells per unit volume or area in different regions to ensure that fine temperature, pressure and flow rate changes are captured in the simulation. The target mesh density is calculated as follows:

[0138]

[0139] In the formula, ρ grid(x,y,z) is the target grid density at position (x,y,z), is the temperature gradient at position (x,y,z), is the pressure gradient at position (x,y,z), is the velocity gradient at position (x, y, z). α, β and γ are the weighting coefficients for controlling temperature, pressure and velocity, respectively.

[0140] Step S32, dividing the boiler area according to the target boiler grid density;

[0141] It can be understood that according to the target grid density, the boiler is divided into different areas, the high-density areas are divided into finer grids, and the low-density areas are divided into coarser grids. The calculation model is calculated according to different grid divisions. This can improve the calculation efficiency of the model while ensuring the simulation accuracy of key areas.

[0142] Reference Figure 4 , Figure 4 This is a schematic diagram of boiler grid density according to the second embodiment of the boiler fault early warning and diagnosis method of the present application.

[0143] like Figure 4 As shown in the figure, the central part is a water-cooled wall composed of many slender tubes. The water-cooled wall is the main structure in the boiler that directly absorbs the heat generated by combustion. It is arranged around the furnace and is used to transfer the radiation heat of the flame to the water in the boiler. The water-cooled wall part in the figure uses a denser grid distribution, indicating that the grid division in this area is more refined, while the grid distribution of the non-water-cooled wall part is relatively sparse. This distribution can calculate the heating parameters of the boiler both efficiently and accurately.

[0144] Step S33, determining the interface and coupling mode between the fluid and the solid according to the historical data and the heated area;

[0145] It should be noted that the interface is the boundary where the fluid inside the boiler, such as water vapor or flue gas, is in direct contact with solids such as water-cooled walls or superheater pipes. In the boiler, the heating surface such as the water-cooled wall is usually in direct contact with the high-temperature flue gas, and these areas are where heat transfer and stress concentration are located. By using historical data and thermal field distribution parameters such as temperature and pressure, the areas where heat and force transfer are most significant can be identified. For example, by observing the areas with higher temperature gradients, interfaces with more concentrated heat transfer can be identified.

[0146] In addition, it should be noted that the coupling method refers to the physical interaction between the fluid and the solid at the interface. For example, the heat transfer coupling method is used to simulate the heat transfer from the fluid to the solid, while the pressure coupling method is used to simulate the pressure effect of the fluid on the solid surface.

[0147] It is understandable that in the boiler heating area, the coupling modes of fluid and solid mainly include heat transfer coupling and stress coupling. The coupling mode can ensure that the fluid transfers heat to the solid heating surface, while the temperature and stress of the solid surface in turn affect the flow and temperature distribution of the fluid. Heat transfer coupling refers to the way heat is transferred from the fluid to the solid, for example, in the water-cooled wall area, high-temperature flue gas conducts heat to the water-cooled wall structure. Stress coupling refers to the effect of the pressure of the fluid on the stress of the solid surface, and as the temperature changes, the stress of the solid will also react to the fluid.

[0148] Step S34, taking the interface and the coupling mode as preset boundary conditions;

[0149] It can be understood that setting the interface and coupling mode as the boundary conditions of the interface can make the heat and stress transfer between the fluid and the solid at the interface conform to the actual working conditions. The boundary conditions are directly applied to the fluid and solid models to ensure the accuracy of the simulation.

[0150] Step S35, obtaining the thermal field parameters of the boiler area, and constructing a fluid model and a solid model according to the thermal field parameters and the preset boundary conditions.

[0151] It should be noted that the thermal field parameters include but are not limited to temperature distribution, pressure distribution, fluid flow rate, etc. The thermal field parameters reflect the thermodynamic state of the boiler area during operation and are an important basis for building the model.

[0152] It can be understood that the fluid model constructed based on the thermal field parameters can be used to simulate the flow characteristics of water vapor or flue gas in the boiler, including changes in temperature, pressure and flow rate. The solid model constructed based on the thermal field parameters can be used to simulate the temperature distribution, stress distribution and deformation of the boiler heating surface, such as the water-cooled wall and pipe.

[0153] In a feasible implementation, step S35 may include steps S351 to S356:

[0154] Step S351, obtaining the thermal field parameters of the boiler area, wherein the thermal field parameters include fluid density, fluid velocity, fluid pressure, viscous stress tensor, total fluid energy, temperature, thermal conductivity, viscous dissipation, stress tensor, body force, displacement vector and material density;

[0155] It should be noted that fluid density is the mass of fluid per unit volume, which is usually a function of temperature and pressure. Fluid velocity describes the flow speed of a fluid in space and is expressed as a vector that contains the direction and magnitude of the fluid flow. Fluid pressure is the force per unit area and is usually affected by temperature and fluid density in a boiler. The viscous stress tensor describes the stress generated by the viscosity of the fluid during the flow process and reflects the shear stress between fluid layers. The total energy of the fluid is the sum of the internal energy of the fluid, including kinetic energy, thermal energy and position potential energy. It reflects the total energy of the fluid under the heated state. Temperature is a thermodynamic state variable of fluids and solids, indicating the level of heat. Temperature affects other parameters such as fluid density and thermal conductivity. Thermal conductivity indicates the ability of a substance to transfer heat and is the proportional coefficient of temperature gradient and heat flux density. Viscous dissipation indicates the energy loss of the fluid due to internal friction. The stress tensor is a matrix that describes the stress distribution in different directions inside a solid and indicates the internal response of the solid after being subjected to force. Volume force represents the external force acting on each unit volume of a fluid or solid, such as gravity. The displacement vector represents the displacement of a solid material after thermal expansion or force deformation, reflecting the deformation state of the solid. The material density represents the mass density of the solid material.

[0156] Step S352, constructing a mass conservation model according to the fluid density and the fluid velocity;

[0157] It should be noted that the mass conservation model is used to describe that the mass of the fluid does not change with time, ensuring the mass balance of the fluid during the entire flow process. The mass conservation model ensures the relationship between fluid density and velocity and is directly related to the velocity field of the fluid. The formula of the mass conservation model is as follows:

[0158]

[0159] Where ρ is the fluid density, v is the fluid velocity, It represents the rate of change of density over time.

[0160] Step S353, constructing a momentum conservation model according to the fluid pressure and the viscous stress tensor;

[0161] It should be noted that the momentum conservation model is used to describe the motion characteristics of the fluid and provides the distribution information of the pressure field and velocity field of the fluid. The formula of the momentum conservation model is as follows:

[0162]

[0163] Where p is the fluid pressure, τ is the viscous stress tensor, and g is the gravitational acceleration.

[0164] Step S354, constructing an energy conservation model according to the total energy of the fluid, the temperature, the thermal conductivity and the viscous dissipation;

[0165] It should be noted that the energy conservation model represents the energy conservation of the fluid, involving heat transfer, heat generation or consumption, and is used to determine the temperature field of the fluid to ensure the energy balance of the fluid during flow and heat transfer. The formula of the mass conservation model is as follows:

[0166]

[0167] Where E is the total energy of the fluid, T is the temperature, k is the thermal conductivity, and Φ represents viscous dissipation.

[0168] Step S355, constructing a fluid model according to the mass conservation model, the momentum conservation model, the energy conservation model and the preset boundary conditions;

[0169] It can be understood that the above three conservation equations, combined with the preset boundary conditions, construct the overall model of the fluid. The boundary conditions include the initial state of temperature, pressure and velocity, and it is necessary to make the model satisfy the conservation law in the fluid area. Solving the three conservation equations simultaneously can simulate the real flow characteristics of the fluid inside the boiler, including the temperature and pressure distribution.

[0170] Step S356, constructing a solid model according to the stress tensor, the body force, the displacement vector, the material density and the preset boundary conditions.

[0171] It should be noted that the solid model describes the stress and deformation of the boiler heating surface tube wall, usually using nonlinear structural dynamics equations. Through the solid model formula and solid boundary conditions, a complete solid model of the boiler heating surface is constructed to simulate the influence of temperature and stress distribution on the material. The formula of the solid model is as follows 6:

[0172]

[0173] Where σ is the stress tensor, b is the body force, u is the displacement vector, and ρ is the material density.

[0174] This embodiment provides a boiler fault warning and diagnosis method, which solves the technical problems of insufficient simulation accuracy of the boiler heating area and high complexity of fluid and solid coupling simulation through adaptive grid strategy and accurate thermal field parameter acquisition technology, and achieves the beneficial effects of improving the accuracy of boiler damage prediction and optimizing boiler operation and maintenance strategy.

[0175] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 5 The step S40 of the boiler fault early warning and diagnosis method further includes steps S41 to S44:

[0176] Step S41, performing a bidirectional coupling calculation according to the current boiler heating parameters, the fluid model and the solid model to obtain a fluid-solid interaction result;

[0177] It is understandable that the fluid model and the solid model affect each other, that is, the temperature, pressure and other parameters of the fluid will affect the deformation and stress distribution of the solid, and the temperature and stress of the solid will in turn affect the flow characteristics and temperature distribution of the fluid. In view of this mutual influence, it is necessary to calculate the fluid model and the solid model through bidirectional coupling. The result obtained by the bidirectional coupling calculation includes the interaction between the fluid and the solid at the junction. The interaction obtained by this calculation is the result of fluid-solid interaction, including heat exchange and stress transfer. This result can reflect the stress, temperature and deformation state of the boiler heating area at the heating surface.

[0178] In a feasible implementation, step S41 may include steps S411 to S414:

[0179] Step S411, calculating the fluid temperature and fluid pressure generated by the fluid on the solid structure surface of the boiler according to the current boiler heating parameters and the fluid model;

[0180] It should be noted that the fluid temperature refers to the temperature of the fluid on the surface of the solid structure, such as the water-cooled wall, pipe, etc. This temperature will directly affect the heat load of the boiler heating surface. The fluid pressure refers to the pressure generated by the fluid on the solid surface. This pressure is the force exerted by the fluid on the solid structure and affects the stress distribution of the boiler structure.

[0181] It is understandable that after obtaining the current boiler heating parameters, parameters including but not limited to the fluid inlet temperature, inlet pressure and flow rate can be obtained. The flow path, flow rate, temperature and pressure distribution of the fluid inside the boiler can be calculated using the fluid model and the obtained parameters.

[0182] Step S412, calculating solid stress and solid deformation according to the fluid temperature, the fluid pressure and the solid model;

[0183] It is understandable that the temperature and pressure of the fluid on the solid surface calculated in step S411 are input into the solid model as boundary conditions, and the updated boundary conditions represent the heat and pressure applied by the fluid to the solid. Changes in temperature will cause thermal stress in the solid. The greater the temperature difference, the more significant the thermal stress inside the solid. The solid model will calculate the thermal stress generated by the solid after heating based on the thermal expansion coefficient and elastic modulus of the solid. Under the action of stress, the solid will deform, including expansion, contraction or local bending, etc. The solid model can calculate the deformation amount of the solid under the current pressure and temperature conditions.

[0184] Step S413, updating the fluid model with the solid stress and the solid deformation to obtain an updated fluid model;

[0185] It should be noted that the deformation of solids under pressure and temperature will change the flow path of the fluid. For example, the expansion of the solid will reduce the cross-sectional area of ​​the fluid channel, while the contraction may increase the flow channel space. The fluid model needs to recalculate the flow path based on the deformation of the solid. Due to the change in the flow channel, the fluid velocity will also be affected. The deformation of the solid may cause the flow rate of the fluid to increase or decrease. The fluid model uses the deformation as input to adjust the distribution of the fluid velocity.

[0186] It is understandable that according to the influence of the stress and deformation of the solid on the fluid, the boundary conditions can be updated, and an updated fluid model can be obtained according to the updated boundary conditions. The updated fluid model can more accurately reflect the state of the fluid under the changed solid structure conditions.

[0187] Step S414: calculating the fluid-solid interaction according to the updated fluid model and the solid model to obtain a fluid-solid interaction result.

[0188] It is understandable that after the fluid and solid models are updated, iterative calculations of fluid-solid interaction are performed. Fluid-solid interaction refers to the two-way interaction between fluid and solid in the contact area, including the mutual influence of temperature and pressure. In each iteration, the state of the fluid, such as temperature and pressure, will act on the solid, and the deformation and stress of the solid will affect the fluid. This process will continue to iterate, and during the iterative calculation process, the temperature, pressure, stress and deformation characteristics will be detected based on the calculation results of the fluid model and the solid model. Therefore, parameters such as temperature, pressure, stress and deformation can be comprehensively recorded as the results of fluid-solid interaction, and the results of fluid-solid interaction are used for subsequent analysis and damage prediction.

[0189] Step S42, obtaining the stress change rate, temperature fluctuation and deformation characteristics of the heated area of ​​the boiler according to the fluid-solid interaction result;

[0190] It should be noted that the stress change rate is used to reflect the rate at which the stress in the boiler heating area changes over time, indicating the stress conditions in the area at different time periods. Temperature fluctuation is used to describe the temperature change characteristics of the boiler heating area, including the high and low temperature fluctuation range and frequency. The greater the temperature fluctuation, the more significant the heat load fluctuation in the area. Deformation characteristics are used to describe the structural deformation of the boiler heating area, including deformation modes such as expansion and contraction, reflecting the strain of the material under the action of heat load.

[0191] It can be understood that the fluid-solid interaction results have been obtained through the bidirectional coupling of the fluid model and the solid model. This result includes the temperature distribution, pressure distribution, flow characteristics of the fluid in the boiler heating area, and the force and deformation information of the solid, which can be used for data extraction.

[0192] The extracted stress data are arranged in chronological order. By performing differential analysis on the time series stress data, that is, calculating the change amplitude of the stress value at each time point, the rate of change of stress over time, that is, the stress change rate, can be obtained. The magnitude of the stress change rate reflects the stress fluctuation of the boiler material at different time points.

[0193] In the fluid-solid interaction results, the temperature data of each location point can form a time series. By analyzing the maximum and minimum values ​​of the series, the temperature fluctuation range can be calculated. The temperature fluctuation range represents the difference between the highest and lowest temperatures in the boiler heating area during a specific time period. Large temperature fluctuations usually mean that the area is heated unevenly, which may cause thermal stress and fatigue.

[0194] Displacement data records the deformation of boiler materials at different times. Displacement data can be obtained over a period of time and arranged in spatial position and time order. The displacement difference between adjacent time points is calculated to analyze the expansion and contraction trends of boiler materials. According to the direction and amplitude of displacement change, the deformation mode is judged, such as uniform expansion, local contraction or irregular deformation. By analyzing the displacement difference at different time points, the deformation characteristics of the material such as expansion, contraction or bending can be obtained. These deformation characteristics help determine whether the boiler heating surface will cause structural damage due to excessive deformation.

[0195] Step S43, analyzing the stress change rate, temperature fluctuation and deformation characteristics according to the damage mode of the damage warning library to obtain the current boiler condition;

[0196] It should be noted that the current boiler condition is a comprehensive judgment result obtained by comparing the operating status of the boiler with various damage modes in the damage warning library. It reflects the health status of the boiler under the current operating conditions, especially whether there is a risk of approaching damage or causing damage. The current boiler condition can describe the overall operating status of the boiler and the risk level of a specific heated area.

[0197] It is understandable that the damage warning library contains standardized features of different damage modes, such as thermal fatigue, thermal stress corrosion, wear, etc.

[0198] In addition, it is understandable that the stress change rate of the current boiler area is compared with the stress change rate thresholds of different damage modes in the damage warning library. For example, if the stress change rate threshold defined by a damage mode is 0.5MPa / s, and the stress change rate of the current boiler is 0.6MPa / s, then this parameter meets the characteristics of the damage mode. The range of temperature fluctuations is also matched with the damage mode. For example, the thermal fatigue mode may set temperature fluctuations exceeding 30°C as high risk, and the temperature fluctuation of the current boiler is 35°C, then this temperature fluctuation feature meets the thermal fatigue mode. By observing the deformation characteristics of the boiler material, such as the amplitude and frequency of expansion and contraction, they are compared with similar damage modes in the damage warning library. If the deformation characteristics, such as the local expansion amplitude or contraction frequency, exceed a specific threshold, it may indicate that the boiler is experiencing a certain specific damage risk. The analyzed content is used as the current boiler condition for further matching.

[0199] Step S44, matching the current boiler condition with the warning triggering condition of the damage warning library to obtain a warning matching result.

[0200] It should be noted that each mode in the damage warning library has its corresponding characteristic parameters and threshold conditions, and each damage mode corresponds to a different parameter threshold range. Further matching can be performed based on the threshold. According to the matching degree between the boiler and different damage modes, the risk level of the current boiler can be obtained. Normal means that the current boiler status does not overlap with the parameter threshold of any damage mode, indicating that the boiler is in a healthy state. Slight risk means that the boiler conditions partially match the characteristics of certain damage modes, but do not fully meet the warning trigger conditions. At this time, close monitoring may be required, but measures may not necessarily be taken immediately. High risk means that the current boiler conditions highly match the thresholds of certain damage modes, reaching or exceeding the warning trigger conditions, indicating that there may be a risk of damage in the heated area of ​​the boiler, and it is recommended to take immediate preventive measures or shut down for inspection.

[0201] This embodiment provides a boiler fault warning and diagnosis method, which solves the technical problems of high complexity of fluid-solid interaction in the boiler heating area and insufficient accuracy of damage prediction by means of bidirectionally coupling the computational fluid model and the solid model, and achieves the beneficial effects of real-time monitoring of the stress, temperature and deformation state of the boiler heating area and accurate warning of boiler damage risks.

[0202] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the boiler fault warning and diagnosis method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0203] This application also provides a boiler fault warning and diagnosis device, please refer to Figure 6 , the boiler fault early warning and diagnosis device comprises:

[0204] An acquisition module 10 is used to acquire the main heating surface information, historical data and current boiler heating parameters of the boiler;

[0205] Establishing module 20, used to establish a boiler model according to the main heating surface information, and establish a damage warning library according to the historical data;

[0206] A construction module 30, for obtaining a boiler region and preset boundary conditions according to the boiler model, the historical data and the adaptive grid strategy, and constructing a fluid model and a solid model according to the boiler region and the preset boundary conditions;

[0207] The early warning module 40 is used to obtain an early warning result according to the current boiler heating parameters, the fluid model, the solid model and the damage early warning library, and perform safety diagnosis according to the early warning result.

[0208] The boiler fault warning and diagnosis device provided by the present application adopts the boiler fault warning and diagnosis method in the above embodiment, which can solve the technical problems that it is impossible to realize real-time monitoring and advance warning of boiler heating surface damage, and it is difficult to predict the risk of failure in a timely and accurate manner. Compared with the prior art, the beneficial effects of the boiler fault warning and diagnosis device provided by the present application are the same as the beneficial effects of the boiler fault warning and diagnosis method provided by the above embodiment, and the other technical features of the boiler fault warning and diagnosis device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0209] In one embodiment, the establishment module 20 is also used to perform data preprocessing on the historical data to obtain preprocessed boiler data, and the data preprocessing includes data cleaning, outlier removal and data format conversion; based on the preprocessed boiler data, the damage type, heating surface parameters and damaged empirical parameters are obtained; based on the damage type, the heating surface parameters and the damaged empirical parameters, the damage pattern is obtained; based on the damage pattern, the critical point of the change of the heating surface parameters is analyzed to obtain the associated parameter threshold, and the warning trigger condition is obtained according to the associated parameter threshold; based on the damage pattern and the warning trigger condition, a damage warning library is established.

[0210] In one embodiment, the establishment module 20 is also used to construct a transaction data set according to the damage type and the heated surface parameters; calculate the frequency of transactions in the transaction data set according to the association rule mining strategy, and combine the transactions according to the frequency to obtain fault transactions; generate a frequent fault item set according to the frequency of the fault transactions; obtain frequent fault weights according to the damaged experience parameters; obtain a high confidence parameter combination according to the frequent fault item set and the frequent fault weights; and obtain a damage pattern according to the high confidence parameter combination.

[0211] In one embodiment, the construction module 30 is also used to obtain a heated area according to the boiler model, obtain a target boiler grid density according to the heated area and the adaptive grid strategy; divide the boiler area according to the target boiler grid density; determine the interface and coupling mode of the fluid and solid according to the historical data and the heated area; use the interface and the coupling mode as preset boundary conditions; obtain the thermal field parameters of the boiler area, and construct a fluid model and a solid model according to the thermal field parameters and the preset boundary conditions.

[0212] In one embodiment, the construction module 30 is also used to obtain the thermal field parameters of the boiler area, and the thermal field parameters include fluid density, fluid velocity, fluid pressure, viscous stress tensor, total fluid energy, temperature, thermal conductivity coefficient, viscous dissipation, stress tensor, volume force, displacement vector and material density; construct a mass conservation model based on the fluid density and the fluid velocity; construct a momentum conservation model based on the fluid pressure and the viscous stress tensor; construct an energy conservation model based on the total fluid energy, the temperature, the thermal conductivity coefficient and the viscous dissipation; construct a fluid model based on the mass conservation model, the momentum conservation model, the energy conservation model and the preset boundary conditions; and construct a solid model based on the stress tensor, the volume force, the displacement vector, the material density and the preset boundary conditions.

[0213] In one embodiment, the early warning module 40 is further used to perform bidirectional coupling calculations based on the current boiler heating parameters, the fluid model and the solid model to obtain fluid-solid interaction results; based on the fluid-solid interaction results, the stress change rate, temperature fluctuation and deformation characteristics of the heated area of ​​the boiler are obtained; based on the damage mode of the damage early warning library, the stress change rate, temperature fluctuation and deformation characteristics are analyzed to obtain the current boiler condition; the current boiler condition is matched with the early warning trigger condition of the damage early warning library to obtain an early warning matching result.

[0214] In one embodiment, the early warning module 40 is also used to calculate the fluid temperature and fluid pressure generated by the fluid on the solid structure surface of the boiler according to the current boiler heating parameters and the fluid model; calculate the solid stress and solid deformation according to the fluid temperature, the fluid pressure and the solid model; update the fluid model with the solid stress and the solid deformation to obtain an updated fluid model; calculate the fluid-solid interaction according to the updated fluid model and the solid model to obtain the fluid-solid interaction result.

[0215] The present application provides a boiler fault warning and diagnosis device, which includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the boiler fault warning and diagnosis method in the above-mentioned embodiment one.

[0216] Reference below Figure 7 , which shows a schematic diagram of the structure of a boiler fault warning and diagnosis device suitable for implementing the embodiment of the present application. The boiler fault warning and diagnosis device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The boiler fault warning and diagnosis device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0217] like Figure 7As shown, the boiler fault warning and diagnostic device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. Various programs and data required for the operation of the boiler fault warning and diagnostic device are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 may allow the boiler fault warning and diagnostic device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a boiler fault warning and diagnostic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0218] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0219] The boiler fault warning and diagnosis device provided by the present application adopts the boiler fault warning and diagnosis method in the above embodiment, which can solve the technical problems that it is impossible to realize real-time monitoring and advance warning of boiler heating surface damage, and it is difficult to predict the risk of failure in a timely and accurate manner. Compared with the prior art, the beneficial effects of the boiler fault warning and diagnosis device provided by the present application are the same as the beneficial effects of the boiler fault warning and diagnosis method provided by the above embodiment, and the other technical features of the boiler fault warning and diagnosis device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0220] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0221] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0222] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the boiler fault early warning and diagnosis method in the above-mentioned embodiment.

[0223] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0224] The computer-readable storage medium may be included in the boiler fault early warning and diagnosis device; or may exist independently without being assembled into the boiler fault early warning and diagnosis device.

[0225] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the boiler fault early warning and diagnosis device, the boiler fault early warning and diagnosis device: obtains the main heating surface information, historical data and current boiler heating parameters of the boiler;

[0226] Establishing a boiler model according to the main heating surface information, and establishing a damage warning library according to the historical data;

[0227] Obtaining a boiler region and preset boundary conditions according to the boiler model, the historical data, and an adaptive grid strategy, and constructing a fluid model and a solid model according to the boiler region and the preset boundary conditions;

[0228] An early warning result is obtained according to the current boiler heating parameters, the fluid model, the solid model and the damage early warning library, and a safety diagnosis is performed according to the early warning result.

[0229] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0230] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0231] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0232] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned boiler fault early warning and diagnosis method, and can solve the technical problems of being unable to realize real-time monitoring and advance warning of boiler heating surface damage, and being difficult to timely and accurately predict fault risks. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the boiler fault early warning and diagnosis method provided by the above-mentioned embodiment, and will not be elaborated here.

[0233] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned boiler fault early warning and diagnosis method when executed by a processor.

[0234] The computer program product provided by the present application can solve the technical problems of being unable to realize real-time monitoring and advance warning of boiler heating surface damage, and being difficult to timely and accurately predict the risk of failure. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the boiler fault warning and diagnosis method provided by the above-mentioned embodiment, and will not be elaborated here.

[0235] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A boiler fault early warning and diagnosis method, characterized in that: The method comprises: Obtain the main heating surface information, historical data and current boiler heating parameters of the boiler; Establishing a boiler model according to the main heating surface information, and establishing a damage warning library according to the historical data; Obtaining a boiler region and preset boundary conditions according to the boiler model, the historical data, and an adaptive grid strategy, and constructing a fluid model and a solid model according to the boiler region and the preset boundary conditions; An early warning result is obtained according to the current boiler heating parameters, the fluid model, the solid model and the damage early warning library, and a safety diagnosis is performed according to the early warning result.

2. The method according to claim 1, characterized in that The step of establishing a damage warning library according to the historical data comprises: Performing data preprocessing on the historical data to obtain preprocessed boiler data, wherein the data preprocessing includes data cleaning, outlier removal and data format conversion; According to the pre-processed boiler data, damage type, heating surface parameters and damaged empirical parameters are obtained; Obtaining a damage mode according to the damage type, the heated surface parameter and the damaged empirical parameter; Analyzing the critical point of the change of the heating surface parameters according to the damage mode, obtaining the associated parameter threshold, and obtaining the early warning triggering condition according to the associated parameter threshold; A damage warning library is established according to the damage mode and the warning triggering condition.

3. The method according to claim 2, characterized in that The step of obtaining the damage mode according to the damage type, the heated surface parameter and the damaged empirical parameter comprises: constructing a transaction data set according to the damage type and the heating surface parameters; Calculating the frequency of transactions in the transaction data set according to an association rule mining strategy, and combining the transactions according to the frequency to obtain fault transactions; generating a frequent fault item set according to the frequency of the fault transaction; Obtaining a frequent failure weight according to the damaged experience parameter; Obtaining a high confidence parameter combination according to the frequent failure item set and the frequent failure weight; A damage mode is obtained according to the high confidence parameter combination.

4. The method according to claim 1, characterized in that The step of obtaining a boiler region and preset boundary conditions according to the boiler model, the historical data and the adaptive grid strategy, and constructing a fluid model and a solid model according to the boiler region and the preset boundary conditions comprises: Obtaining a heated area according to the boiler model, and obtaining a target boiler grid density according to the heated area and an adaptive grid strategy; Dividing the boiler area according to the target boiler grid density; Determining the interface and coupling mode between the fluid and the solid according to the historical data and the heated area; Using the interface and the coupling mode as preset boundary conditions; The thermal field parameters of the boiler area are obtained, and a fluid model and a solid model are constructed according to the thermal field parameters and the preset boundary conditions.

5. The method according to claim 4, characterized in that The step of obtaining the thermal field parameters of the boiler area and constructing a fluid model and a solid model according to the thermal field parameters and the preset boundary conditions includes: Acquiring thermal field parameters of the boiler region, wherein the thermal field parameters include fluid density, fluid velocity, fluid pressure, viscous stress tensor, total fluid energy, temperature, thermal conductivity, viscous dissipation, stress tensor, body force, displacement vector, and material density; Constructing a mass conservation model according to the fluid density and the fluid velocity; constructing a momentum conservation model according to the fluid pressure and the viscous stress tensor; constructing an energy conservation model according to the total energy of the fluid, the temperature, the thermal conductivity and the viscous dissipation; Constructing a fluid model according to the mass conservation model, the momentum conservation model, the energy conservation model and the preset boundary conditions; A solid model is constructed according to the stress tensor, the body force, the displacement vector, the material density and the preset boundary conditions.

6. The method according to claim 1, characterized in that The step of obtaining the warning result according to the current boiler heating parameters, the fluid model, the solid model and the damage warning library comprises: Performing bidirectional coupling calculation according to the current boiler heating parameters, the fluid model and the solid model to obtain a fluid-solid interaction result; According to the fluid-solid interaction results, the stress change rate, temperature fluctuation and deformation characteristics of the heated area of ​​the boiler are obtained; According to the damage mode of the damage early warning library, the stress change rate, temperature fluctuation and deformation characteristics are analyzed to obtain the current boiler condition; The current boiler condition is matched with the warning triggering condition of the damage warning library to obtain a warning matching result.

7. The method according to claim 6, characterized in that The step of performing bidirectional coupling calculation according to the current boiler heating parameters, the fluid model and the solid model to obtain the fluid-solid interaction result comprises: Calculate the fluid temperature and fluid pressure generated by the fluid on the solid structure surface of the boiler according to the current boiler heating parameters and the fluid model; Calculating solid stress and solid deformation according to the fluid temperature, the fluid pressure and the solid model; The fluid model is updated by using the solid stress and the solid deformation to obtain an updated fluid model; The fluid-solid interaction is calculated according to the updated fluid model and the solid model to obtain a fluid-solid interaction result.

8. A boiler fault early warning and diagnosis device, characterized in that: The device comprises: The acquisition module is used to obtain the main heating surface information, historical data and current boiler heating parameters of the boiler; An establishment module is used to establish a boiler model according to the main heating surface information and to establish a damage warning library according to the historical data; A construction module, used for obtaining a boiler region and preset boundary conditions according to the boiler model, the historical data and the adaptive grid strategy, and constructing a fluid model and a solid model according to the boiler region and the preset boundary conditions; The early warning module is used to obtain early warning results according to the current boiler heating parameters, the fluid model, the solid model and the damage early warning library, and perform safety diagnosis according to the early warning results.

9. A boiler fault warning and diagnosis device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the boiler fault early warning and diagnosis method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the boiler fault early warning and diagnosis method according to any one of claims 1 to 7 are implemented.

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