Heating surface fault diagnosis and prediction method and system based on boiler flow field analysis

Abstract

The invention relates to the technical field of boiler heating surface fault diagnosis, in particular to a heating surface fault diagnosis prediction method and system based on boiler flow field analysis. The method comprises the following steps: collecting boiler multi-dimensional data; analyzing a flow field in the boiler; constructing a heating surface fault diagnosis model; coupling the flow field simulation result and the fault diagnosis model to construct a fault prediction model; extracting features and reducing dimensions; performing fault mode identification by using a Gaussian mixture model; and calculating a comprehensive fault score and carrying out risk grading. Through fusion of flow field analysis, deep learning and multi-source data, the accuracy of fault diagnosis is remarkably improved, and the early warning time is remarkably shortened. Experimental results show that compared with a traditional method, the fault detection rate is increased to 96.7%, the false alarm rate is reduced to 3.2%, the early warning time is prolonged to 12.3 hours, and the diagnosis accuracy rate reaches 94.1%. The method provides powerful support for safe and efficient operation of the boiler, and has important engineering application value.

Description

Technical Field

[0001] The invention relates to the technical field of boiler heating surface fault diagnosis, and in particular to a heating surface fault diagnosis prediction method and system based on boiler flow field analysis. Background Art

[0002] With the rapid development of industrial automation and intelligent manufacturing, boilers are the core equipment of thermal systems, and their safe and stable operation is crucial to energy production and industrial processes. In recent years, data-driven fault diagnosis and prediction technologies have been widely used in the field of boiler operation and maintenance. Traditional fault diagnosis methods mainly rely on expert experience and simple threshold judgments, which are difficult to cope with complex operating conditions and multi-source heterogeneous data. With the advancement of computational fluid dynamics (CFD) and machine learning technology, fault diagnosis methods based on flow field analysis have gradually attracted attention. These methods can more accurately describe the working status and potential failure risks of the heating surface by constructing an internal flow field model of the boiler and combining it with real-time monitoring data. However, most of the current flow field analysis methods are limited to static simulations, which make it difficult to capture the dynamic characteristics of the boiler during operation, and lack comprehensive consideration of the multi-scale and multi-physical field coupling effects.

[0003] The existing boiler heating surface fault diagnosis and prediction technology still has some shortcomings. First, most methods only focus on a single physical quantity (such as temperature and pressure), ignoring the comprehensive influence of factors such as flow field distribution and chemical reaction on the state of the heating surface, resulting in deviations in the diagnosis results. Secondly, traditional fault prediction models often use linear or simple nonlinear methods, which are difficult to effectively capture the complex dynamic characteristics and potential failure modes of the boiler system. Furthermore, the existing systems generally lack the interpretability of the diagnosis results, making it difficult to provide intuitive and clear decision support for operation and maintenance personnel. In addition, most methods fail to effectively integrate historical fault data, expert knowledge and physical models, limiting the accuracy and reliability of diagnosis and prediction. Finally, the existing systems often show poor generalization and adaptability when facing new fault modes or changes in operating conditions. Therefore, it is urgent to develop an intelligent diagnosis and prediction method that can comprehensively consider flow field characteristics, multi-source data fusion, and nonlinear dynamic modeling to improve the accuracy, reliability and adaptability of boiler heating surface fault diagnosis and provide strong support for the safe and efficient operation of boilers. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that, firstly, most methods only focus on a single physical quantity (such as temperature, pressure), ignoring the comprehensive influence of factors such as flow field distribution and chemical reaction on the state of the heating surface, resulting in deviations in the diagnostic results. Secondly, traditional fault prediction models often use linear or simple nonlinear methods, which are difficult to effectively capture the complex dynamic characteristics and potential failure modes of the boiler system. Furthermore, the existing systems generally lack the interpretability of the diagnostic results, making it difficult to provide intuitive and clear decision support for operation and maintenance personnel. In addition, most methods fail to effectively integrate historical fault data, expert knowledge and physical models, limiting the accuracy and reliability of diagnostic predictions. Finally, the existing systems often exhibit poor generalization and adaptability when faced with new fault modes or changes in operating conditions.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for diagnosing and predicting heating surface faults based on boiler flow field analysis, which includes collecting boiler data, analyzing the internal flow field of the boiler, constructing a heating surface fault diagnosis model by capturing flow characteristics and heat transfer characteristics, and outputting historical data and environmental factors of fault occurrence;

[0008] The heating surface fault prediction model is constructed by coupling the flow field simulation results output by the boiler flow field analysis with the heating surface fault diagnosis model; the real-time monitoring data of the boiler heating surface fault prediction is output;

[0009] The characteristic information obtained from flow field analysis is combined with real-time monitoring data to build an intelligent diagnosis system, which can identify potential faults and provide preventive maintenance suggestions.

[0010] As a preferred solution of the heating surface fault diagnosis and prediction method based on boiler flow field analysis of the present invention, wherein: the collected boiler data includes flow field parameter data, heating surface state data, operation parameter data, environmental parameters, historical fault data, equipment state data, and maintenance records;

[0011] Flow field parameter data: velocity distribution, temperature distribution, pressure distribution, flue gas composition, the flue gas composition includes O2, CO2, NO x ;

[0012] Heating surface status data: tube wall temperature, tube wall thickness, surface corrosion degree, scaling condition;

[0013] Operation parameter data: boiler load, feed water flow, steam parameters, fuel input amount and properties, the steam parameters including temperature, pressure, flow;

[0014] Environmental parameters: ambient temperature, humidity, atmospheric pressure;

[0015] Historical fault data: past fault types, fault occurrence time, fault duration, and fault impact range;

[0016] Equipment status data: vibration data, noise level, equipment operating hours;

[0017] Maintenance records: regular inspection data, replacement parts information, cleaning records.

[0018] As a preferred solution of the heating surface fault diagnosis and prediction method based on boiler flow field analysis of the present invention, wherein: the boiler data is collected, the internal flow field of the boiler is analyzed, and the velocity distribution and temperature distribution of the fluid in the boiler are collected to provide basic data for the boiler heating surface analysis, and the internal flow field analysis of the boiler is constructed, which adopts the following expression:

[0019]

[0020] Where ρ is the smoke density, which is inferred from the smoke composition data; v is the velocity distribution, flow field parameters; P is the pressure distribution, flow field parameters; μ is the smoke viscosity, which is inferred from the fuel input and properties; c p is the specific heat capacity of flue gas, which is inferred from the flue gas composition data; T is the temperature distribution, flow field parameters; k is the thermal conductivity of flue gas, which is inferred from the fuel input and properties; S is the heat source term, which is inferred from the fuel input and boiler load data;

[0021] The flow field and temperature distribution analysis outputs detailed flow characteristics of flow velocity and temperature distribution, and serves as the input of the state change of the heating surface. Through the change of the heating surface tube wall thickness over time, the heat transfer characteristics of the boiler heating surface state and corrosion prediction and the boiler heating surface state and scaling prediction are output, and key parameters are provided for the construction of the heating surface fault diagnosis model. The closing thickness change equation combining the flow field temperature and chemical reaction is constructed, which uses the following expression:

[0022]

[0023] Where W is the tube wall thickness, the state data of the heating surface; k c is the corrosion constant, which is calculated from past failure type data; E c is the corrosion activation energy, which is calculated from the past fault type data; R is the gas constant; T is the temperature, the temperature distribution output in the previous step; C corrosive It is the concentration of corrosive substances, the flue gas components in the flow field parameters, and the flue gas components include O2, CO2, NO x ;k s is the scaling constant, which is calculated from the past fault type data; C scaling is the concentration of fouling substances, the flue gas composition in the flow field parameters;

[0024] The heating surface fault diagnosis model is constructed by capturing the flow characteristics and heat transfer characteristics, and the historical data and environmental factors of the fault occurrence are output; the coupling results of the flow field, temperature distribution and pipe wall thickness change are used as input variables, and the flow field, heating surface state, operating parameters, environmental parameters, historical fault data and maintenance records are used to construct the heating surface fault diagnosis model for real-time fault prediction, which uses the following expression:

[0025]

[0026] in, is the predicted fault state; X flow (t) is the flow field parameter, velocity distribution, temperature distribution, pressure distribution, and smoke composition; X heat (t) is the state parameter of the heating surface, including tube wall temperature, tube wall thickness, surface corrosion degree, and scaling condition; X run (t) is the operating parameters, boiler load, feed water flow, steam parameters, fuel input and properties; X env (t) is the environmental parameters, including ambient temperature, humidity, and atmospheric pressure; X hist (t) is the historical fault data, including the past fault type, fault occurrence time, fault duration, and fault impact range; X maint (t) is the maintenance record, including regular inspection data, replacement parts information, and cleaning records; Θ is the model parameter, which is optimized through training data.

[0027] As a preferred solution of the heating surface fault diagnosis and prediction method based on boiler flow field analysis of the present invention, wherein: the flow field simulation results output by the boiler flow field analysis are coupled with the heating surface fault diagnosis model to construct a heating surface fault prediction model; and real-time monitoring data of the fault prediction of the boiler heating surface is output;

[0028] By extracting the statistical characteristics of the multivariate time series data coupled with the flow field simulation results output by the boiler flow field analysis and the heating surface fault diagnosis model, the data are used as the input of the heating surface fault prediction model. The statistical characteristics such as the mean, variance, maximum and minimum values ​​of each variable are extracted through the sliding window to capture the change pattern of the data. The output feature vector will be used as the input of the next pattern recognition. The constructed heating surface fault prediction model uses the following expression:

[0029]

[0030] Among them, F i,j is the feature vector of the i-th variable in window j; X i,t is the value of the i-th variable at time point t; W is the window size; is the mean value of the i-th variable in window j.

[0031] As a preferred solution of the heating surface fault diagnosis and prediction method based on boiler flow field analysis of the present invention, wherein: the real-time monitoring data of the fault prediction of the heating surface of the boiler is output, and pattern recognition and fault pattern classification are constructed based on the real-time monitoring data of the fault prediction, and the feature vector output based on the heating surface fault prediction model is used as the input of pattern recognition, and the high-dimensional feature data is reduced in dimension, the main features are extracted, and the calculation complexity is reduced, wherein the dimension is reduced by nonlinear principal component analysis, nonlinear features are extracted, redundant features are reduced, main information is retained, and pattern recognition of nonlinear principal component analysis is constructed, which adopts the following expression:

[0032] z=σ(W2·σ(W1·x+b1)+b2)

[0033] Among them, z is the feature matrix after nonlinear dimensionality reduction; x is the original feature matrix; W1 and W2 are weight matrices; b1 and b2 are bias vectors; σ is a nonlinear activation function;

[0034] The reduced feature matrix will be used as the input for fault mode classification. The feature data will be classified through the Gaussian mixture model to identify complex fault modes and provide fault warnings. The Gaussian mixture model is used to classify the feature data and identify the fault mode. The following expression is used:

[0035]

[0036] Among them, P(X|λ) is the probability that the feature vector X belongs to a certain category; w i is the weight of the i-th Gaussian component; is the probability density function of the i-th Gaussian component, with a mean of μ i ; covariance is Σ i .

[0037] As a preferred solution of the heating surface fault diagnosis and prediction method based on boiler flow field analysis described in the present invention, wherein: the characteristic information obtained by flow field analysis is combined with real-time monitoring data to build an intelligent diagnosis system, and potential faults are identified through the intelligent diagnosis system, and preventive maintenance suggestions are provided; the fault mode classification results will be used in the intelligent diagnosis and prediction system to provide reliable fault mode identification, wherein a comprehensive fault score is calculated, the fault risk is quantified, the severity of the fault is judged by the comprehensive score, and corresponding maintenance measures are provided. The comprehensive fault score is used in the intelligent diagnosis and prediction system to determine the scope and priority of the maintenance measures. The fault score formula can be constructed using the following expression:

[0038]

[0039] Among them, S f is the comprehensive fault score; w iis the weight of the i-th failure mode; C i is the feature score of the i-th failure mode; n is the number of failure modes.

[0040] As a preferred solution of the heating surface fault diagnosis and prediction method based on boiler flow field analysis of the present invention, wherein: the characteristic information obtained by flow field analysis is combined with real-time monitoring data to build an intelligent diagnosis system, and potential faults are identified through the intelligent diagnosis system, and preventive maintenance suggestions are provided. The degree of fault risk is determined according to the fault score, and different maintenance strategies are adopted.

[0041] Low risk: When S f When the risk level of the warning level is less than the first threshold, the risk level of the warning level is judged to be low risk, and warning information is sent to relevant departments and the public that may be affected, and low-intensity boiler heating surface fault diagnosis is generated and displayed for risk awareness training;

[0042] Medium risk: When the first threshold ≤ S f <When the second threshold is reached, the risk level of the warning level is judged to be medium risk, and warning information is sent to relevant departments. A medium-intensity boiler heating surface fault diagnosis is generated and displayed for risk communication and emergency drills. At the same time, the emergency response plan is activated to prepare evacuation and rescue resources;

[0043] High risk: When the second threshold ≥ S f When the risk level of the warning level is judged to be high, an emergency warning is issued to all relevant departments and the public, and a high-intensity boiler heating surface fault diagnosis is generated and displayed for evacuation route planning and rescue strategy formulation. At the same time, the evacuation procedures and emergency response measures are immediately initiated.

[0044] In a second aspect, an embodiment of the present invention provides a heating surface fault diagnosis and prediction system based on boiler flow field analysis, which includes:

[0045] The acquisition module collects boiler data, analyzes the internal flow field of the boiler, builds a heating surface fault diagnosis model by capturing flow characteristics and heat transfer characteristics, and outputs historical data and environmental factors of fault occurrence;

[0046] The construction module constructs a heating surface fault prediction model by coupling the flow field simulation results output by the boiler flow field analysis with the heating surface fault diagnosis model; outputs the real-time monitoring data of the fault prediction of the boiler heating surface;

[0047] The prediction module uses the characteristic information obtained from flow field analysis and combines it with real-time monitoring data to build an intelligent diagnosis system, which identifies potential faults and provides preventive maintenance suggestions.

[0048] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the heating surface fault diagnosis and prediction method based on boiler flow field analysis as described in the first aspect of the present invention are implemented.

[0049] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the heating surface fault diagnosis and prediction method based on boiler flow field analysis as described in the first aspect of the present invention are implemented.

[0050] The beneficial effects of the present invention are: collecting boiler data and performing flow field analysis: by comprehensively collecting multi-dimensional data such as flow field parameters, heating surface status, and operating parameters, and combining the computational fluid dynamics model to perform boiler internal flow field analysis, an accurate description of the complex flow and heat transfer process inside the boiler is achieved. This step provides comprehensive and accurate basic data for subsequent fault diagnosis, significantly improving the reliability and accuracy of fault diagnosis.

[0051] Constructing a heating surface fault diagnosis model: Using flow characteristics and heat transfer characteristics, combined with historical fault data and environmental factors, a comprehensive fault diagnosis model is constructed. This model can capture the dynamic changes in the state of the boiler heating surface and realize the early identification of potential faults, thereby greatly improving the timeliness and accuracy of fault warning.

[0052] Coupling flow field simulation results with fault diagnosis model: By coupling the flow field simulation results with the fault diagnosis model, a more comprehensive and dynamic heating surface fault prediction model is constructed. This coupling method makes full use of flow field information and historical fault data, significantly improving the accuracy and robustness of the model for fault prediction under complex working conditions.

[0053] Feature extraction and dimensionality reduction: The sliding window method is used to extract the statistical features of time series data, and the dimension is reduced through nonlinear principal component analysis, which effectively captures the change pattern of the data and reduces the computational complexity. This step significantly improves the computational efficiency of the model, enables the system to process large amounts of data in real time, and provides the possibility for rapid fault diagnosis.

[0054] Build an intelligent diagnosis system: Use Gaussian mixture models to classify feature data, identify complex fault modes, and combine comprehensive fault scoring mechanisms to achieve quantitative assessment of fault risks. This method not only improves the accuracy of fault identification, but also provides intuitive and explainable decision support for operation and maintenance personnel, effectively improving the efficiency and pertinence of fault handling.

[0055] Risk grading and preventive maintenance recommendations: Based on the fault score, the risk is divided into three levels: low, medium, and high, and corresponding early warning and maintenance strategies are formulated for different risk levels. This hierarchical management method realizes the refined control of boiler operation risks, effectively reduces the equipment failure rate, optimizes the allocation of maintenance resources, and improves the overall operation and maintenance efficiency.

[0056] In summary, this invention has built a comprehensive, accurate and efficient boiler heating surface fault diagnosis and prediction system through a series of innovative steps such as multi-dimensional data acquisition, flow field analysis, nonlinear modeling, feature extraction and dimensionality reduction, and intelligent diagnosis. This system not only significantly improves the accuracy and timeliness of fault diagnosis, but also realizes the refined management of boiler operation risks, providing strong technical support for the safe and efficient operation of boilers, and has important practical value and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0058] Figure 1 It is a flow chart of the heating surface fault diagnosis and prediction method based on boiler flow field analysis;

[0059] Figure 2 Computer equipment diagram of the heating surface fault diagnosis and prediction method based on boiler flow field analysis. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0063] Example 1

[0064] Reference Figure 1-2 , which is the first embodiment of the present invention, and provides a method for diagnosing and predicting heating surface faults based on boiler flow field analysis, comprising:

[0065] S100: Collect boiler data, analyze the internal flow field of the boiler, build a heating surface fault diagnosis model by capturing flow characteristics and heat transfer characteristics, and output historical data and environmental factors of fault occurrence;

[0066] S101: The collected boiler data includes flow field parameter data, heating surface status data, operation parameter data, environmental parameters, historical fault data, equipment status data, and maintenance records;

[0067] Flow field parameter data: velocity distribution, temperature distribution, pressure distribution, flue gas composition, the flue gas composition includes O2, CO2, NO x ;

[0068] Heating surface status data: tube wall temperature, tube wall thickness, surface corrosion degree, scaling condition;

[0069] Operation parameter data: boiler load, feed water flow, steam parameters, fuel input amount and properties, the steam parameters including temperature, pressure, flow;

[0070] Environmental parameters: ambient temperature, humidity, atmospheric pressure;

[0071] Historical fault data: past fault types, fault occurrence time, fault duration, and fault impact range;

[0072] Equipment status data: vibration data, noise level, equipment operating hours;

[0073] Maintenance records: regular inspection data, replacement parts information, cleaning records.

[0074] S200: Constructing a heating surface fault prediction model by coupling the flow field simulation results output by the boiler flow field analysis and the heating surface fault diagnosis model; outputting the real-time monitoring data of the fault prediction of the boiler heating surface;

[0075] S201: The boiler data is collected, and the internal flow field of the boiler is analyzed. The velocity distribution and temperature distribution of the fluid in the boiler are collected to provide basic data for the boiler heating surface analysis, and the internal flow field analysis of the boiler is constructed, which adopts the following expression:

[0076]

[0077] Where ρ is the smoke density, which is inferred from the smoke composition data; v is the velocity distribution, flow field parameters; P is the pressure distribution, flow field parameters; μ is the smoke viscosity, which is inferred from the fuel input and properties; cp is the specific heat capacity of flue gas, which is inferred from the flue gas composition data; T is the temperature distribution, flow field parameters; k is the thermal conductivity of flue gas, which is inferred from the fuel input and properties; S is the heat source term, which is inferred from the fuel input and boiler load data;

[0078] The flow field and temperature distribution analysis outputs detailed flow characteristics of flow velocity and temperature distribution, and serves as the input of the state change of the heating surface. Through the change of the heating surface tube wall thickness over time, the heat transfer characteristics of the boiler heating surface state and corrosion prediction and the boiler heating surface state and scaling prediction are output, and key parameters are provided for the construction of the heating surface fault diagnosis model. The closing thickness change equation combining the flow field temperature and chemical reaction is constructed, which uses the following expression:

[0079]

[0080] Where W is the tube wall thickness, the state data of the heating surface; k c is the corrosion constant, which is calculated from past failure type data; E c is the corrosion activation energy, which is calculated from the past fault type data; R is the gas constant; T is the temperature, the temperature distribution output in the previous step; C corrosive It is the concentration of corrosive substances, the flue gas components in the flow field parameters, and the flue gas components include O2, CO2, NO x ;k s is the scaling constant, which is calculated from the past fault type data; C scaling is the concentration of fouling substances, the flue gas composition in the flow field parameters;

[0081] The heating surface fault diagnosis model is constructed by capturing the flow characteristics and heat transfer characteristics, and the historical data and environmental factors of the fault occurrence are output; the coupling results of the flow field, temperature distribution and pipe wall thickness change are used as input variables, and the flow field, heating surface state, operating parameters, environmental parameters, historical fault data and maintenance records are used to construct the heating surface fault diagnosis model for real-time fault prediction, which uses the following expression:

[0082]

[0083] in, is the predicted fault state; X flow (t) is the flow field parameter, velocity distribution, temperature distribution, pressure distribution, and smoke composition; X heat (t) is the state parameter of the heating surface, including tube wall temperature, tube wall thickness, surface corrosion degree, and scaling condition; X run (t) is the operating parameters, boiler load, feed water flow, steam parameters, fuel input and properties; X env (t) is the environmental parameters, including ambient temperature, humidity, and atmospheric pressure; X hist(t) is the historical fault data, including the past fault type, fault occurrence time, fault duration, and fault impact range; X maint (t) is the maintenance record, including regular inspection data, replacement parts information, and cleaning records; Θ is the model parameter, which is optimized through training data.

[0084] S:202: The flow field simulation results output by the boiler flow field analysis are coupled with the heating surface fault diagnosis model to construct a heating surface fault prediction model; and real-time monitoring data of fault prediction occurring on the boiler heating surface is output;

[0085] By extracting the statistical characteristics of the multivariate time series data coupled with the flow field simulation results output by the boiler flow field analysis and the heating surface fault diagnosis model, the data are used as the input of the heating surface fault prediction model. The statistical characteristics such as the mean, variance, maximum and minimum values ​​of each variable are extracted through the sliding window to capture the change pattern of the data. The output feature vector will be used as the input of the next pattern recognition. The constructed heating surface fault prediction model uses the following expression:

[0086]

[0087] Among them, F i,j is the feature vector of the i-th variable in window j; X i,t is the value of the i-th variable at time point t; W is the window size; is the mean value of the i-th variable in window j.

[0088] S203: The output of the real-time monitoring data of the fault prediction of the heating surface of the boiler is used to construct pattern recognition and fault pattern classification according to the real-time monitoring data of the fault prediction. The feature vector outputted by the heating surface fault prediction model is used as the input of pattern recognition to reduce the dimension of the high-dimensional feature data, extract the main features, and reduce the computational complexity. The nonlinear principal component analysis is used to reduce the dimension, extract the nonlinear features, reduce the redundant features, retain the main information, and construct the pattern recognition of the nonlinear principal component analysis. The following expression is used:

[0089] z=σ(W2·σ(W1·x+b1)+b2)

[0090] Among them, z is the feature matrix after nonlinear dimensionality reduction; x is the original feature matrix; W1 and W2 are weight matrices; b1 and b2 are bias vectors; σ is a nonlinear activation function;

[0091] The reduced feature matrix will be used as the input for fault mode classification. The feature data will be classified through the Gaussian mixture model to identify complex fault modes and provide fault warnings. The Gaussian mixture model is used to classify the feature data and identify the fault mode. The following expression is used:

[0092]

[0093] Among them, P(X|λ) is the probability that the feature vector X belongs to a certain category; w i is the weight of the i-th Gaussian component; is the probability density function of the i-th Gaussian component, with a mean of μ i ; covariance is Σ i .

[0094] S300: Utilizes the characteristic information obtained from flow field analysis and combines it with real-time monitoring data to build an intelligent diagnosis system, which identifies potential faults and provides preventive maintenance recommendations.

[0095] S301: The characteristic information obtained by flow field analysis is combined with real-time monitoring data to build an intelligent diagnosis system, identify potential faults through the intelligent diagnosis system, and provide preventive maintenance suggestions; the fault mode classification results will be used in the intelligent diagnosis prediction system to provide reliable fault mode identification, wherein a comprehensive fault score is calculated to quantify the fault risk, and the severity of the fault is judged by the comprehensive score, and corresponding maintenance measures are provided. The comprehensive fault score is used in the intelligent diagnosis prediction system to determine the scope and priority of the maintenance measures. The fault score formula can be constructed using the following expression:

[0096]

[0097] Among them, S f is the comprehensive fault score; w i is the weight of the i-th failure mode; C i is the feature score of the i-th failure mode; n is the number of failure modes.

[0098] S302: The characteristic information obtained by flow field analysis is combined with real-time monitoring data to build an intelligent diagnosis system, identify potential faults through the intelligent diagnosis system, and provide preventive maintenance suggestions. The degree of fault risk is determined according to the fault score, and different maintenance strategies are adopted.

[0099] Low risk: When S f When the risk level of the warning level is less than the first threshold, the risk level of the warning level is judged to be low risk, and warning information is sent to relevant departments and the public that may be affected, and low-intensity boiler heating surface fault diagnosis is generated and displayed for risk awareness training;

[0100] Medium risk: When the first threshold ≤ S f <When the second threshold is reached, the risk level of the warning level is judged to be medium risk, and warning information is sent to relevant departments. A medium-intensity boiler heating surface fault diagnosis is generated and displayed for risk communication and emergency drills. At the same time, the emergency response plan is activated to prepare evacuation and rescue resources;

[0101] High risk: When the second threshold ≥ S f When the risk level of the warning level is judged to be high, an emergency warning is issued to all relevant departments and the public, and a high-intensity boiler heating surface fault diagnosis is generated and displayed for evacuation route planning and rescue strategy formulation. At the same time, the evacuation procedures and emergency response measures are immediately initiated.

[0102] The following threshold values ​​can be used:

[0103] For the comprehensive fault score S f , we can set the following threshold range:

[0104] Low risk: S f <0.3

[0105] Medium risk: 0.3≤S f <0.7

[0106] High risk: S f ≥0.7

[0107] These thresholds are set based on the following considerations:

[0108] 0.3 as the low risk upper limit: This value is low, indicating that the system is in good operating condition and only requires routine monitoring and maintenance.

[0109] 0.7 is used as the high risk lower limit: This value is high, which means that the system has obvious abnormalities and requires immediate intervention.

[0110] Medium-risk range between 0.3 and 0.7: provides sufficient warning time for operation and maintenance personnel to take preventive maintenance measures.

[0111] This threshold setting enables refined hierarchical management of boiler operation risks, helps optimize maintenance resource allocation, and improves equipment reliability. The threshold can be dynamically adjusted based on actual operation data and expert experience to meet the needs of different types of boilers and operating environments.

[0112] Flow field analysis: refers to the use of computational fluid dynamics (CFD) methods to numerically simulate and analyze the fluid movement, heat transfer and chemical reaction processes inside the boiler.

[0113] Heating surface: refers to the surface in the boiler that directly contacts with high-temperature flue gas and exchanges heat, such as water-cooled walls, superheaters, etc.

[0114] Nonlinear principal component analysis: A nonlinear dimensionality reduction technique that can effectively extract nonlinear features in data.

[0115] Multi-source data fusion: By integrating multi-dimensional data such as flow field parameters, heating surface status, and operating parameters, a comprehensive description of the boiler's operating status is achieved, providing a rich information basis for fault diagnosis.

[0116] Flow field-fault model coupling: Combining the flow field simulation results with the fault diagnosis model fully utilizes the fluid dynamics information and significantly improves the accuracy of the model in predicting faults under complex working conditions.

[0117] Nonlinear feature extraction: Nonlinear principal component analysis is used for dimensionality reduction, which effectively captures the nonlinear features in the data and overcomes the limitations of traditional linear methods.

[0118] Intelligent diagnosis system: The Gaussian mixture model is used to identify fault patterns, combined with a comprehensive fault scoring mechanism to achieve accurate quantification of fault risks, providing a reliable basis for operation and maintenance decisions.

[0119] Through the above-mentioned innovative methods and key steps, the present invention constructs an intelligent and precise boiler heating surface fault diagnosis and prediction system, which significantly improves the accuracy, timeliness and explainability of fault diagnosis, and provides strong technical support for the safe and efficient operation of the boiler.

[0120] Furthermore, this embodiment also provides a heating surface fault diagnosis and prediction system based on boiler flow field analysis, comprising:

[0121] The acquisition module collects boiler data, analyzes the internal flow field of the boiler, builds a heating surface fault diagnosis model by capturing flow characteristics and heat transfer characteristics, and outputs historical data and environmental factors of fault occurrence;

[0122] The construction module constructs a heating surface fault prediction model by coupling the flow field simulation results output by the boiler flow field analysis with the heating surface fault diagnosis model; outputs the real-time monitoring data of the fault prediction of the boiler heating surface;

[0123] The prediction module uses the characteristic information obtained from flow field analysis and combines it with real-time monitoring data to build an intelligent diagnosis system, which identifies potential faults and provides preventive maintenance suggestions.

[0124] This embodiment also provides a computer device, which is suitable for the case of a heating surface fault diagnosis and prediction method based on boiler flow field analysis, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the heating surface fault diagnosis and prediction method based on boiler flow field analysis as proposed in the above embodiment.

[0125] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0126] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for diagnosing and predicting heating surface faults based on boiler flow field analysis as proposed in the above embodiment is implemented.

[0127] In summary, we collect boiler data and conduct flow field analysis: by comprehensively collecting multi-dimensional data such as flow field parameters, heating surface status, and operating parameters, and combining the computational fluid dynamics model to analyze the internal flow field of the boiler, we can achieve an accurate description of the complex flow and heat transfer process inside the boiler. This step provides comprehensive and accurate basic data for subsequent fault diagnosis, significantly improving the reliability and accuracy of fault diagnosis.

[0128] Constructing a heating surface fault diagnosis model: Using flow characteristics and heat transfer characteristics, combined with historical fault data and environmental factors, a comprehensive fault diagnosis model is constructed. This model can capture the dynamic changes in the state of the boiler heating surface and realize the early identification of potential faults, thereby greatly improving the timeliness and accuracy of fault warning.

[0129] Coupling flow field simulation results with fault diagnosis model: By coupling the flow field simulation results with the fault diagnosis model, a more comprehensive and dynamic heating surface fault prediction model is constructed. This coupling method makes full use of flow field information and historical fault data, significantly improving the accuracy and robustness of the model for fault prediction under complex working conditions.

[0130] Feature extraction and dimensionality reduction: The sliding window method is used to extract the statistical features of time series data, and the dimension is reduced through nonlinear principal component analysis, which effectively captures the change pattern of the data and reduces the computational complexity. This step significantly improves the computational efficiency of the model, enables the system to process large amounts of data in real time, and provides the possibility for rapid fault diagnosis.

[0131] Build an intelligent diagnosis system: Use Gaussian mixture models to classify feature data, identify complex fault modes, and combine comprehensive fault scoring mechanisms to achieve quantitative assessment of fault risks. This method not only improves the accuracy of fault identification, but also provides intuitive and explainable decision support for operation and maintenance personnel, effectively improving the efficiency and pertinence of fault handling.

[0132] Risk grading and preventive maintenance recommendations: Based on the fault score, the risk is divided into three levels: low, medium, and high, and corresponding early warning and maintenance strategies are formulated for different risk levels. This hierarchical management method realizes the refined control of boiler operation risks, effectively reduces the equipment failure rate, optimizes the allocation of maintenance resources, and improves the overall operation and maintenance efficiency.

[0133] In summary, this invention has built a comprehensive, accurate and efficient boiler heating surface fault diagnosis and prediction system through a series of innovative steps such as multi-dimensional data acquisition, flow field analysis, nonlinear modeling, feature extraction and dimensionality reduction, and intelligent diagnosis. This system not only significantly improves the accuracy and timeliness of fault diagnosis, but also realizes the refined management of boiler operation risks, providing strong technical support for the safe and efficient operation of boilers, and has important practical value and economic benefits.

[0134] Example 2

[0135] Reference Figure 1 - Figure 2 , which is the second embodiment of the present invention, provides a heating surface fault diagnosis and prediction method based on boiler flow field analysis. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0136] In order to verify the effectiveness of the heating surface fault diagnosis and prediction method based on boiler flow field analysis proposed in this invention, a 600MW unit boiler of a thermal power plant was selected as the research object and a 6-month experiment was carried out. The boiler is a supercritical pressure once-through boiler with a rated evaporation capacity of 1930t / h, a main steam pressure of 25.4MPa, and a main steam temperature of 571℃.

[0137] During the experimental preparation phase, a high-precision sensor network was first installed at key parts of the boiler, including temperature sensors, pressure sensors, flow meters, and flue gas analyzers. A total of 150 measurement points were arranged, covering major heating surface areas such as water-cooled walls, superheaters, and reheaters. At the same time, a data acquisition system based on edge computing was built to achieve millisecond-level data acquisition and preprocessing.

[0138] During the experiment, the system performed a complete data collection and analysis cycle every 5 minutes. First, the sensor data, DCS system data and historical operation data were integrated through multi-source data fusion technology. Then, the internal flow field of the boiler was analyzed using an improved computational fluid dynamics model, which takes into account complex factors such as multiphase flow, chemical reaction and radiation heat transfer, and the calculation accuracy is about 20% higher than that of the traditional CFD model.

[0139] Based on the results of flow field analysis, a heating surface fault diagnosis model is constructed. The model uses a deep learning algorithm, specifically a hybrid structure of long short-term memory network (LSTM) and convolutional neural network (CNN), which can capture both temporal and spatial features. The model training uses historical data from the past three years, including normal operation data and various fault cases, totaling about 5 million records.

[0140] To improve computational efficiency, the sliding window method is used to extract statistical features of time series data, with the window size set to 30 minutes and the step length set to 5 minutes. Then, nonlinear dimensionality reduction is performed using improved kernel principal component analysis (KPCA), which retains 95% of the information and reduces the feature dimension from the original 1500 dimensions to 50 dimensions.

[0141] In the fault pattern recognition stage, the deep Gaussian mixture model (DGMM) is used, which significantly improves the recognition ability of complex nonlinear fault patterns by introducing deep neural networks. At the same time, an interpretability mechanism based on Shapley value is designed to provide an intuitive explanation for each diagnostic result.

[0142] Finally, according to the comprehensive fault score S f , the risk level is divided into low risk (S f <0.3), medium risk (0.3≤S f <0.7 and high risk (S f ≥0.7). The system will automatically generate corresponding warning information and maintenance suggestions according to different risk levels, and present them to the operation and maintenance personnel through a visual interface.

[0143] In order to evaluate the performance of the method of the present invention, it is compared with the traditional threshold-based fault diagnosis method and the conventional machine learning method (support vector machine, SVM). The experimental results are shown in the following table:

[0144] Evaluation Metrics Traditional threshold method SVM Method Method of the present invention Fault detection rate (%) 78.5 89.3 96.7 False alarm rate (%) 15.2 8.7 3.2 Early warning time (hours) 2.5 5.8 12.3 Diagnostic accuracy (%) 72.3 85.6 94.1 Calculation time (seconds / time) 0.5 3.2 1.8

[0145] By analyzing the data in the above table, we can draw the following conclusions:

[0146] Fault detection rate: The fault detection rate of the method of the present invention reaches 96.7%, which is 18.2 percentage points higher than the traditional threshold method and 7.4 percentage points higher than the SVM method. This shows that the method can more comprehensively and accurately identify various types of faults, greatly reducing the occurrence of missed detection.

[0147] False alarm rate: The false alarm rate of the method of the present invention is only 3.2%, which is significantly lower than 15.2% of the traditional threshold method and 8.7% of the SVM method. This means that the method can effectively reduce unnecessary downtime and maintenance caused by false alarms, and improve the reliability and economy of the system.

[0148] Early warning time: The method of the present invention can issue a fault warning 12.3 hours in advance, which is 9.8 hours earlier than the traditional threshold method and 6.5 hours earlier than the SVM method. This provides sufficient time for operation and maintenance personnel to carry out preventive maintenance and greatly reduces the risk of sudden failures.

[0149] Diagnostic accuracy: The diagnostic accuracy of the method of the present invention reaches 94.1%, which is 21.8 percentage points higher than the traditional threshold method and 8.5 percentage points higher than the SVM method. This shows that the method can not only accurately detect faults, but also accurately determine the type and location of faults, providing precise guidance for maintenance work.

[0150] Computation time: Although the method of the present invention uses complex flow field analysis and deep learning algorithms, the single calculation time is controlled to 1.8 seconds through optimized feature extraction and dimensionality reduction technology, which is 43.8% faster than the SVM method. This ensures that the system can respond in real time and meet the needs of online monitoring.

[0151] In addition, the method of the present invention also shows excellent generalization ability and robustness. During the experiment, the system successfully predicted and diagnosed three new faults that had not appeared in the training data, providing valuable decision support for timely handling of these faults. At the same time, through the Shapley value interpretation mechanism, the system can provide a clear explanation for each diagnosis result, greatly improving the trust of operation and maintenance personnel in the system and decision-making efficiency.

[0152] In summary, the method of the present invention is significantly superior to traditional methods and conventional machine learning methods in key indicators such as fault detection rate, false alarm rate, early warning time, and diagnostic accuracy. By organically combining advanced technologies such as flow field analysis, deep learning, and multi-source data fusion, this method realizes the intelligent and precise diagnosis and prediction of boiler heating surface faults, providing strong technical support for the safe and efficient operation of boilers, and has important engineering application value.

[0153] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for diagnosing and predicting heating surface faults based on boiler flow field analysis, characterized in that: include, Collect boiler data, analyze the internal flow field of the boiler, build a heating surface fault diagnosis model by capturing flow characteristics and heat transfer characteristics, and output historical data and environmental factors of fault occurrence; The heating surface fault prediction model is constructed by coupling the flow field simulation results output by the boiler flow field analysis with the heating surface fault diagnosis model; the real-time monitoring data of the boiler heating surface fault prediction is output; The characteristic information obtained from flow field analysis is combined with real-time monitoring data to build an intelligent diagnosis system, which can identify potential faults and provide preventive maintenance suggestions.

2. The method for diagnosing and predicting heating surface faults based on boiler flow field analysis according to claim 1, characterized in that: The collected boiler data includes flow field parameter data, heating surface status data, operation parameter data, environmental parameters, historical fault data, equipment status data, and maintenance records; Flow field parameter data: velocity distribution, temperature distribution, pressure distribution, flue gas composition, the flue gas composition includes O2, CO2, NO x ; Heating surface status data: tube wall temperature, tube wall thickness, surface corrosion degree, scaling condition; Operation parameter data: boiler load, feed water flow, steam parameters, fuel input amount and properties, the steam parameters including temperature, pressure, flow; Environmental parameters: ambient temperature, humidity, atmospheric pressure; Historical fault data: past fault types, fault occurrence time, fault duration, and fault impact range; Equipment status data: vibration data, noise level, equipment operating hours; Maintenance records: regular inspection data, replacement parts information, cleaning records.

3. The method for diagnosing and predicting heating surface faults based on boiler flow field analysis according to claim 2, characterized in that: The boiler data is collected to analyze the internal flow field of the boiler. The velocity distribution and temperature distribution of the fluid in the boiler are collected to provide basic data for the boiler heating surface analysis, and the internal flow field analysis of the boiler is constructed, which adopts the following expression: Where ρ is the smoke density, which is inferred from the smoke composition data; v is the velocity distribution, flow field parameters; P is the pressure distribution, flow field parameters; μ is the smoke viscosity, which is inferred from the fuel input and properties; c p is the specific heat capacity of flue gas, which is inferred from the flue gas composition data; T is the temperature distribution, flow field parameters; k is the thermal conductivity of flue gas, which is inferred from the fuel input and properties; S is the heat source term, which is inferred from the fuel input and boiler load data; The flow field and temperature distribution analysis outputs detailed flow characteristics of flow velocity and temperature distribution, and serves as the input of the state change of the heating surface. Through the change of the heating surface tube wall thickness over time, the heat transfer characteristics of the boiler heating surface state and corrosion prediction and the boiler heating surface state and scaling prediction are output, and key parameters are provided for the construction of the heating surface fault diagnosis model. The closing thickness change equation combining the flow field temperature and chemical reaction is constructed, which uses the following expression: Where W is the tube wall thickness, the state data of the heating surface; k c is the corrosion constant, which is calculated from past failure type data; E c is the corrosion activation energy, which is calculated from the past fault type data; R is the gas constant; T is the temperature, the temperature distribution output in the previous step; C corrosive It is the concentration of corrosive substances, the flue gas components in the flow field parameters, and the flue gas components include O2, CO2, NO x ;k s is the scaling constant, which is calculated from the past fault type data; C scaling is the concentration of fouling substances, the flue gas composition in the flow field parameters; The heating surface fault diagnosis model is constructed by capturing the flow characteristics and heat transfer characteristics, and the historical data and environmental factors of the fault occurrence are output; the coupling results of the flow field, temperature distribution and pipe wall thickness change are used as input variables, and the flow field, heating surface state, operating parameters, environmental parameters, historical fault data and maintenance records are used to construct the heating surface fault diagnosis model for real-time fault prediction, which uses the following expression: in, is the predicted fault state; X flow (t) is the flow field parameter, velocity distribution, temperature distribution, pressure distribution, and smoke composition; X heat (t) is the state parameter of the heating surface, including tube wall temperature, tube wall thickness, surface corrosion degree, and scaling condition; X run (t) is the operating parameters, boiler load, feed water flow, steam parameters, fuel input and properties; X env (t) is the environmental parameters, including ambient temperature, humidity, and atmospheric pressure; X hist (t) is the historical fault data, including the past fault type, fault occurrence time, fault duration, and fault impact range; X maint (t) is the maintenance record, including regular inspection data, replacement parts information, and cleaning records; Θ is the model parameter, which is optimized through training data.

4. The method for diagnosing and predicting heating surface faults based on boiler flow field analysis according to claim 3, characterized in that: The flow field simulation results output by the boiler flow field analysis are coupled with the heating surface fault diagnosis model to construct a heating surface fault prediction model; and real-time monitoring data of the fault prediction of the boiler heating surface is output; By extracting the statistical characteristics of the multivariate time series data coupled with the flow field simulation results output by the boiler flow field analysis and the heating surface fault diagnosis model, the data are used as the input of the heating surface fault prediction model. The statistical characteristics such as the mean, variance, maximum and minimum values ​​of each variable are extracted through the sliding window to capture the change pattern of the data. The output feature vector will be used as the input of the next pattern recognition. The constructed heating surface fault prediction model uses the following expression: Among them, F i,j is the feature vector of the i-th variable in window j; X i,t is the value of the i-th variable at time point t; W is the window size; is the mean value of the i-th variable in window j.

5. The method for diagnosing and predicting heating surface faults based on boiler flow field analysis according to claim 4, characterized in that: The output is the real-time monitoring data of the fault prediction of the heating surface of the boiler, and the pattern recognition and fault pattern classification are constructed according to the real-time monitoring data of the fault prediction. The feature vector outputted by the heating surface fault prediction model is used as the input of the pattern recognition, and the high-dimensional feature data is reduced in dimension, the main features are extracted, and the calculation complexity is reduced. The nonlinear principal component analysis is used for dimensionality reduction, nonlinear features are extracted, redundant features are reduced, main information is retained, and pattern recognition of nonlinear principal component analysis is constructed, which adopts the following expression: z=σ(W2·σ(W1·x+b1)+b2) Among them, z is the feature matrix after nonlinear dimensionality reduction; X is the original feature matrix; W1 and W2 are weight matrices; b1 and b2 are bias vectors; σ is a nonlinear activation function; The reduced feature matrix will be used as the input for fault mode classification. The feature data will be classified through the Gaussian mixture model to identify complex fault modes and provide fault warnings. The Gaussian mixture model is used to classify the feature data and identify the fault mode. The following expression is used: Among them, P(X|λ) is the probability that the feature vector X belongs to a certain category; w i is the weight of the i-th Gaussian component; is the probability density function of the i-th Gaussian component, with a mean of μ i ; covariance is Σ i .

6. The method for diagnosing and predicting heating surface faults based on boiler flow field analysis according to claim 5, characterized in that: The characteristic information obtained by flow field analysis is combined with real-time monitoring data to build an intelligent diagnosis system, identify potential faults through the intelligent diagnosis system, and provide preventive maintenance suggestions; The results of fault mode classification will be used in the intelligent diagnosis and prediction system to provide reliable fault mode identification, in which the comprehensive fault score is calculated to quantify the fault risk, and the severity of the fault is judged by the comprehensive score, and the corresponding maintenance measures are provided. The comprehensive fault score is used in the intelligent diagnosis and prediction system to determine the scope and priority of the maintenance measures. The fault score formula can be constructed using the following expression: Among them, S f is the comprehensive fault score; w i is the weight of the i-th failure mode; C i is the feature score of the i-th failure mode; n is the number of failure modes.

7. The method for diagnosing and predicting heating surface faults based on boiler flow field analysis according to claim 6, characterized in that: The characteristic information obtained by flow field analysis is combined with real-time monitoring data to build an intelligent diagnosis system, which identifies potential faults and provides preventive maintenance suggestions. The degree of fault risk is determined according to the fault score, and different maintenance strategies are adopted. Low risk: When S f When the risk level of the warning level is less than the first threshold, the risk level of the warning level is judged to be low risk, and warning information is sent to relevant departments and the public that may be affected, and low-intensity boiler heating surface fault diagnosis is generated and displayed for risk awareness training; Medium risk: When the first threshold ≤ S f <When the second threshold is reached, the risk level of the warning level is judged to be medium risk, and warning information is sent to relevant departments. A medium-intensity boiler heating surface fault diagnosis is generated and displayed for risk communication and emergency drills. At the same time, the emergency response plan is activated to prepare evacuation and rescue resources; High risk: When the second threshold ≥ S f When the risk level of the warning level is judged to be high, an emergency warning is issued to all relevant departments and the public, and a high-intensity boiler heating surface fault diagnosis is generated and displayed for evacuation route planning and rescue strategy formulation. At the same time, the evacuation procedures and emergency response measures are immediately initiated.

8. A heating surface fault diagnosis and prediction system based on boiler flow field analysis, based on the heating surface fault diagnosis and prediction method based on boiler flow field analysis according to any one of claims 1 to 7, characterized in that: Also includes, The acquisition module collects boiler data, analyzes the internal flow field of the boiler, builds a heating surface fault diagnosis model by capturing flow characteristics and heat transfer characteristics, and outputs historical data and environmental factors of fault occurrence; The construction module constructs a heating surface fault prediction model by coupling the flow field simulation results output by the boiler flow field analysis with the heating surface fault diagnosis model; outputs the real-time monitoring data of the fault prediction of the boiler heating surface; The prediction module uses the characteristic information obtained from flow field analysis and combines it with real-time monitoring data to build an intelligent diagnosis system, which identifies potential faults and provides preventive maintenance suggestions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the heating surface fault diagnosis and prediction method based on boiler flow field analysis according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the heating surface fault diagnosis and prediction method based on boiler flow field analysis according to any one of claims 1 to 7 are implemented.

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