Early warning method and system for blockage of four pipes of boiler by foreign matters

Abstract

The invention belongs to the technical field of boiler maintenance, and particularly relates to a boiler four-pipe foreign matter blockage early warning method and system. The method comprises the steps of obtaining boiler four-tube foreign matter blockage historical data; collecting boiler four-tube real-time state data of a reheater, an induced draft fan and a flame detection cooling fan; a fault mode-influence-risk mapping relation is established by adopting FMEA based on historical data, and a risk assessment framework is formed; generating an initial risk level; training a machine learning algorithm model by adopting historical data and generating a blockage probability; and dynamically adjusting the initial risk level according to the blockage probability, generating a final risk level and outputting a corresponding maintenance strategy. The system comprises a data acquisition module, an equipment monitoring module, a risk division module, a risk assessment module, a jam prediction module and a decision module. According to the scheme, the FMEA and the machine learning algorithm are fused, early warning, accurate risk assessment and dynamic maintenance decision making of boiler foreign matter blockage are achieved, and boiler operation safety and maintenance efficiency are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of boiler maintenance, and in particular relates to a method and system for early warning of foreign matter blockage in four boiler pipes. Background Art

[0002] In thermal power generation, the four boiler tubes (water-wall tubes, superheater tubes, reheater tubes, and economizer tubes) are core components for converting thermal energy into mechanical energy. Their operating status directly impacts the safety and economic viability of power plants. These tubes are subject to long-term exposure to harsh environments characterized by high temperatures (500°C–1200°C), high pressures (10MPa–30MPa), and dust-laden flue gas. These tubes are susceptible to blockage due to foreign matter, such as flaking oxide scale, residual welding slag, and fly ash deposits. This can lead to failures such as overheating and tube bursts, and decreased heat transfer efficiency. According to industry statistics, four-tube failures account for over 60% of boiler accidents, resulting in unplanned power plant downtime exceeding 1 billion yuan annually. Therefore, early warning of four-tube blockage caused by foreign matter has become a critical technical challenge that the industry urgently needs to address. Traditional monitoring methods rely on regular manual inspections or single parameter threshold alarms, such as triggering an alarm when the temperature exceeds the design value. These methods fail to capture the early, progressive characteristics of blockage. While existing systems employ infrared thermal imagers and differential pressure sensors, they lack effective feature correlation algorithms. The localized high-temperature areas detected by the infrared thermal imager and the resistance changes monitored by the differential pressure sensor cannot be automatically correlated, resulting in a false alarm rate as high as 40%. Traditional FMEA-based risk assessment methods use static thresholds to categorize risk levels, which cannot adapt to dynamic operating conditions such as load fluctuations and changes in coal types. This ultimately leads to a lack of accurate identification and intelligent early warning. Summary of the Invention

[0003] The present invention provides a boiler four-tube foreign body blockage early warning method and system to solve the technical problems in the prior art of boiler foreign body blockage early warning difficulty, insufficient multi-parameter correlation analysis, and poor adaptability of dynamic risk assessment.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: A boiler four-pipe foreign body blockage early warning method comprises the following steps: Obtain historical data on foreign matter blockage in four boiler pipes; Collect real-time status data of the four boiler tubes; Based on the historical data of foreign matter blockage in the four boiler tubes, FMEA was used to establish a mapping relationship between failure mode, effect, and risk, divide the risk levels, and form a risk assessment framework. Using the risk assessment framework, an initial risk level is generated based on the real-time status data of the four boiler tubes; The machine learning algorithm model is trained using historical data of foreign body blockage of the boiler's four tubes. The trained machine learning algorithm model is then used to process the real-time status data of the boiler's four tubes to generate a blockage probability. According to the congestion probability, the initial risk level is dynamically adjusted to generate the final risk level, and corresponding warnings are issued based on the final risk level.

[0005] The real-time collection of boiler four-tube status data is specifically as follows: the boiler four tubes include water-cooled wall tubes, superheater tubes, reheater tubes and economizer tubes; the temperature distribution data of the water-cooled wall tubes, superheater tubes, reheater tubes and economizer tubes are monitored in real time by an infrared thermal imager; and the pressure difference data of the water-cooled wall tubes, superheater tubes, reheater tubes and economizer tubes are monitored in real time by a pressure difference sensor.

[0006] Based on the historical data of foreign body blockage in the four tubes of the boiler, FMEA is used to establish a mapping relationship between failure mode, effect and risk, and form a risk assessment framework. Specifically, according to the historical data of foreign body blockage in the four tubes of the boiler, the analysis objects are sorted out according to the system-subsystem-equipment-component architecture, and the boundaries of each level are clarified. According to the system-subsystem-equipment-component architecture, the failures caused by foreign body blockage are analyzed from bottom to top. According to the analysis results, the risks of different failures caused by foreign body blockage are evaluated, and a mapping between different failures and risks is formed. Then, the risk levels are divided to form a risk assessment framework.

[0007] The risk level division is specifically as follows: based on the evaluation of different faults caused by foreign body blockage, the degree of harm caused by the fault consequences of the foreign body fault is evaluated, and the severity is calculated; by evaluating the probability of occurrence of the fault caused by the foreign body fault, the frequency of occurrence is calculated; by evaluating the difficulty of detecting the fault caused by the foreign body fault, the undetectability is calculated; based on the calculated severity, frequency of occurrence, and undetectability, the risk priority of the fault caused by the foreign body blockage is calculated, and the risk level is divided according to the level.

[0008] Based on the risk levels, FMEA sets status data threshold ranges corresponding to different risk levels under various fault modes for the four boiler tubes. The real-time status data of the four boiler tubes are compared with the status data threshold ranges corresponding to different risk levels under various fault modes set by FMEA, and the corresponding fault modes are matched. The risk level corresponding to the matching fault is then retrieved to generate the initial risk level.

[0009] The machine learning algorithm model is trained using historical data of foreign body blockage of the four boiler tubes. The machine learning algorithm model generates a blockage probability based on the real-time status data of the four boiler tubes. Specifically, an LSTM neural network model is used to extract key feature data from the status monitoring data from the historical data of foreign body blockage of the four boiler tubes, calculate the blockage probability corresponding to different key feature data, and form a feature-blocking probability mapping relationship; and a pre-trained LSTM neural network model is used to generate a blockage probability corresponding to the real-time equipment status based on the feature-blocking probability mapping relationship in the LSTM neural network model according to the real-time status data of the four boiler tubes.

[0010] The jam probability corresponding to the generated real-time equipment status is analyzed based on the Bayesian optimization algorithm and the data in the risk assessment framework formed by FMEA to analyze the difference between the probability generated by the machine learning algorithm model and the actual risk. The model output probability is dynamically adjusted according to the difference. After the adjustment is completed, the Bayesian optimization algorithm is used again to evaluate the difference between the adjusted probability and the actual risk, and the optimization is continuously iterated until the probability output by the adjusted model reaches the optimal state.

[0011] The method of dynamically adjusting the initial risk level according to the congestion probability to generate the final risk level is as follows: based on each risk level in the risk assessment framework, for different congestion probabilities, dynamically correcting the calculation indicators in the risk assessment, and then adjusting the risk level to generate the final risk level.

[0012] The foreign object blockage inspection strategy is determined by risk level, which is divided into three risk levels: low, medium, and high. Three warning modes are set for each risk level. For low risk, the monitoring interface displays a corresponding risk pop-up window; for medium risk, a yellow warning light and a risk pop-up window appear; for high risk, a red sound and light alarm is triggered, and a risk pop-up window appears.

[0013] A boiler four-tube foreign body blockage early warning system includes a data acquisition module, an equipment monitoring module, a risk classification module, a risk assessment module, a blockage prediction module and an early warning module; The data acquisition module is used to obtain historical data of foreign matter blockage in the four pipes of the boiler; The equipment monitoring module is used to collect real-time status data of the four boiler tubes; The risk classification module is used to establish a failure mode-effect-risk mapping relationship based on the historical data of foreign body blockage of the four boiler tubes using FMEA, classify the risk levels, and form a risk assessment framework; The risk assessment module is used to generate an initial risk level based on the real-time status data of the four boiler tubes using a risk assessment framework; The blockage prediction module is used to train a machine learning algorithm model using historical data of foreign body blockage of the four boiler tubes, and use the trained machine learning algorithm model to process the real-time status data of the four boiler tubes to generate a blockage probability; The early warning module is used to dynamically adjust the initial risk level according to the congestion probability, generate a final risk level, and issue a corresponding early warning based on the final risk level.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention discloses a four-tube foreign body blockage early warning method for boilers. It uses an infrared thermal imager and a differential pressure sensor to collect real-time temperature distribution and differential pressure data from the four tubes. Combined with an LSTM neural network, it extracts dynamic features such as temperature gradients and differential pressure change rates. This method overcomes the limitations of traditional single-parameter monitoring and captures weak abnormal signals in the early stages of blockage. By mining temporal correlations between data, it transforms multi-dimensional monitoring data into quantifiable blockage risk indicators, significantly improving fault identification sensitivity. A "failure mode-effect-risk" mapping framework based on FMEA is established, and risk levels are classified based on historical data, providing structured knowledge support for risk assessment. Simultaneously, a machine learning algorithm is used to dynamically calculate the blockage probability, addressing the shortcomings of static FMEA assessments. The two algorithms are calibrated to each other, preserving the reliability of expert experience while enhancing the assessment model's adaptability to complex operating conditions and reducing false positives and false negatives. A Bayesian optimization algorithm is used to iteratively adjust the machine learning model output, dynamically revising the initial FMEA risk level based on real-time blockage probability, forming a dynamic assessment mechanism combining "data-driven + rule-constrained." Differentiated warnings are triggered based on the final risk level, achieving closed-loop management of the entire risk identification and warning process, improving operational efficiency and scientific decision-making. The warning system utilizes a modular design, with each module functioning independently and working collaboratively, facilitating system expansion and maintenance. This architecture is not only applicable to four-tube boilers but can also be applied to other industrial equipment fault warning scenarios, demonstrating its high versatility and technical applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 : A schematic flow chart of a boiler four-tube foreign body blockage early warning method according to the present invention; Figure 2 : A schematic diagram of a boiler four-tube foreign body blockage early warning system module of the present invention. DETAILED DESCRIPTION

[0016] In order to further understand the content of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0017] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0018] This embodiment proposes a boiler four-tube foreign body blockage early warning method, such as Figure 1 As shown, the following steps are included: Obtain historical data on foreign matter blockage in four boiler pipes; Collect real-time status data of the four boiler tubes including the reheater, induced draft fan, and fire inspection cooling fan; Based on the historical data of foreign matter blockage in the four boiler tubes, FMEA was used to establish a mapping relationship between failure mode, effect and risk, forming a risk assessment framework. Using the risk assessment framework, an initial risk level is generated based on the real-time status data of the four boiler tubes; The machine learning algorithm model is trained using historical data on foreign body blockage of the boiler's four tubes. The machine learning algorithm model generates blockage probabilities based on the real-time status data of the boiler's four tubes. According to the blockage probability, the initial risk level is dynamically adjusted to generate the final risk level, and the foreign body blockage maintenance strategy corresponding to the risk level is output.

[0019] Based on the above-mentioned boiler four-tube foreign body blockage early warning method, its specific implementation method is as follows: Through the equipment operation logs, maintenance and fault record data, and DCS (Distributed Control System) historical data in the boiler plant, we collected non-stop events and maintenance records related to foreign body blockage in the boiler four-tube system over the past five years, focusing on: historical status data of the boiler four-tube and data on failure modes caused by blockage; the historical status data of the boiler four-tube specifically includes: historical temperature difference data, historical pressure difference data and historical operating parameters of the water-cooled wall tubes, superheater tubes, reheater tubes and economizer tubes; the specific parameters of the water-cooled wall tube temperature difference data include: the temperature difference of the working medium at the inlet and outlet of the water-cooled wall, the temperature deviation between adjacent water-cooled wall tube rows, and the deviation of the water-cooled wall tube wall temperature from the design value; the specific parameters of the water-cooled wall tube pressure difference data include: the pressure difference between the lower and upper water-cooled wall headers and the pressure difference distribution of the water-cooled walls of each circuit; the operating parameter data of the water-cooled wall tubes include: boiler compliance, temperature and burner commissioning mode. The specific temperature difference data for superheater tubes includes: the difference in steam temperature between the superheater inlet and outlet, the temperature difference between each superheater stage, and the steam temperature deviation between the left and right superheaters. The specific pressure difference data for superheater tubes includes: the superheater inlet and outlet pressure difference and the pressure difference distribution between each tube group. The superheater operating parameters include: main steam flow rate, desuperheating water volume, and furnace outlet flue gas temperature. The specific temperature difference data for reheater tubes includes: the difference in steam temperature between the reheater inlet and outlet, the temperature deviation between reheater tube bundles, and the deviation of reheated steam temperature from the design value. The specific pressure difference data for reheater tubes includes: the reheater inlet and outlet pressure difference and the pressure difference before and after the reheater bypass valve. The operating parameters for reheater include: reheated steam flow rate, high-pressure cylinder exhaust temperature, and flue gas damper opening. The temperature difference data of the economizer tube are specifically: the water temperature difference at the inlet and outlet of the economizer tube and the temperature difference at the inlet and outlet of the economizer flue gas; the pressure difference data of the economizer tube are specifically: the water pressure difference at the inlet and outlet of the economizer and the pressure difference data of the economizer ash hopper; the operating parameters of the economizer tube include: feed water flow, exhaust gas temperature and dust collector inlet and outlet pressure difference.

[0020] The above data are used to form a historical data set of foreign body blockage of the four pipes of the boiler. The above historical data of foreign body blockage of the four pipes of the boiler are recorded using a standardized template to record the fault information: fault location, fault type, foreign body type, impact consequences, treatment measures and failure mode.

[0021] Based on the historical data of foreign matter blockage in the four-tube boiler, FMEA (Failure Mode and Effects Analysis) was used to identify the key levels and components of the four-tube boiler system according to the system → subsystem → equipment → component hierarchy. Specifically, the water-wall tube structure hierarchy is: boiler system → steam-water system → water-wall tube → lower header / upper header / vertical tube screen. The failure modes of its key components include: lower header / upper header: blockage by residual welding slag, rust accumulation, and foreign matter left over from construction; vertical tube screen: local blockage caused by oxide scale shedding, falling coke blocks, and water slag deposition; elbow area: reduced flow area due to the read chess game. The superheater tube hierarchy is: boiler system → steam-water system → superheater tubes → low-temperature section tube bundle / high-temperature section tube bundle. Key component failure modes include: low-temperature section tube bundle: fly ash deposition, foreign matter blockage, and tube scaling; high-temperature section tube bundle: oxide scale shedding, welding slag residue, and pipe deformation; desuperheater: nozzle blockage leading to temperature regulation failure. The reheater tube hierarchy is: boiler system → steam-water system → reheater tubes → high-temperature section U-bend. Key component failure modes include: high-temperature section U-bend: oxide scale shedding, welding slag residue, and foreign matter left over from construction; low-temperature section straight pipe: fly ash deposition and pipe deformation; bypass valve: foreign matter stuck, resulting in poor valve closure. The economizer tube hierarchy is: boiler system → steam-water system → economizer tubes → inlet header / coiled tube bank. Key component failure modes include: inlet header: accumulation of foreign matter such as welding slag, rust, and water slag; coiled tube bank: fly ash deposition, blockage by low-temperature corrosion products, and scaling within the tubes; outlet header: partial blockage caused by detached foreign matter. Following a system-subsystem-equipment-component architecture, the impact of foreign matter blockage on the four boiler tubes was analyzed from the bottom up. Specifically, in the water-wall tubes, at the component level, partial blockage leads to uneven working fluid flow and increased tube wall temperature. At the equipment level, overall water-wall thermal efficiency decreases, water circulation deteriorates, and film boiling may occur. At the subsystem level, pressure fluctuations in the steam-water system can cause reduced steam quality. At the system level, boiler output decreases, and in severe cases, overtemperature protection shutdowns are triggered. In the superheater, at the component level, local blockages can lead to uneven steam flow and tube wall overheating. At the equipment level, superheater outlet temperature deviations can increase, reducing metal material strength. At the subsystem level, abnormal turbine inlet steam parameters can cause efficiency reduction. At the system level, unit load can be limited, heat rate can increase, and long-term overheating can cause tube bursts. In the reheater, at the component level, elbow blockages can increase local resistance and reduce steam flow. At the equipment level, the reheater inlet and outlet pressure differentials can increase, leading to uneven temperature distribution. At the subsystem level, abnormal turbine intermediate pressure cylinder inlet steam parameters can cause efficiency reduction. At the system level, unit thermal efficiency can decrease, potentially triggering a protective shutdown.In the economizer, the component level: the blockage in the tube increases the water flow resistance, the tube wall temperature rises, the equipment level: the pressure difference between the inlet and outlet of the economizer increases, the feedwater preheating is insufficient, the subsystem level: the overall thermal efficiency of the boiler decreases, the exhaust gas temperature rises, the system level: the coal consumption increases, and long-term operation may cause the economizer to explode.

[0022] According to the above analysis of the influence of different faults caused by foreign matter blockage in the boiler four tubes, the risk priority of the fault caused by foreign matter blockage in the water-cooled wall tube, the superheater tube, the reheater tube and the economizer tube in the boiler four tubes is calculated. The risk priority calculation includes three indexes of severity, occurrence frequency and un-detectability. The severity S is calculated by evaluating the harm degree of the fault consequence caused by foreign matter fault; the occurrence frequency O is calculated by evaluating the occurrence probability of the fault caused by foreign matter fault; and the un-detectability D is calculated by evaluating the difficulty of the fault caused by foreign matter fault being found. In the severity S evaluation, if the equipment has slight abnormalities and does not affect the unit operation, it is slightly affected; if the load reduction operation is required and the equipment needs to be overhauled, it is moderately affected; and if the non-scheduled shutdown, equipment scrap or safety accident occurs, it is a catastrophic impact. In the occurrence frequency O evaluation, if the fault occurs once in more than 10 years, it is rare; if the fault occurs once in 1-10 years, it is occasional; and if the fault occurs more than once in 1 year, it is frequent. In the un-detectability D evaluation, if the fault can be found in real time by conventional monitoring, it is easy to detect; if the fault needs to be found by special inspection, it is moderately difficult to detect; and if there is no obvious sign before the fault occurs, it is difficult to detect. According to the above evaluation rules, the severity S, the occurrence frequency O and the un-detectability D are evaluated, and the risk priority RPN of the fault caused by foreign matter blockage is calculated according to the severity S, the occurrence frequency O and the un-detectability D, and the calculation formula is: , the value range is 1-1000, and the higher the value of the risk priority RPN of the fault caused by foreign matter blockage, the higher the risk level. According to the risk priority RPN, the risk level is divided, and the specific is: low risk: RPN<100; medium risk: 100≤RPN<200; high risk: RPN≥200. Through the above analysis of the fault caused by foreign matter blockage and the risk level calculation, the mapping of different faults and risks is established, and the risk assessment framework is formed.

[0023] The temperature distribution data of the water cooling wall pipe, the superheater pipe, the reheater pipe and the coal economizer pipe are monitored in real time by an infrared thermal imager; the pressure difference data of the water cooling wall pipe, the superheater pipe, the reheater pipe and the coal economizer pipe are monitored in real time by a pressure difference sensor. The temperature data adopts the infrared thermal imager, the infrared thermal imager selects a high-temperature-resistant and high-resolution industrial-grade infrared thermal imager, a plurality of infrared thermal imagers are installed on the top of the furnace of the water cooling wall pipe along the width direction, the temperature distribution of the vertical pipe screen is monitored from above, and the corresponding area of the burner and the elbow joint are mainly covered; a support is installed on the side of the lower header and the upper header of the water cooling wall pipe, and one thermal imager is arranged on each support to detect the temperature anomaly of the interface between the header and the pipe. An infrared thermal imager is installed on the top of the horizontal flue of the superheater, the pipe bundle in the low-temperature section and the high-temperature section is scanned in layers, and the temperature of the pipe before and after the temperature reducer is mainly monitored; one thermal imager is installed on the side of the inlet and outlet pipes of the superheater, and the temperature change of the inlet and outlet areas is monitored. An infrared thermal imager is installed above the U-shaped elbow of the high-temperature section of the reheater, and a certain inclination angle is arranged, so as to monitor the temperature distribution inside the elbow; thermal imagers are installed on the side of the straight pipe area of the low-temperature section of the reheater at intervals of 3-5 meters, and the whole pipe bundle area is covered. 2-3 thermal imagers are installed on the side wall of the flue above the pipe row of the coal economizer, which are distributed in a fan shape to ensure that the whole pipe row is covered; one thermal imager is installed on the side of the inlet and outlet headers of the coal economizer to monitor the temperature of the connection part between the header and the pipe. The pressure difference sensor adopts a high-precision and corrosion-resistant differential pressure transmitter, and the specific installation position is as follows: one pressure difference sensor is vertically installed on the main pipe between the lower header and the upper header of the water cooling wall to measure the overall pressure difference between the headers; pressure difference sensors are respectively installed on the branch pipes of each circuit water cooling wall close to the header interface to monitor the pressure difference distribution between the circuits. The pressure difference sensors are horizontally installed on the inlet and outlet main pipes of the superheater to measure the pressure difference between the inlet and outlet, and the installation position is at least 5 times the pipe diameter away from the elbow; pressure difference sensors are respectively installed on the inlet and outlet pipes of each pipe group to monitor the pressure difference difference between the pipe groups. The pressure difference sensors are vertically installed on the inlet and outlet main pipes of the reheater to obtain the pressure difference data between the inlet and outlet; one pressure difference sensor is respectively installed on the pipes before and after the bypass valve of the reheater to monitor the sealing property of the bypass valve and the pressure difference change of the bypass pipe. The pressure difference sensors are horizontally installed on the inlet and outlet main pipes of the coal economizer to measure the water pressure difference between the inlet and outlet of the coal economizer; pressure sensors are respectively installed on the top and bottom of the ash bucket of the coal economizer to obtain the pressure difference of the ash bucket by calculating the difference value of the two, so as to judge the ash blockage. For the above data, sliding average filtering algorithm and other algorithms are used to eliminate abnormal values of the sensors, and parameters of different ranges are normalized to the interval [0, 1] for subsequent analysis.

[0024] Based on the risk level classification, the FMEA set threshold ranges for the temperature and pressure differential data of the water-wall, superheater, reheater, and economizer tubes, corresponding to different risk levels under each fault mode. The real-time collected temperature and pressure differential data for the water-wall, superheater, reheater, and economizer tubes were compared with the threshold ranges for the different risk levels under each fault mode set by the FMEA. The corresponding fault mode was matched, and the corresponding risk level was retrieved to generate an initial risk level. An LSTM neural network model was trained using historical data on foreign matter blockage of four boiler tubes. Key features were extracted from the condition monitoring data, and the blockage probabilities corresponding to different key features were calculated, forming a feature-blockage probability mapping relationship. During the training process, hyperparameters were adjusted using 5-fold cross-validation to avoid overfitting. The real-time status data of the four boiler tubes was input into the trained LSTM neural network model. Based on the feature-blockage probability mapping relationship in the LSTM neural network model, the blockage probabilities corresponding to different real-time status data of the four boiler tubes were generated.

[0025] As a further optimization solution in this embodiment, the congestion probability output by the LSTM neural network model is calibrated using a Bayesian optimization algorithm, combined with a risk assessment framework developed through FMEA analysis. Bayesian optimization is a global optimization method used to optimize the objective function. Its mathematical theory relies on Bayesian inference, Gaussian process modeling, and the definition and optimization of acquisition functions. In the probability calibration scenario, the raw probabilities output by the model are considered the objective function values ​​to be optimized. Through Bayesian inference, all known information about the model output probabilities is summarized into a posterior distribution. Based on this posterior distribution, the acquisition function is used to select the next sampling point to minimize the objective function, bringing the congestion probability output by the model closer to the true probability. In practice, the objective function is assumed to follow a Gaussian process distribution, defined by its mean and covariance functions, such as the commonly used RBF kernel function. This is used to estimate the mean and uncertainty of the objective function, providing a basic model framework for probability calibration. FMEA uses three ratings—severity (S), frequency (O), and undetectability (D)—to quantify and prioritize risks, thereby determining the risk level. The output is the relative risk of each failure mode, expressed as the risk priority number (RPN): RPN = S × O × D. A higher RPN value indicates a higher risk level. These historical risk level assessments serve as an important basis for probability calibration, reflecting the inherent risk level of the failure mode and guiding the direction and magnitude of adjustments to model output probabilities. For example, if an FMEA failure mode has an RPN ≥ 200, the model probability is automatically increased by 10%-20% based on the rules. During the actual calibration process, the original model output probability is first analyzed using a Bayesian optimization algorithm. By continuously sampling and updating the posterior distribution, the discrepancy between the model probability and the true probability is determined. Then, combined with historical FMEA risk level information, when a high-risk failure mode with an RPN ≥ 200 is encountered, the model output probability is adjusted upward according to the pre-set rules. After the adjustment is completed, the Bayesian optimization algorithm is used again to evaluate the closeness between the calibrated probability and the true probability, and the optimization is continuously iterated until the calibrated probability reaches a relatively ideal state, which can more accurately reflect the true possibility of a fault occurring.

[0026] After the congestion probability is output by the LSTM neural network model, the initial risk level is dynamically adjusted based on the congestion probability to generate a final risk level. Specifically, based on each risk level in the risk assessment framework, the calculated indicators in the risk assessment are dynamically modified for different congestion probabilities, and the risk level is adjusted to generate the final risk level. Adjustment rules are formulated based on the congestion probability. If the congestion probability is less than 10%, the initial risk level is low; if the congestion probability is between 10% and 30%, the initial risk level is medium; and if the congestion probability is greater than 30%, the initial risk level is high. If the initial risk level is inconsistent with the risk level derived from the congestion probability, an adjustment is required. In addition to the congestion probability, the initial risk level can also be fine-tuned based on factors such as the impact on the equipment or system after the risk event occurs and the urgency of the event. The final risk level is determined by combining the congestion probability and the aforementioned adjustment factors. If the congestion probability is 40% and the congestion will cause significant economic losses, the final risk level is determined to be high after adjustment. Risk levels are categorized into three levels: low, medium, and high. Three warning types are set for each of these three risk levels. When the risk is low, the monitoring interface displays the corresponding risk pop-up window; when the risk is medium, a yellow warning light and a risk pop-up window are displayed; when the risk is high, a red sound and light alarm is triggered and a risk pop-up window is displayed.

[0027] Example 2 like Figure 2 As shown, based on the boiler four-tube foreign body blockage warning method proposed in the above-mentioned embodiment 1, this embodiment proposes a boiler four-tube foreign body blockage warning system, including a data acquisition module, an equipment monitoring module, a risk classification module, a risk assessment module, a blockage prediction module, and a decision module; through the data acquisition module, equipment monitoring module, risk classification module, risk assessment module, blockage prediction module, and warning module working together, the process of the boiler four-tube foreign body blockage warning method described in embodiment 1 is implemented. Specifically: The data acquisition module is used to obtain historical data on foreign matter blockage of the four boiler pipes by connecting to the power plant maintenance data and fault records; The equipment monitoring module is used to collect real-time status data of the four boiler tubes in the boiler, namely the water-cooled wall tubes, superheater tubes, reheater tubes and economizer tubes, through an infrared thermal imager and a pressure differential sensor; The risk classification module is used to analyze objects using the "system-subsystem-equipment-component" architecture based on historical data of foreign body blockage of four boiler tubes using FMEA. It defines potential failure modes for each component, assesses severity, frequency, and undetectability based on the historical data of foreign body blockage of four boiler tubes, calculates risk priority, and establishes a mapping relationship between failure mode, impact, and risk, thereby forming a risk assessment framework that includes failure mode, impact analysis, and risk level. The risk assessment module is used to receive real-time status data of the four boiler tubes and match it with the failure mode in the risk assessment framework. When the monitoring data meets the characteristics of a certain failure mode, the corresponding risk priority RPN is automatically retrieved and an initial risk level is generated based on the RPN value. The blockage prediction module uses an LSTM neural network model, which is trained by inputting key features from historical data of foreign body blockage of the boiler's four pipes. Using the trained LSTM neural network model, the module calculates and outputs the blockage probability corresponding to the real-time status data of the boiler's four pipes using real-time monitored equipment status data. The decision module is used to dynamically adjust the initial risk level according to the congestion probability, dynamically correct the risk assessment index, generate the final risk level, and issue corresponding warnings based on the final risk level.

[0028] In addition, it should be understood that although this specification describes the embodiments, not every embodiment contains only one independent technical solution. This description is for clarity only. Those skilled in the art should consider the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for the purpose of illustrating the technical concept of the present invention and cannot be used to limit the scope of protection of the present invention. Any changes made based on the technical solution in accordance with the technical concept proposed by the present invention fall within the scope of protection of the claims of the present invention.

Claims

1. A boiler four-tube foreign body blockage early warning method, characterized in that: The following steps are involved: Obtain historical data on foreign matter blockage in four boiler pipes; Collect real-time status data of the four boiler tubes; Based on the historical data of foreign matter blockage in the four boiler tubes, FMEA was used to establish a mapping relationship between failure mode, effect, and risk, divide the risk levels, and form a risk assessment framework. Using the risk assessment framework, an initial risk level is generated based on the real-time status data of the four boiler tubes; The machine learning algorithm model is trained using historical data of foreign body blockage of the boiler's four tubes. The trained machine learning algorithm model is then used to process the real-time status data of the boiler's four tubes to generate a blockage probability. According to the congestion probability, the initial risk level is dynamically adjusted to generate the final risk level, and corresponding warnings are issued based on the final risk level.

2. A boiler four-tube foreign body blockage early warning method according to claim 1, characterized in that: The real-time collection of boiler four-tube status data is specifically as follows: the boiler four tubes include water-cooled wall tubes, superheater tubes, reheater tubes and economizer tubes; the temperature distribution data of the water-cooled wall tubes, superheater tubes, reheater tubes and economizer tubes are monitored in real time by an infrared thermal imager; and the pressure difference data of the water-cooled wall tubes, superheater tubes, reheater tubes and economizer tubes are monitored in real time by a pressure difference sensor.

3. The method for early warning of foreign matter blockage in four boiler tubes according to claim 1, characterized in that: Based on the historical data of foreign body blockage in the four tubes of the boiler, FMEA is used to establish a mapping relationship between failure mode, effect and risk, and form a risk assessment framework. Specifically, according to the historical data of foreign body blockage in the four tubes of the boiler, the analysis objects are sorted out according to the system-subsystem-equipment-component architecture, and the boundaries of each level are clarified. According to the system-subsystem-equipment-component architecture, the failures caused by foreign body blockage are analyzed from bottom to top. According to the analysis results, the risks of different failures caused by foreign body blockage are evaluated, and a mapping between different failures and risks is formed. Then, the risk levels are divided to form a risk assessment framework.

4. A boiler four-tube foreign body blockage early warning method according to claim 3, characterized in that: The risk level division is specifically as follows: based on the evaluation of different faults caused by foreign body blockage, the degree of harm caused by the fault consequences of the foreign body fault is evaluated, and the severity is calculated; by evaluating the probability of occurrence of the fault caused by the foreign body fault, the frequency of occurrence is calculated; by evaluating the difficulty of detecting the fault caused by the foreign body fault, the undetectability is calculated; based on the calculated severity, frequency of occurrence, and undetectability, the risk priority of the fault caused by the foreign body blockage is calculated, and the risk level is divided according to the level.

5. The method for early warning of foreign matter blockage in four boiler tubes according to claim 3, characterized in that: Based on the risk levels, FMEA sets status data threshold ranges corresponding to different risk levels under various fault modes for the four boiler tubes. The real-time status data of the four boiler tubes are compared with the status data threshold ranges corresponding to different risk levels under various fault modes set by FMEA, and the corresponding fault modes are matched. The risk level corresponding to the matching fault is then retrieved to generate the initial risk level.

6. The boiler four-tube foreign body blockage early warning method according to claim 1, characterized in that: The machine learning algorithm model is trained using historical data of foreign body blockage of the four boiler tubes. The machine learning algorithm model generates a blockage probability based on the real-time status data of the four boiler tubes. Specifically, an LSTM neural network model is used to extract key feature data from the status monitoring data from the historical data of foreign body blockage of the four boiler tubes, calculate the blockage probability corresponding to different key feature data, and form a feature-blocking probability mapping relationship; and a pre-trained LSTM neural network model is used to generate a blockage probability corresponding to the real-time equipment status based on the feature-blocking probability mapping relationship in the LSTM neural network model according to the real-time status data of the four boiler tubes.

7. A boiler four-tube foreign body blockage early warning method according to claim 6, characterized in that: The jam probability corresponding to the generated real-time equipment status is analyzed based on the Bayesian optimization algorithm and the data in the risk assessment framework formed by FMEA to analyze the difference between the probability generated by the machine learning algorithm model and the actual risk. The model output probability is dynamically adjusted according to the difference. After the adjustment is completed, the Bayesian optimization algorithm is used again to evaluate the difference between the adjusted probability and the actual risk, and the optimization is continuously iterated until the probability output by the adjusted model reaches the optimal state.

8. The method for early warning of foreign matter blockage in four boiler tubes according to claim 1, characterized in that: The method of dynamically adjusting the initial risk level according to the congestion probability to generate the final risk level is as follows: based on each risk level in the risk assessment framework, for different congestion probabilities, dynamically correcting the calculation indicators in the risk assessment, and then adjusting the risk level to generate the final risk level.

9. The method for early warning of foreign matter blockage in four boiler tubes according to claim 1, characterized in that: The foreign body blockage inspection strategy is determined according to the risk level, which is divided into three risk levels: low risk, medium risk and high risk. Three warning forms are set according to the three risk levels. When the risk is low, the monitoring interface will display a corresponding risk pop-up window; When the risk is medium, a yellow warning light and a risk pop-up window will appear; When the risk is high, a red sound and light alarm is triggered and a risk pop-up window is displayed.

10. A boiler four-tube foreign body blockage early warning system, based on a boiler four-tube foreign body blockage early warning method according to any one of claims 1 to 9, characterized in that: It includes data acquisition module, equipment monitoring module, risk classification module, risk assessment module, congestion prediction module and early warning module; The data acquisition module is used to obtain historical data of foreign matter blockage in the four pipes of the boiler; The equipment monitoring module is used to collect real-time status data of the four boiler tubes; The risk classification module is used to establish a failure mode-effect-risk mapping relationship based on the historical data of foreign body blockage of the four boiler tubes using FMEA, classify the risk levels, and form a risk assessment framework; The risk assessment module is used to generate an initial risk level based on the real-time status data of the four boiler tubes using a risk assessment framework; The blockage prediction module is used to train a machine learning algorithm model using historical data of foreign body blockage of the four boiler tubes, and use the trained machine learning algorithm model to process the real-time status data of the four boiler tubes to generate a blockage probability; The early warning module is used to dynamically adjust the initial risk level according to the congestion probability, generate a final risk level, and issue a corresponding early warning based on the final risk level.

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