Normalizing air cooling system of metal continuous heat treatment furnace

Abstract

The invention relates to the field of metal heat treatment and industrial automation control, in particular to a normalizing air cooling system of a metal continuous heat treatment furnace, and particularly relates to an ideal field reconstruction module which is used for obtaining steel plate parameters and environmental parameters, introducing a thermodynamic phase change dynamics principle to calculate a phase change latent heat release process and constructing an ideal pure temperature flow field matrix; the fault simulation generation module is used for calling a fault mechanism model to convert an equipment fault into a parameterized interference coefficient matrix, and superposing the parameterized interference coefficient matrix to an ideal matrix to generate a theoretical damaged state matrix; the double-track differential analysis module is used for collecting real-time data to construct an observation matrix, and respectively calculating a first difference between the observation matrix and the ideal matrix and a second difference between the theoretical damaged matrix and the ideal matrix to obtain a real and theoretical difference matrix; the feature coupling judgment module is used for extracting the difference matrix features, calculating the coupling degree, judging that the abnormality belongs to a real fault or environmental noise, and generating an air cooling regulation and control instruction; according to the method, non-linear fluctuation misjudgment is eliminated through phase change latent heat calculation, dual-track differential stripping environment common-mode interference is utilized, and micro equipment abnormity is accurately captured in a noisy environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of metal heat treatment and industrial automation control, in particular to a normalizing air cooling system of a metal continuous heat treatment furnace. BACKGROUND

[0002] In the normalizing process application scene of the metal continuous heat treatment furnace, the control system relies on accurate temperature flow field monitoring to ensure the uniformity of the microstructure and performance of the steel plate and the qualified rate of mechanical indicators. The field industrial computer usually needs to combine the data of the contact sensor and the image of the non-contact thermal imager to realize real-time sensing of the cooling state of the plate surface. For the monitoring of the air cooling process, the existing scheme generally adopts a direct threshold alarm architecture, that is, the temperature data of the steel plate surface is collected by an infrared thermal imager or a temperature gun, the temperature values are extracted by an image segmentation algorithm or a fixed-point sampling logic, and the measurement results are directly compared with the set process curve to determine whether the cooling is up to standard. Although this scheme has certain feasibility under the condition that the equipment state is good and the environment is simple, it excessively depends on the absolute value of the temperature sensor and lacks deep decoupling of the physical mechanism. When encountering a complex industrial site, the monitoring algorithm is easy to misjudge the normal phase change heat release platform of the steel plate as insufficient cooling, or misidentify non-physical environmental noise such as oxide skin blocking and steam disturbance as fan failure, resulting in incorrect control instructions. In addition, the numerical value comparison monitoring logic is extremely sensitive to environmental temperature drift and cannot distinguish between local minor equipment hidden dangers and overall process fluctuations, resulting in false or false alarms when the system faces early faults such as half-blockage of the air nozzle, making it difficult to support the production line for intelligent closed-loop control with high precision and low false alarm rate. Therefore, how to establish a digital twin benchmark with physical perception ability, effectively separate the phase change heat release and environmental interference, and improve the accuracy and robustness of the fault diagnosis of the air cooling system has become a technical problem to be solved. SUMMARY

[0003] In order to overcome the above-mentioned defects of the prior art, in order to achieve the above-mentioned purpose, the present application provides the following technical scheme: a normalizing air cooling system of a metal continuous heat treatment furnace, the system comprising: an ideal field reconstruction module, configured to acquire the specification parameters of a steel plate to be processed, an inlet temperature and current environmental parameters, calculate the latent heat release process in the normalizing process based on the thermodynamic phase change kinetics principle, and construct an ideal pure temperature flow field matrix free of noise and fault interference; a fault simulation generation module, configured to call a fault mechanism model in an expert knowledge base, convert various air cooling equipment faults into a parameterized interference coefficient matrix, and superimpose the parameterized interference coefficient matrix into the ideal pure temperature flow field matrix respectively, to generate a theoretical damaged state matrix corresponding to different fault types; a double-track differential analysis module, configured to collect real-time sensor array data of the air cooling section, construct a real-time observation data matrix, calculate a first difference between the real-time observation data matrix and the ideal pure temperature flow field matrix, and a second difference between the theoretical damaged state matrix and the ideal pure temperature flow field matrix, to obtain a real difference matrix and a theoretical difference matrix; and a feature coupling judgment module, configured to extract the numerical distribution features of the real difference matrix and the theoretical difference matrix, calculate the spatiotemporal feature coupling degree of the two, determine whether an abnormal signal belongs to a real equipment fault or environmental noise interference according to the coupling degree, and generate corresponding air cooling regulation instructions.

[0004] Preferably, the specific steps of the ideal field reconstruction module for constructing the ideal pure temperature flow field matrix are: acquiring the chemical composition content, plate thickness and roller speed of the steel plate to be processed, wherein the chemical composition content includes carbon, manganese and silicon element content; using a continuous cooling transformation curve logic, combining the chemical composition content to calculate the energy release value when austenite transforms into pearlite or ferrite; based on the energy release value and the law of conservation of energy, using the finite difference method to deduce the spatiotemporal temperature distribution of the steel plate in the continuous cooling process, to generate the ideal pure temperature flow field matrix.

[0005] Preferably, the specific steps of the fault simulation generation module for generating the theoretical damaged state matrix are: accessing the expert knowledge base, extracting typical fault features of the air cooling system, and establishing a corresponding parameterized interference coefficient matrix, wherein the typical fault features include tuyere blockage, fan efficiency attenuation and air bellow air leakage; for the tuyere blockage fault, defining a local heat exchange coefficient attenuation matrix based on the Gaussian distribution law; running multiple simulation threads in the background, applying different parameterized interference coefficient matrices to the ideal pure temperature flow field matrix respectively, simulating the thermodynamic response when the fault occurs, and generating a series of theoretical damaged state matrices representing different fault mechanisms.

[0006] Preferably, the calculation steps of the dual-track differential analysis module are as follows: acquiring real-time data collected by the infrared thermal imager and the wind pressure sensor, performing spatiotemporal alignment processing to form a real-time observation data matrix; performing the first path differential calculation, subtracting the ideal pure temperature flow field matrix from the real-time observation data matrix, stripping the basic temperature drop trend, and obtaining a real difference matrix containing mixed interference information; performing the second path differential calculation, subtracting the ideal pure temperature flow field matrix from the theoretical damaged state matrix, extracting pure fault feature data, and obtaining a theoretical difference matrix.

[0007] Preferably, the specific steps for the feature coupling decision module to calculate the feature coupling degree are as follows: obtaining the distribution shape of the actual difference matrix and the theoretical difference matrix in the deviation space; using a two-dimensional cross-correlation algorithm or a cosine similarity algorithm to compare the degree of morphological overlap between the actual difference matrix and each theoretical difference matrix; generating a similarity score, which characterizes the matching level between the currently observed abnormal shape and the known physical fault mechanism.

[0008] Preferably, the determination logic of the feature coupling decision module is as follows: A high confidence threshold and a low confidence threshold are preset, with the high confidence threshold being greater than the low confidence threshold; the similarity score is compared with the high confidence threshold; if the similarity score is greater than the high confidence threshold, the current anomaly is determined to be a real equipment fault, and the corresponding fault type is identified; the similarity score is compared with the low confidence threshold; if the similarity score is less than the low confidence threshold, and the amplitude of the actual difference matrix is ​​greater than a preset alarm line, the current anomaly is determined to be non-physical environmental noise or sensor artifact; if the similarity score is between the low confidence threshold and the high confidence threshold, or if the similarity score is less than the low confidence threshold and the amplitude of the actual difference matrix is ​​less than or equal to the preset alarm line, it is marked as an observation signal, and no fault or noise determination instruction is generated.

[0009] Preferably, the generation step of the air-cooling control command specifically includes: in response to the determination result being a real equipment fault, generating a targeted equipment adjustment signal or shutdown maintenance alarm based on the identified fault type and fault location, wherein the equipment adjustment signal includes adjusting the fan speed in a specific area or changing the roller conveyor speed; in response to the determination result being non-physical environmental noise or sensor artifacts, the system automatically ignores the abnormal fluctuation, keeps the current air-cooling control parameters unchanged, and records the false alarm event to optimize subsequent decision logic.

[0010] Preferably, the system further includes: an adaptive correction module, used to collect current ambient temperature and humidity data, correct the boundary condition parameters in the ideal field reconstruction module, and update the benchmark value of the ideal pure temperature flow field matrix in real time when it is determined that the overall cooling curve has shifted due to fluctuations in ambient temperature and humidity.

[0011] Compared with the prior art, the present invention has the following beneficial effects: This invention effectively solves the benchmark deviation problem caused by neglecting phase transformation heat release in traditional monitoring models by introducing the thermodynamic phase transformation kinetics principle and explicitly calculating the latent heat of phase transformation. The system uses the continuous cooling transformation curve to dynamically calculate the energy release during the transformation of austenite to pearlite or ferrite, accurately reproduces the phase transformation plateau characteristics of the steel plate in a specific temperature range, thereby avoiding misjudging normal nonlinear fluctuations of phase transformation heat release as cooling system failures, eliminating serious process false alarms caused by physical model distortion, and ensuring the physical authenticity of the monitoring benchmark. This invention utilizes a dual-track differential analysis mechanism to construct a pure deviation comparison platform, significantly improving the system's ability to capture minor equipment anomalies. By performing differential calculations between real-time observation data and the ideal flow field, and between the theoretical damage state and the ideal flow field, the system transforms the comparison of absolute temperature fields into a comparison of relative deviation patterns. This processing method effectively eliminates common-mode interference caused by overall environmental temperature drift, making feature matching no longer affected by absolute temperature levels. Thus, even in noisy industrial thermal radiation environments, it can still accurately identify minor local faults such as partial blockage of a single nozzle. Based on image morphology principles and confidence interval determination logic, this invention achieves accurate differentiation between physical faults and non-physical noise. Unlike traditional threshold methods that only focus on the temperature difference amplitude, this solution calculates the spatiotemporal coupling degree between the actual difference matrix and the theoretical difference matrix. It can identify signals with large temperature differences but whose shapes do not conform to physical laws (such as oxide scale obstruction or steam disturbance), and classify them as environmental noise and ignore them. This greatly reduces false alarms of shutdown caused by sensor artifacts or environmental interference, and ensures the continuous stability of the heat treatment process. This invention constructs a digital twin closed loop with fault mechanism deduction capabilities, achieving zero-sample identification and environmental adaptation for unknown fault modes. By pre-generating theoretical damaged state matrices representing different fault mechanisms in the digital space, the system transforms passive fault detection into active pattern matching, identifying fault modes that conform to physical laws without relying on massive historical data training. At the same time, the system can dynamically correct boundary conditions according to changes in ambient temperature and humidity, eliminating the impact of seasonal changes and diurnal temperature differences on control accuracy and ensuring consistent control throughout the year. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] See Figure 1 This embodiment provides a normalizing air-cooling system for a continuous metal heat treatment furnace. Based on the principles of thermodynamic phase change kinetics and dual-track differential analysis, this system aims to solve the problem of insufficient air-cooling control precision in complex industrial environments. Specifically, the system includes the collaborative working logic of the following modules: First, the ideal field reconstruction module performs the benchmark construction task, and obtains the specifications, inlet temperature and current environmental parameters of the steel plate to be processed through the sensor interface. This module not only considers the basic heat conduction, but also introduces the thermodynamic phase transformation kinetics principle to calculate the latent heat release process of phase transformation when austenite transforms into pearlite or ferrite during normalizing, thereby constructing an ideal pure temperature flow field matrix without noise and fault interference. Next, the fault simulation generation module performs a reverse deduction task, calling the fault mechanism model in the expert knowledge base to convert various air-cooled equipment faults such as nozzle blockage and fan efficiency degradation into parameterized interference coefficient matrices. Subsequently, these parameterized interference coefficient matrices are superimposed on the ideal pure temperature flow field matrix to generate theoretical damage state matrices corresponding to different fault types. Subsequently, the dual-track differential analysis module performs a real-time comparison task, collecting real-time sensor array data from the air-cooled section to construct a real-time observation data matrix; the system calculates the first difference between the real-time observation data matrix and the ideal pure temperature flow field matrix, and the second difference between the theoretical damaged state matrix and the ideal pure temperature flow field matrix, thereby obtaining the actual difference matrix and the theoretical difference matrix. Finally, the feature coupling decision module performs the final decision task, extracts the numerical distribution features of the actual difference matrix and the theoretical difference matrix, calculates the spatiotemporal feature coupling degree between the two, determines whether the abnormal signal belongs to real equipment failure or environmental noise interference based on the coupling degree, and generates corresponding air-cooling control commands.

[0015] This embodiment constructs a digital twin closed loop with physical sensing capabilities. By introducing the latent heat of phase change calculation, the system eliminates the possibility of temperature nonlinear fluctuations caused by normal phase change heat release being misjudged as insufficient cooling, and establishes a high-precision physical benchmark. At the same time, through the dual-track differential mechanism, the comparison of absolute temperature fields is transformed into the comparison of relative deviation patterns, effectively removing common-mode interference such as overall environmental temperature drift, enabling the system to accurately capture minute equipment abnormality signals even in noisy industrial thermal radiation environments.

[0016] This embodiment further specifies the steps of constructing the ideal pure temperature flow field matrix in the ideal field reconstruction module; this step aims to solve the benchmark deviation problem caused by the traditional model neglecting the exothermic phase transition; the specific execution logic is as follows: First, the system performs a data acquisition step, obtaining the chemical composition content, plate thickness, and roller speed of the steel plate to be processed from the host computer system; [Chemical composition content]: The source is the steel coil property table issued by the production management system (MES), and its physical meaning is the key parameter that determines the phase transformation temperature point of the material, specifically including the content of carbon, manganese, and silicon elements; Next, the system performs an energy calculation step, using the Continuous Cooling Transformation Curve (CCT) logic and the aforementioned chemical composition to dynamically calculate the energy release value during the transformation from austenite to pearlite or ferrite; [Energy Release Value]: Sourced from real-time interpolation calculations using a thermodynamic database. Specifically, the system calculates the phase transformation fraction based on the Avrami equation. This leads to the derivation of the latent heat release rate of phase change per unit time. ,in The total enthalpy of phase change is (J / m³). These are kinetic constants related to the chemical composition; Finally, the system performs a spatiotemporal deduction step, using the finite difference method to mesh the steel plate based on the energy release value and the law of energy conservation. Specifically, it solves the unsteady-state heat conduction differential equations containing internal heat sources: ,in For density, For specific heat capacity, The thermal conductivity is used to deduce the spatiotemporal temperature distribution of the steel plate during continuous cooling through time step iteration, and finally generate an ideal pure temperature flow field matrix.

[0017] This embodiment calculates the latent heat of phase change explicitly. This feature accurately reproduces the "phase transformation plateau" characteristics of steel plates in the 600℃-700℃ range. Without this step, the ideal curve would be a smooth downward curve, which would cause the system to incorrectly identify a cooling system failure when the steel plate undergoes normal phase transformation and heat release (i.e., mistakenly believe that the slow temperature drop is caused by a fan failure). This feature effectively avoids serious process misreporting and ensures the physical authenticity of the benchmark model.

[0018] This embodiment further specifies the step of the fault simulation generation module in generating the theoretical damaged state matrix; this step aims to achieve zero-sample identification capability for unknown faults; the specific execution logic is as follows: First, the system performs a feature extraction step, accesses the expert knowledge base, extracts typical fault features of the air-cooled system, and establishes the corresponding parameterized interference coefficient matrix; [Typical fault features]: The source is a summary of historical operation and maintenance records, and the physical meaning is common equipment failure modes, including nozzle blockage, fan efficiency reduction, and air box leakage. Next, the system executes the model definition step, defining a local heat accumulation coefficient matrix based on a Gaussian distribution for the nozzle blockage fault. This matrix simulates the physical phenomenon of reduced cooling capacity and increased local temperature relative to a baseline caused by blockage. Its mathematical expression is: ;in Map the coordinates of the nozzle center. This is a coefficient representing the severity of blockage (value 0~1, representing the intensity of temperature rise distortion). The radius of the heat-affected zone; Finally, the system performs a parallel simulation step, running multiple simulation threads in the background to apply different parameterized disturbance coefficient matrices to the ideal pure temperature flow field matrix (i.e., performing element-wise multiplication operations). The thermodynamic response during a fault is simulated to generate a series of theoretical damaged state matrices characterizing different fault mechanisms.

[0019] This embodiment transforms the "fault diagnosis" problem into a "pattern matching" problem. By generating various possible fault modes in the digital space in advance, the system no longer passively detects anomalies, but actively verifies whether the current anomaly conforms to the characteristics of a certain physical fault. This mechanism-based deduction method enables the system to identify fault modes that have never been encountered before but conform to physical laws without relying on massive amounts of historical fault data for training.

[0020] This embodiment further specifies the calculation steps of the dual-track differential analysis module; this step aims to extract pure fault features from complex background noise; the specific execution logic is as follows: First, the system performs a data alignment step to acquire real-time data collected by the infrared thermal imager and wind pressure sensor, and uses the roller speed for spatiotemporal alignment processing to form a real-time observation data matrix. Next, the system performs the first path difference calculation, subtracting the ideal pure temperature flow field matrix from the real-time observation data matrix. This operation aims to remove the basic temperature drop trend, because the temperature drop of several hundred degrees during normalizing is the dominant trend, and it is difficult to find small anomalies by direct analysis. After the difference, the real difference matrix containing mixed interference information is obtained. Subsequently, the system performs a second path difference calculation, subtracting the ideal pure temperature flow field matrix from the theoretical damaged state matrix; this operation aims to extract pure fault characteristic data to obtain the theoretical difference matrix.

[0021] This embodiment cleverly constructs a "pure deviation comparison platform" through a dual-track differential design. On this platform, the complex temperature field of the real world is simplified into a "deviation field," so that subsequent feature matching is no longer affected by the absolute temperature and only focuses on the distortion of the temperature distribution. This greatly improves the system's sensitivity to local minor faults (such as partial blockage of a single nozzle) and suppresses common-mode errors caused by the overall drift of the ambient temperature.

[0022] This embodiment further specifies the feature coupling degree calculation step of the feature coupling decision module; this step utilizes image morphology principles to quantify the similarity of abnormal signals; the specific execution logic is as follows: First, the system performs a morphology acquisition step to obtain the reality difference matrix. Difference Matrix from Theory (To avoid confusion with temperature symbols) Obfuscation, used here The distribution shape of the theoretical difference matrix in the deviation space; Next, the system performs an algorithm comparison step, employing a two-dimensional normalized cross-correlation algorithm (ZNCC) to compare the degree of morphological overlap between the actual difference matrix and each theoretical difference matrix; the specific calculation formula is as follows: in, and These are matrix elements. and The mean of the matrix; Finally, the system executes the score generation step to generate a similarity score. [Similarity score]: The source is the output of the ZNCC algorithm mentioned above. Its physical meaning is the matching level of the topological structure between the currently observed abnormal morphology and the known physical fault mechanism. The value range is -1 to 1 (after normalization, the range of 0 to 1 is usually considered).

[0023] This embodiment uses a correlation algorithm instead of Euclidean distance, making the decision result insensitive to the amplitude but sensitive to the shape. Even if the sensor itself has drift that causes an overall amplitude deviation, as long as the local "fault shape" (such as a circular cold spot or a strip-shaped temperature difference band) matches, the system can still accurately identify the fault, achieving accurate diagnosis at the morphological level and avoiding the false alarm defect of the traditional threshold method.

[0024] This embodiment further specifies the decision logic of the feature coupling decision module; this logic introduces the concept of confidence interval to distinguish between physical faults and non-physical noise; the specific execution logic is as follows: First, the system performs a threshold setting step, preset a high confidence threshold. (e.g., 0.85) and low confidence threshold (e.g., 0.4), and limited to Simultaneously define the matrix magnitude. The infinite norm of the reality difference matrix, i.e. ; Next, the system executes the logic for determining true faults and assigns similarity scores. Compare with a high confidence threshold; in response to The system determines that the current anomaly is a real equipment failure and identifies the corresponding failure type; Subsequently, the system executes noise assessment logic, comparing the similarity score with a low confidence threshold; in response to And the magnitude of the real difference matrix Greater than the preset alarm line (For example, 15℃), the system determines that the current anomaly is non-physical environmental noise or sensor artifact (i.e., "the temperature difference is very large but the shape does not conform to the physical law at all"). Finally, the system executes the observed tag logic in response to In and Between, or and The system marks it as a signal to be observed and does not generate fault or noise judgment instructions.

[0025] This embodiment creatively introduces a "non-physical noise" identification mechanism; traditional systems will alarm as long as the temperature difference is large, while this system will identify "large temperature difference but incorrect shape" (such as irregular low temperature points caused by oxide scale) by similarity scoring, thereby greatly reducing false alarms caused by environmental interference and improving the robustness of the system.

[0026] This embodiment further specifies the steps for generating air-cooled control commands; this step executes differentiated control strategies based on the decision result; the specific execution logic is as follows: First, the system executes the fault response branch; in response to the judgment result being a real equipment fault, the system generates targeted equipment adjustment signals or shutdown maintenance alarms based on the identified fault type and fault location; [Equipment adjustment signals]: the source is the control algorithm library, and the physical meaning is the action command used to compensate for cooling capacity, including adjusting the fan speed in a specific area or changing the roller conveyor speed; Next, the system executes the noise response branch; in response to the judgment result being non-physical environmental noise or sensor artifacts, the system automatically ignores the abnormal fluctuation, keeps the current air-cooling control parameters unchanged, and records the false alarm event to optimize subsequent decision logic.

[0027] This embodiment achieves "stability" control of the system; in harsh industrial environments, it can distinguish between "real problems" and "false alarms," ​​avoiding control oscillations caused by sensor noise or environmental disturbances (such as steam blockage), ensuring the continuous stability of the heat treatment process, and the accumulated noise samples can be used for continuous self-learning optimization of the system.

[0028] This embodiment provides a normalizing air-cooling system for a continuous metal heat treatment furnace with environmental adaptability, which complements the aforementioned system; this module is designed to address the impact of seasonal environmental changes on control accuracy; the specific execution logic is as follows: First, the system performs an environmental monitoring step, collecting current ambient temperature and humidity data through environmental sensors; Next, the system performs a deviation determination step to determine whether there is a deviation in the overall cooling curve due to fluctuations in ambient temperature and humidity. Finally, the system executes a parameter correction step. In response to the judgment result being environmental fluctuations, it corrects the boundary condition parameters in the ideal field reconstruction module and updates the baseline value of the ideal pure temperature flow field matrix in real time.

[0029] This embodiment eliminates the impact of seasonal changes (winter-summer temperature difference) and diurnal temperature difference on the accuracy of the baseline model by dynamically correcting the boundary conditions. This ensures that the "ideal pure flow field" generated by the system always matches the current physical environment, whether in the cold winter or the hot summer, thus guaranteeing the consistency of control accuracy throughout the year.

[0030] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0031] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0032] In the description of this invention, it should be understood that the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0033] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0034] In the description of this invention, "several" means one or more, and "a large number" means two or more.

[0035] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0036] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0037] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A metal continuous heat treatment furnace normalizing air cooling system, characterized in that the system The application relates to a wind cooling fault diagnosis system and method. The ideal field reconstruction module is used for acquiring the steel plate specification parameters, an inlet temperature and current environment parameters, calculating a phase change latent heat release process in a normalizing process based on a thermodynamic phase change dynamics principle, and constructing an ideal pure temperature flow field matrix free from noise and fault interference. The fault simulation generation module is used for calling a fault mechanism model in an expert knowledge base, converting various air cooling equipment faults into a parameterized interference coefficient matrix, and superimposing the parameterized interference coefficient matrix into the ideal pure temperature flow field matrix to generate a theoretical damaged state matrix corresponding to different fault types. The double-track differential analysis module is used for collecting real-time sensor array data of the air cooling section, constructing a real-time observation data matrix, respectively calculating a first difference between the real-time observation data matrix and the ideal pure temperature flow field matrix and a second difference between the theoretical damaged state matrix and the ideal pure temperature flow field matrix, and obtaining a real difference matrix and a theoretical difference matrix. The feature coupling judgment module is used for extracting numerical distribution features of the real difference matrix and the theoretical difference matrix, calculating a time-space feature coupling degree of the real difference matrix and the theoretical difference matrix, judging whether an abnormal signal belongs to a real equipment fault or environmental noise interference according to the coupling degree, and generating corresponding air cooling regulation instructions.

2. A metal continuous heat treatment furnace normalizing air cooling system according to claim 1, characterized in that, The ideal field reconstruction module constructs the ideal pure temperature flow field matrix in the following specific steps: The chemical composition content, the plate thickness and the roller speed of the steel plate to be processed are acquired, wherein the chemical composition content includes carbon, manganese and silicon element contents. The energy release value when the austenite is converted into pearlite or ferrite is calculated by using a continuous cooling transformation curve logic and combining the chemical composition content. Based on the energy release value and the law of conservation of energy, the time-space temperature distribution of the steel plate in the continuous cooling process is deduced by using a finite difference method to generate the ideal pure temperature flow field matrix.

3. The normalizing air cooling system of a metal continuous heat treatment furnace according to claim 1, characterized in that, The fault simulation generation module generates the theoretical damaged state matrix in the following specific steps: The expert knowledge base is accessed to extract typical fault features of the air cooling system and establish corresponding parameterized interference coefficient matrices, wherein the typical fault features include a blast nozzle blockage, fan efficiency attenuation and air box air leakage. A local heat exchange coefficient attenuation matrix based on a Gaussian distribution law is defined for the blast nozzle blockage fault. A plurality of simulation threads are run in the background in parallel, different parameterized interference coefficient matrices are respectively applied to the ideal pure temperature flow field matrix, the thermodynamic response when the fault occurs is simulated, and a series of theoretical damaged state matrices representing different fault mechanisms are generated.

4. The normalizing air cooling system of a metal continuous heat treatment furnace according to claim 1, characterized in that, The calculation steps of the double-track differential analysis module are as follows: Real-time data collected by an infrared thermal imager and a wind pressure sensor are acquired and time-space alignment processing is performed to form a real-time observation data matrix. First path difference calculation is performed to subtract the ideal pure temperature flow field matrix from the real-time observation data matrix to strip the basic temperature drop trend and obtain a real difference matrix containing mixed interference information. Second path difference calculation is performed to subtract the ideal pure temperature flow field matrix from the theoretical damaged state matrix to extract pure fault feature data and obtain a theoretical difference matrix.

5. The normalizing air cooling system of a metal continuous heat treatment furnace according to claim 1, characterized in that, The feature coupling judgment module performs feature coupling degree calculation in the following specific steps: The distribution patterns of the real difference matrix and the theoretical difference matrix in a deviation space are acquired. The two-dimensional cross-correlation algorithm or the cosine similarity algorithm is used to compare the shape coincidence degree between the real difference matrix and each theoretical difference matrix; A similarity score is generated, which represents the matching level of the currently observed abnormal shape and the known physical failure mechanism.

6. A metal continuous heat treatment furnace normalizing air cooling system according to claim 5, characterized in that, The determination logic of the feature coupling determination module is specifically: The high confidence threshold and the low confidence threshold are preset, and the high confidence threshold is greater than the low confidence threshold; The similarity score is compared with the high confidence threshold, and if the similarity score is greater than the high confidence threshold, it is determined that the current anomaly is a real equipment failure, and the corresponding fault type is identified; The similarity score is compared with the low confidence threshold, and if the similarity score is less than the low confidence threshold and the amplitude of the real difference matrix is greater than the preset alarm line, it is determined that the current anomaly is a non-physical environmental noise or a sensor artifact; If the similarity score is between the low confidence threshold and the high confidence threshold, or the similarity score is less than the low confidence threshold and the amplitude of the real difference matrix is less than or equal to the preset alarm line, it is marked as an observed signal, and no fault or noise determination instruction is generated.

7. A metal continuous heat treatment furnace normalizing air cooling system according to claim 6, characterized in that, The generation step of the air cooling regulation instruction is specifically: In response to the determination result being a real equipment failure, a targeted equipment adjustment signal or shutdown maintenance alarm is generated according to the identified fault type and fault position, wherein the equipment adjustment signal includes adjusting the speed of the fan in a specific area or changing the speed of the roller; In response to the determination result being a non-physical environmental noise or a sensor artifact, the system automatically ignores the abnormal fluctuation, keeps the current air cooling control parameters unchanged, and records the false alarm event to optimize the subsequent determination logic.

8. A metal continuous heat treatment furnace normalizing air cooling system according to claim 1, characterized in that, The system further comprises: An adaptive correction module is configured to collect current environmental temperature and humidity data when it is determined that the overall cooling curve is shifted due to environmental temperature and humidity fluctuations, correct the boundary condition parameters in the ideal field reconstruction module, and update the reference value of the ideal pure temperature flow field matrix in real time.

Images