Fault identification method and device for radiant tube in continuous annealing furnace and electronic equipment
By obtaining the flue gas temperature data of the radiation tube in the furnace and applying an outlier value detection algorithm, the fault is identified in real time, and the problem of poor timeliness of the radiation tube combustion abnormality is solved, which improves the fault response speed and the quality of cold rolled products.
Patent Information
- Application Number
- CN202411291725.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-14
- Publication Date
- 2024-12-20
AI Technical Summary
The radiation tube in the furnace is unable to be discovered in time when combustion is abnormal, resulting in poor real-time faults, loss of energy consumption, and affecting the output and quality of cold-rolled products.
By obtaining the flue gas temperature data at the radiation tube burner, using the preset outlier value detection algorithm to determine the outlier value and fault type, real-time monitoring and fault identification are achieved.
It improves the real-time performance of radiation tube failure, reduces energy consumption during combustion, extends the service life of radiation tubes, and improves the output and quality of cold-rolled products.
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Figure CN119167235A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of strip steel production, and in particular to a fault identification method, device and electronic equipment for a radiation tube in a continuous annealing furnace. Background Art
[0002] At present, the continuous annealing furnace is an important process link in the cold rolling galvanizing production process, and the stable heating capacity of the continuous annealing furnace is the key to the normal production of the cold rolling production line. In order to ensure the stable heating capacity of the continuous annealing furnace, the continuous annealing furnace is required to achieve combustion balance and complete combustion.
[0003] However, in order to ensure that the continuous annealing furnace achieves combustion balance and complete combustion, a large number of on-site staff are needed to conduct real-time monitoring and fault confirmation. In the absence of real-time data, abnormal combustion of the radiation tube cannot be discovered in time, which leads to poor real-time determination of radiation tube failures, greatly reducing the energy consumption of the radiation tube during combustion, affecting the service life of the radiation tube, and reducing the output and quality of cold-rolled products. Summary of the invention
[0004] The embodiments of the present application provide a fault identification method, device and electronic equipment for radiant tubes in a continuous annealing furnace. The embodiments provided by the present application solve the technical problem that the failure and abnormality of radiant tubes are not determined in time, resulting in poor real-time performance in determining the failure of radiant tubes, loss of energy consumption for combustion of radiant tubes, and reduction in the output and quality of cold-rolled products. The embodiments provided by the present application can timely detect the combustion abnormalities and failures of radiant tubes, improve the real-time performance in determining the failure of radiant tubes, greatly reduce the energy consumption of radiant tubes during combustion, and improve the output and quality of cold-rolled products on the basis of improving the service life of radiant tubes.
[0005] In a first aspect of an embodiment of the present application, an embodiment of the present application provides a method for identifying a fault of a radiant tube in a continuous annealing furnace, wherein the method for identifying a fault of a radiant tube in a continuous annealing furnace comprises:
[0006] Obtain the flue gas temperature data at all the radiant tube burners in the target continuous annealing furnace;
[0007] Based on all the flue gas temperature data, the first preset abnormal value detection algorithm and the second preset abnormal value detection algorithm, determining whether there are abnormal values in the flue gas temperature data corresponding to all the radiant tubes and the abnormal category of the abnormal value;
[0008] If there are abnormal values in all the flue gas temperature data, the fault type of the radiation tube matching the abnormal value is determined based on the abnormal category of the abnormal value.
[0009] In a feasible implementation manner, the determining whether there are abnormal values in the flue gas temperature data corresponding to all the radiant tubes and the abnormal category of the abnormal value based on all the flue gas temperature data, the first preset abnormal value detection algorithm and the second preset abnormal value detection algorithm includes:
[0010] Based on all the flue gas temperature data and a first preset abnormal value detection algorithm, determining whether there are abnormal values in the flue gas temperature data corresponding to all the radiant tubes;
[0011] If there is an abnormal value in the flue gas temperature data, determining the abnormal value as a low-level abnormality;
[0012] When the abnormal value is a low-level abnormality, it is determined whether the abnormal value is a high-level abnormality based on the abnormal flue gas temperature data and the second preset abnormal value detection algorithm, wherein the abnormality category judgment of the second preset abnormal value detection algorithm is higher than the abnormality category judgment of the first preset abnormal value detection algorithm.
[0013] In a feasible implementation manner, determining whether there are abnormal values in the flue gas temperature data corresponding to all the radiant tubes based on all the flue gas temperature data and a first preset abnormal value detection algorithm includes:
[0014] Based on all the flue gas temperature data, the number of the flue gas temperature data, and a preset abnormal value calculation formula in a first preset abnormal value detection algorithm, determining a high temperature abnormal value and a low temperature abnormal value corresponding to the flue gas temperature data under each radiant tube;
[0015] Determining a first critical abnormal value based on the number of the radiation tubes in the target continuous annealing furnace and a first detection level coefficient in a first preset abnormal value detection algorithm;
[0016] Based on the first critical abnormal value, the high temperature abnormal value and the low temperature abnormal value corresponding to the flue gas temperature data under each of the radiation tubes, it is determined whether there are abnormal values in the flue gas temperature data corresponding to all the radiation tubes.
[0017] In a feasible implementation manner, determining whether there are abnormal values in the flue gas temperature data corresponding to all the radiant tubes based on the first critical abnormal value, the high temperature abnormal value corresponding to the flue gas temperature data under each of the radiant tubes, and the low temperature abnormal value includes:
[0018] If the high temperature abnormal value corresponding to the flue gas temperature data under the radiation tube is greater than the low temperature abnormal value, and the high temperature abnormal value is greater than the first critical abnormal value, then the flue gas temperature data corresponding to the radiation tube is determined to be an abnormal value;
[0019] If the low temperature abnormal value corresponding to the flue gas temperature data under the radiation tube is greater than the high temperature abnormal value, and the low temperature abnormal value is greater than the first critical abnormal value, the lowest flue gas temperature data in all the radiation tubes is determined to be an abnormal value.
[0020] In a feasible implementation manner, when the abnormal value is a low-level abnormality, determining whether the abnormal value is a high-level abnormality based on the abnormal flue gas temperature data and a second preset abnormal value detection algorithm includes:
[0021] When the abnormal value is a low-level abnormality, determining a second critical abnormal value based on the number of the radiation tubes in the target continuous annealing furnace and a second detection level coefficient in the first preset abnormal value detection algorithm;
[0022] Based on the second critical abnormal value, the high temperature abnormal value and the low temperature abnormal value corresponding to each low-level abnormal flue gas temperature data, it is determined whether the abnormal value of the flue gas temperature data is a high-level abnormality,
[0023] In a feasible implementation manner, determining whether the abnormal value of the flue gas temperature data is a high-level abnormality based on the second critical abnormal value, the high-temperature abnormal value and the low-temperature abnormal value corresponding to each low-level abnormal flue gas temperature data, includes:
[0024] If the high temperature abnormality value corresponding to the flue gas temperature data with a low level abnormality is greater than the low temperature abnormality value, and the high temperature abnormality value is greater than the second critical abnormality value, it is determined that the flue gas temperature data corresponding to the radiation tube is a high level abnormality;
[0025] If the low-temperature abnormality value corresponding to the flue gas temperature data with a low-level abnormality is greater than the high-temperature abnormality value, and the low-temperature abnormality value is greater than the second critical abnormality value, the lowest flue gas temperature data in all the radiation tubes is determined to be a high-level abnormality.
[0026] In a feasible implementation manner, if there is an abnormal value in all the flue gas temperature data, determining the fault type of the radiation tube matching the abnormal value based on the abnormal category of the abnormal value includes:
[0027] If there are abnormal values in all the flue gas temperature data, and the flue gas temperature data is highly abnormal, it is determined that the radiation tube matching the abnormal value is a leakage fault;
[0028] If there are abnormal values in all the flue gas temperature data, and the flue gas temperature data is low-level abnormal, it is determined that the radiant tube matching the abnormal value is a load or air-fuel ratio fault;
[0029] If there is an abnormal value in the minimum flue gas temperature data, and the minimum flue gas temperature data is highly abnormal, it is determined that the radiation tube matching the abnormal value is a load fault;
[0030] If there is an abnormal value in the minimum flue gas temperature data, and the minimum flue gas temperature data is abnormal at a low level, it is determined that the radiation tube matching the abnormal value has an air-fuel ratio fault.
[0031] In a second aspect of the embodiment of the present application, the embodiment of the present application provides a fault identification device for a radiant tube in a continuous annealing furnace, wherein the fault identification device for a radiant tube in a continuous annealing furnace comprises:
[0032] An acquisition module is used to acquire the flue gas temperature data at all the radiant tube burners in the target continuous annealing furnace;
[0033] A first determination module is used to determine whether there are abnormal values in the flue gas temperature data corresponding to all the radiant tubes and the abnormal category of the abnormal value based on all the flue gas temperature data, a first preset abnormal value detection algorithm and a second preset abnormal value detection algorithm;
[0034] The second determination module is used to determine the fault type of the radiation tube matching the abnormal value based on the abnormal category of the abnormal value if there is an abnormal value in all the flue gas temperature data.
[0035] According to a third aspect of the embodiments of the present application, an electronic device is provided, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory through the bus, and the machine-readable instructions are executed by the processor when running, such as the steps of the above-mentioned method for identifying faults of radiation tubes in a continuous annealing furnace.
[0036] In a fourth aspect of the embodiments of the present application, the embodiments of the present application further provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the fault identification method for the radiation tube in the continuous annealing furnace are executed as described above.
[0037] The fault identification method, device and electronic equipment for radiant tubes in a continuous annealing furnace provided in the embodiments of the present application, compared with the prior art, obtains the flue gas temperature data at the burners of all radiant tubes in the target continuous annealing furnace, and determines whether there are abnormal values and the abnormal category of the abnormal values in the flue gas temperature data corresponding to all radiant tubes based on all the flue gas temperature data, a first preset abnormal value detection algorithm and a second preset abnormal value detection algorithm, and when there are abnormal values in all the flue gas temperature data, determines the fault type of the radiant tube matching the abnormal value based on the abnormal category of the abnormal value, can timely discover the combustion abnormality and fault of the radiant tube, improves the real-time performance of determining the fault of the radiant tube, greatly reduces the energy consumption of the radiant tube during combustion, and improves the output and quality of cold-rolled products on the basis of improving the service life of the radiant tube. BRIEF DESCRIPTION OF THE DRAWINGS
[0038] Figure 1 A flowchart of a method for identifying a fault of a radiant tube in a continuous annealing furnace provided in an embodiment of the present application is shown;
[0039] Figure 2 A schematic diagram of the structure of a burner of a radiant tube in a method for identifying a fault of a radiant tube in a continuous annealing furnace provided in an embodiment of the present application is shown;
[0040] Figure 3 A structural block diagram of a fault identification device for a radiant tube in a continuous annealing furnace provided in an embodiment of the present application is shown;
[0041] Figure 4 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown.
[0042] Figure 3 and Figure 4 The corresponding relationship between the reference numerals and the names of the drawings is as follows:
[0043] 300 a fault identification device for a radiation tube in a continuous annealing furnace; 310 an acquisition module; 320 a first determination module; 330 a second determination module; 400 an electronic device; 410 a processor; 420 a memory; 430 a bus. DETAILED DESCRIPTION
[0044] In order to better understand the technical solutions provided by the embodiments of this specification, the technical solutions of the embodiments of this specification are described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of this specification and the specific features in the embodiments are detailed descriptions of the technical solutions of the embodiments of this specification, rather than limitations on the technical solutions of this specification. In the absence of conflict, the embodiments of this specification and the technical features in the embodiments can be combined with each other.
[0045] In this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of more restrictions, the elements limited by the statement "comprise one..." do not exclude the existence of other identical elements in the process, method, article or equipment including the elements. The term "more than two" includes two or more than two situations.
[0046] First, the applicable application scenarios of the present application are introduced. The embodiments provided in the present application are applicable to the field of strip steel production technology.
[0047] Now, in order to ensure the combustion balance and complete combustion of the continuous annealing furnace, a large number of on-site staff are needed to carry out real-time monitoring and fault confirmation. In the absence of real-time data, the radiation tube cannot be discovered in time when it is burning abnormally, which leads to poor real-time determination of radiation tube failure, greatly reducing the energy consumption of the radiation tube during combustion, affecting the service life of the radiation tube, and reducing the output and quality of cold-rolled products.
[0048] Based on this, the embodiments of the present application provide a fault identification method, device and electronic equipment for the radiation tube in a continuous annealing furnace. The embodiments provided by the present application solve the technical problems. The present application can timely detect the combustion anomalies and faults of the radiation tube, improve the real-time performance of determining the faults of the radiation tube, greatly reduce the energy consumption of the radiation tube during combustion, and on the basis of improving the service life of the radiation tube, also improve the output and quality of cold-rolled products.
[0049] See also Figure 1 , Figure 1 A flowchart of a method for identifying a fault of a radiant tube in an electric annealing furnace provided in an embodiment of the present application is shown in FIG. Figure 1 As shown in, the fault identification method of the radiant tube in the continuous annealing furnace provided in the embodiment of the present application includes the following steps:
[0050] S101. Obtain flue gas temperature data at all radiation tube burners in a target continuous annealing furnace.
[0051] In this step, the embodiment provided in the present application collects the flue gas temperature data at all the radiation tube burners in the target continuous annealing furnace in real time within the heating area of the target continuous annealing furnace.
[0052] It can be understood that the equipment for collecting the flue gas temperature data at the burners of all radiation tubes in the embodiments provided in the present application can be customized according to different application scenarios and usage requirements.
[0053] Among them, it is assumed that the acquisition equipment in the embodiment provided in this application can be specifically but not limited to a thermocouple temperature data acquisition equipment, and the embodiment provided in this application uses the above-mentioned flue gas temperature data to monitor the production combustion process of each radiation tube in the target continuous annealing furnace in real time.
[0054] S102: Based on all flue gas temperature data, a first preset abnormal value detection algorithm and a second preset abnormal value detection algorithm, determine whether there are abnormal values in the flue gas temperature data corresponding to all radiant tubes and the abnormal category of the abnormal values.
[0055] In this step, after determining the flue gas temperature data corresponding to each radiant tube burner in the target continuous annealing furnace, each flue gas temperature data is transmitted to the server through a network communication device (or other communication method), and the server performs data analysis and processing on each flue gas temperature data according to the first preset abnormal value detection algorithm and the second preset abnormal value detection algorithm, and determines whether each flue gas temperature data has an abnormal value, and when at least one flue gas temperature data has an abnormal value, determines the abnormal category corresponding to the abnormal value, and transmits the corresponding abnormal category and the abnormal category corresponding to the abnormal value to a host and / or display machine of the on-site production line control network for visual display, so as to realize real-time monitoring and quantification of the gas combustion status at each burner. It is assumed that the embodiment provided by the present application can realize the display of different abnormal levels by displaying radiant tubes of different colors on the interface, and can view the temperature history curve of the radiant tube by clicking on each radiant tube. (The temperature history curve of the radiant tube changes during normal operation, and the temperature history curve of the radiant tube does not change and remains unchanged when the state is abnormal).
[0056] Here, the embodiments provided in the present application are specifically but not limited to (Human Machine Interface) in the host and / or display machine.
[0057] Among them, the first preset outlier detection algorithm and the second preset outlier detection algorithm in the embodiment provided by the present application can be customized according to different application scenarios and usage requirements. It is assumed that the first preset outlier detection algorithm and the second preset outlier detection algorithm in the embodiment provided by the present application can be specifically but not limited to the Dixon test analysis algorithm, and the first preset outlier detection algorithm can be specifically a Dixon test analysis algorithm with a detection level coefficient of 0.05; the first preset outlier detection algorithm can be specifically a Dixon test analysis algorithm with a detection level coefficient of 0.01.
[0058] In the above, the Dixon test analysis algorithm is a method for testing the consistency of a set of measurements, which can be used to detect one or more outliers.
[0059] S103: If there are abnormal values in all the flue gas temperature data, determine the fault type of the radiation tube matching the abnormal value based on the abnormal category of the abnormal value.
[0060] In this step, assuming that there are abnormal values in the flue gas temperature data, it is necessary to determine the fault type of the radiant tube according to the abnormal category of the corresponding flue gas temperature data, wherein the fault type of the radiant tube and the abnormal category of the abnormal value have a preset mapping relationship.
[0061] Compared with the prior art, the fault identification method for radiant tubes in a continuous annealing furnace provided in an embodiment of the present application obtains flue gas temperature data at the burners of all radiant tubes in a target continuous annealing furnace, and determines whether there are abnormal values and abnormal categories of the abnormal values in the flue gas temperature data corresponding to all radiant tubes based on all flue gas temperature data, a first preset abnormal value detection algorithm, and a second preset abnormal value detection algorithm. In the case where there are abnormal values in all flue gas temperature data, the fault type of the radiant tube matching the abnormal value is determined based on the abnormal category of the abnormal value. The combustion abnormality and fault of the radiant tube can be discovered in time, the real-time performance of determining the fault of the radiant tube is improved, the energy consumption of the radiant tube during combustion is greatly reduced, and the output and quality of the cold-rolled products are improved on the basis of improving the service life of the radiant tube.
[0062] In one embodiment, based on all flue gas temperature data, a first preset abnormal value detection algorithm and a second preset abnormal value detection algorithm, determining whether there are abnormal values and abnormal categories of the abnormal values in the flue gas temperature data corresponding to all radiant tubes includes the following sub-steps:
[0063] Sub-step 1011: Based on all the flue gas temperature data and a first preset abnormal value detection algorithm, determine whether there are abnormal values in the flue gas temperature data corresponding to all the radiant tubes.
[0064] In this step, after determining the first detection level coefficient in the first preset abnormal value detection algorithm, the abnormal value in the above flue gas temperature data is determined according to the flue gas temperature data and the first preset abnormal value detection algorithm.
[0065] Here, the first preset outlier detection algorithm in the embodiment provided in the present application is specifically an outlier detection algorithm for roughly detecting horizontal angles.
[0066] Sub-step 1012: If there is an abnormal value in the flue gas temperature data, determine that the abnormal value is a low-level abnormality.
[0067] In this step, if the flue gas temperature data in the embodiment provided in the present application is detected according to the first preset abnormal value detection algorithm and it is determined that an abnormal value exists, then the abnormal category of the abnormal value is determined to be a low-level abnormality.
[0068] Sub-step 1013: When the abnormal value is a low-level abnormality, determine whether the abnormal value is a high-level abnormality based on the abnormal flue gas temperature data and the second preset abnormal value detection algorithm, wherein the abnormality category judgment of the second preset abnormal value detection algorithm is higher than the abnormality category judgment of the first preset abnormal value detection algorithm.
[0069] In this step, after determining that the abnormal value is a low-level abnormal state, it is necessary to continue to judge whether the abnormal value is a high-level abnormal state. At this time, it is necessary to determine whether the abnormal value in the above flue gas temperature data is a high-level abnormal state based on the second detection level coefficient in the second preset abnormal value detection algorithm.
[0070] In one embodiment, based on all the flue gas temperature data and a first preset abnormal value detection algorithm, determining whether there are abnormal values in the flue gas temperature data corresponding to all the radiant tubes includes the following sub-steps:
[0071] Sub-step 1: determining the high temperature abnormal value and the low temperature abnormal value corresponding to the flue gas temperature data under each radiant tube based on all flue gas temperature data, the quantity of flue gas temperature data and the preset abnormal value calculation formula in the first preset abnormal value detection algorithm.
[0072] In this step, the embodiment provided by the present application first determines the number of all radiation tubes in the target continuous annealing furnace and the number of flue gas temperature data corresponding to all the radiation tubes. After determining the number of flue gas temperature data, all the flue gas temperature data are arranged in order of size, and then the high temperature abnormality value and low temperature abnormality value corresponding to the flue gas temperature data under each radiation tube are determined based on all the flue gas temperature data and the preset abnormality calculation formula. At this time, it is assumed that the high temperature abnormality value in the embodiment provided by the present application is represented by D; the low temperature abnormality value is represented by D′.
[0073] Here, in the embodiment provided in the present application, according to the preset abnormal value calculation formula, the specific formula for determining the high temperature abnormal value and the low temperature abnormal value corresponding to the flue gas temperature data under each radiation tube is as shown in Table 1:
[0074] Table 1
[0075]
[0076] Here, as shown in Table 1, due to the different numbers of all radiation tubes in the target continuous annealing furnace, the high temperature abnormal value and the low temperature abnormal value corresponding to the flue gas temperature data under each radiation tube are also different.
[0077] In the above, it is assumed that the order of all smoke temperature data in the embodiment provided by the present application can be specifically: T1≤T2≤…≤T n , where T is used to characterize the flue gas temperature data; n is used to characterize the number of all radiation tubes in the target continuous annealing furnace.
[0078] Sub-step 2: determining a first critical abnormal value based on the number of radiation tubes in the target continuous annealing furnace and a first detection level coefficient in a first preset abnormal value detection algorithm.
[0079] In this step, according to the setting requirements of the application scenario, the first detection level coefficient in the first preset outlier detection algorithm is determined, and then the first critical outlier value is determined according to the first detection level coefficient and the number of radiation tubes in the target continuous annealing furnace.
[0080] It is assumed that the first detection level coefficient in the embodiment provided in the present application is specifically but not limited to 0.05, and the first detection level coefficient is represented by α, so the first critical abnormal value is specifically but not limited to D1′ 1-α(n) To express.
[0081] Sub-step 3: based on the first critical abnormal value, the high temperature abnormal value and the low temperature abnormal value corresponding to the flue gas temperature data under each radiation tube, determine whether there are abnormal values in the flue gas temperature data corresponding to all the radiation tubes.
[0082] In this step, the embodiment provided in the present application determines whether there are abnormal values in the flue gas temperature data corresponding to the radiation tube based on the first critical abnormal value and the size relationship between the high temperature abnormal value and the low temperature abnormal value corresponding to the flue gas temperature data under each radiation tube.
[0083] It can be understood that if the high temperature abnormal value corresponding to the flue gas temperature data under the radiation tube is greater than the low temperature abnormal value, and the high temperature abnormal value is greater than the first critical abnormal value, then the flue gas temperature data corresponding to the radiation tube is determined to be an abnormal value, specifically: D>D′ and D>D1′ 1-α(n) When the radiation tube corresponds to T n is an abnormal value; if the low temperature abnormal value corresponding to the flue gas temperature data under the radiation tube is greater than the high temperature abnormal value, and the low temperature abnormal value is greater than the first critical abnormal value, then the lowest flue gas temperature data in all radiation tubes is determined to be an abnormal value, specifically: D′>D, and D′>D1′ 1-α(n) When , it is determined that T1 corresponding to the radiation tube is an abnormal value; if the above two relationships do not exist, it is determined that there is no abnormal value in the flue gas temperature data.
[0084] In one embodiment, when the abnormal value is a low-level abnormality, determining whether the abnormal value is a high-level abnormality based on the abnormal flue gas temperature data and a second preset abnormal value detection algorithm includes the following sub-steps:
[0085] Sub-step 1: when the abnormal value is a low-level abnormality, determine a second critical abnormal value based on the number of radiation tubes in the target continuous annealing furnace and a second detection level coefficient in a first preset abnormal value detection algorithm.
[0086] In one embodiment, assuming that the embodiment provided in the present application is in a state where the abnormal value is a low-level abnormality, the second detection level coefficient in the first preset abnormal value detection algorithm is determined according to the setting requirements of the application scenario, and then the second critical abnormal value is determined based on the second detection level coefficient and the number of radiation tubes in the target continuous annealing furnace.
[0087] It is assumed that the second detection level coefficient in the embodiment provided in the present application is specifically but not limited to 0.01, and the second detection level coefficient is represented by α, so the second critical abnormal value is specifically but not limited to D2′ 1-α(n) To express.
[0088] Sub-step 2: determining whether the abnormal value of the flue gas temperature data is a high-level abnormality based on the second critical abnormality value, the high-temperature abnormality value and the low-temperature abnormality value corresponding to each low-level abnormal flue gas temperature data.
[0089] In this step, the embodiment provided in the present application determines whether there are abnormal values in the flue gas temperature data corresponding to the radiation tube based on the second critical abnormal value and the size relationship between the high temperature abnormal value and the low temperature abnormal value corresponding to the flue gas temperature data under each radiation tube.
[0090] It can be understood that if the high temperature anomaly value corresponding to the flue gas temperature data under the radiation tube is greater than the low temperature anomaly value, and the high temperature anomaly value is greater than the second critical anomaly value, then the flue gas temperature data corresponding to the radiation tube is determined to be an abnormal value of a high-level abnormality, specifically: D>D′ and D>D2′ 1-α(n) When determining the radiation tube corresponding to T n is an abnormal value of a high-level abnormality; if the low-temperature abnormal value corresponding to the flue gas temperature data under the radiation tube is greater than the high-temperature abnormal value, and the low-temperature abnormal value is greater than the second critical abnormal value, then the lowest flue gas temperature data in all radiation tubes is determined to be an abnormal value of a high-level abnormality, specifically: D′>D, and D′>D2′ 1-α(n) When , it is determined that T1 corresponding to the radiation tube is an abnormal value of high-level abnormality; if the above two relationships do not exist, it is determined that there is no abnormal value of high-level abnormality in the flue gas temperature data.
[0091] In one embodiment, step S102 includes the following sub-steps:
[0092] Sub-step 1021: If there are abnormal values in all the flue gas temperature data, and the flue gas temperature data is highly abnormal, it is determined that the radiation tube matching the abnormal value is a leakage fault.
[0093] In this step, it is assumed that T n If the abnormal value is a high-level abnormal value, it is determined that the radiation tube may have a heat exchanger leakage fault. At this time, an alarm is required.
[0094] In the above, when a leakage failure of the air heat exchanger or the gas heat exchanger occurs at the radiant tube burner in the embodiment provided in the present application, it will cause after-combustion of the gas, thereby causing the flue gas temperature of the burner to rise sharply.
[0095] Sub-step 1022: If there are abnormal values in all the flue gas temperature data, and the flue gas temperature data are low-level abnormalities, it is determined that the radiant tube matching the abnormal value is a load or air-fuel ratio fault.
[0096] In this step, it is assumed that T n If it is a low-level abnormal value, it is determined that the radiation tube may have a load that is too large or an air-fuel ratio that is biased. At this time, an alarm is required.
[0097] It is understandable that for the burners in each radiation tube, since they are in the same column of the same zone, the flue gas temperature data generated should be the same. However, if there is a difference in the load distribution of the burners between the same zones, the flue gas temperature data at the burners will be different, and the greater the difference in load distribution, the greater the deviation of the flue gas temperature data.
[0098] Here, the abnormal air-fuel ratio of the burner will also cause the flue gas temperature data to change. If the air-fuel ratio is too large, the flue gas volume will be large, and the corresponding flue gas temperature data will be low; if the air-fuel ratio is too small, insufficient oxygen combustion may occur, resulting in high flue gas temperature data.
[0099] Sub-step 1023: If there is an abnormal value in the minimum flue gas temperature data, and the minimum flue gas temperature data is highly abnormal, it is determined that the radiation tube matching the abnormal value is a load fault.
[0100] In this step, assuming that there is an abnormal value of T1 with a high level of abnormality in the embodiment provided in the present application, it is determined that the radiation tube may have a fault of seriously underloaded load, and at this time, an alarm process is required.
[0101] Sub-step 1024: If there is an abnormal value in the minimum flue gas temperature data, and the minimum flue gas temperature data is abnormal at a low level, it is determined that the radiation tube matching the abnormal value has an air-fuel ratio fault.
[0102] In this step, assuming that there is an abnormal value of T1 that is a low-level abnormality in the embodiment provided in the present application, it is determined that the radiant tube may have a fault in which the air-fuel ratio is too large. At this time, an alarm process is required.
[0103] In summary, a new thermocouple is added in each radiation tube to detect the flue gas temperature data at the combustion point of each radiation tube, and the flue gas temperature data judged to be abnormal are eliminated until no abnormal radiation tube is judged.
[0104] Here, please see Figure 2 , Figure 2 FIG. 1 shows a schematic diagram of the structure of the burner of a radiant tube in a radiant tube fault identification method in a continuous annealing furnace provided by an embodiment of the present application. Figure 2 As shown, a burner is installed at one end of the radiation tube, and the burner is connected to the gas inlet, and a heat exchanger is installed at the other end of the radiation tube, and the heat exchanger is respectively connected to the air inlet and the exhaust gas outlet, and a connecting pipe is connected between the burner and the heat exchanger, and the pipe contains heated air.
[0105] Working principle: the waste heat from the combustion exhaust gas heats the air through the heat exchanger, and then is sent to the burner to burn with the gas. The heat exchanger is used to heat the air, achieving the purpose of efficient use of thermal energy. The new burner can burn fully and achieve the purpose of energy saving and carbon reduction.
[0106] Compared with the prior art, the fault identification method for radiant tubes in a continuous annealing furnace provided in an embodiment of the present application obtains flue gas temperature data at the burners of all radiant tubes in a target continuous annealing furnace, and determines whether there are abnormal values and abnormal categories of the abnormal values in the flue gas temperature data corresponding to all radiant tubes based on all flue gas temperature data, a first preset abnormal value detection algorithm, and a second preset abnormal value detection algorithm. In the case where there are abnormal values in all flue gas temperature data, the fault type of the radiant tube matching the abnormal value is determined based on the abnormal category of the abnormal value. The combustion abnormality and fault of the radiant tube can be discovered in time, the real-time performance of determining the fault of the radiant tube is improved, the energy consumption of the radiant tube during combustion is greatly reduced, and the output and quality of the cold-rolled products are improved on the basis of improving the service life of the radiant tube.
[0107] The embodiment provided by the present application can perform visual monitoring and early warning of the post-combustion status of all radiation tubes in the target continuous annealing furnace in real time, and can promptly check and replace radiation tubes with problems, thereby solving the blindness and uncertainty of manual adjustment. The embodiment provided by the present application determines the fault type of the radiation tube and adjusts the parameters in the target continuous annealing furnace to achieve combustion balance and efficient combustion of all radiation tubes. While ensuring complete and sufficient combustion, the annealing accuracy of the target continuous annealing furnace is improved. The embodiment provided by the present application adopts a graphical interface capable of human-computer interaction to facilitate operation and processing by operators.
[0108] See also Figure 3 , Figure 3 This is a structural block diagram of a fault identification device for a radiant tube in a continuous annealing furnace provided in an embodiment of the present application, such as Figure 3As shown in , the fault identification device 300 of the radiation tube in the continuous annealing furnace includes:
[0109] The acquisition module 310 is used to acquire the flue gas temperature data at all the radiant tube burners in the target continuous annealing furnace.
[0110] The first determination module 320 is used to determine whether there are abnormal values and abnormal categories of the abnormal values in the flue gas temperature data corresponding to all the radiant tubes based on all the flue gas temperature data, the first preset abnormal value detection algorithm and the second preset abnormal value detection algorithm.
[0111] The second determination module 330 is used to determine the fault type of the radiation tube matching the abnormal value based on the abnormal category of the abnormal value if there is an abnormal value in all the flue gas temperature data.
[0112] In one embodiment, the first determining module 320 is specifically configured to:
[0113] Based on all the flue gas temperature data and a first preset abnormal value detection algorithm, it is determined whether there are abnormal values in the flue gas temperature data corresponding to all the radiant tubes.
[0114] If there are abnormal values in the flue gas temperature data, the abnormal values are determined to be low-level abnormalities.
[0115] When the abnormal value is a low-level abnormality, it is determined whether the abnormal value is a high-level abnormality based on the abnormal flue gas temperature data and the second preset abnormal value detection algorithm, wherein the abnormality category judgment of the second preset abnormal value detection algorithm is higher than the abnormality category judgment of the first preset abnormal value detection algorithm.
[0116] In one embodiment, based on all the flue gas temperature data and a first preset abnormal value detection algorithm, determining whether there are abnormal values in the flue gas temperature data corresponding to all the radiant tubes includes:
[0117] Based on all the flue gas temperature data, the number of flue gas temperature data and the preset abnormal value calculation formula in the first preset abnormal value detection algorithm, the high temperature abnormal value and the low temperature abnormal value corresponding to the flue gas temperature data under each radiation tube are determined.
[0118] A first critical abnormal value is determined based on the number of radiation tubes in the target continuous annealing furnace and a first detection level coefficient in a first preset abnormal value detection algorithm.
[0119] Based on the first critical abnormal value, the high temperature abnormal value and the low temperature abnormal value corresponding to the flue gas temperature data under each radiation tube, it is determined whether there are abnormal values in the flue gas temperature data corresponding to all the radiation tubes.
[0120] In one embodiment, based on the first critical abnormal value, the high temperature abnormal value and the low temperature abnormal value corresponding to the flue gas temperature data under each radiation tube, determining whether there are abnormal values in the flue gas temperature data corresponding to all radiation tubes includes:
[0121] If the high temperature abnormal value corresponding to the flue gas temperature data under the radiation tube is greater than the low temperature abnormal value, and the high temperature abnormal value is greater than the first critical abnormal value, it is determined that the flue gas temperature data corresponding to the radiation tube is an abnormal value.
[0122] If the low temperature anomaly value corresponding to the flue gas temperature data under the radiation tube is greater than the high temperature anomaly value, and the low temperature anomaly value is greater than the first critical anomaly value, the lowest flue gas temperature data in all the radiation tubes is determined to be an anomaly value.
[0123] In one embodiment, when the abnormal value is a low-level abnormality, determining whether the abnormal value is a high-level abnormality based on the abnormal flue gas temperature data and a second preset abnormal value detection algorithm includes:
[0124] When the abnormal value is a low-level abnormality, a second critical abnormal value is determined based on the number of radiation tubes in the target continuous annealing furnace and a second detection level coefficient in the first preset abnormal value detection algorithm.
[0125] Based on the second critical abnormal value, the high temperature abnormal value and the low temperature abnormal value corresponding to each low-level abnormal flue gas temperature data, it is determined whether the abnormal value of the flue gas temperature data is a high-level abnormality.
[0126] In one embodiment, determining whether the abnormal value of the flue gas temperature data is a high-level abnormality based on the second critical abnormal value, the high-temperature abnormal value and the low-temperature abnormal value corresponding to each low-level abnormal flue gas temperature data includes:
[0127] If the high temperature anomaly value corresponding to the flue gas temperature data with a low level anomaly is greater than the low temperature anomaly value, and the high temperature anomaly value is greater than the second critical anomaly value, then the flue gas temperature data corresponding to the radiation tube is determined to be a high level anomaly.
[0128] If the low-temperature anomaly value corresponding to the flue gas temperature data with a low-level anomaly is greater than the high-temperature anomaly value, and the low-temperature anomaly value is greater than the second critical anomaly value, then the lowest flue gas temperature data in all radiation tubes is determined to be a high-level anomaly.
[0129] In one embodiment, the second determining module 330 is specifically configured to:
[0130] If there are abnormal values in all the flue gas temperature data, and the flue gas temperature data is highly abnormal, it is determined that the radiation tube matching the abnormal value is a leakage fault.
[0131] If there are abnormal values in all the flue gas temperature data, and the flue gas temperature data are low-level abnormalities, it is determined that the radiant tube matching the abnormal value is a load or air-fuel ratio fault.
[0132] If there are abnormal values in the minimum flue gas temperature data, and the minimum flue gas temperature data is highly abnormal, it is determined that the radiation tube matching the abnormal value is a load fault.
[0133] If there are abnormal values in the minimum flue gas temperature data, and the minimum flue gas temperature data is abnormal at a low level, it is determined that the radiation tube matching the abnormal value is an air-fuel ratio fault.
[0134] Compared with the prior art, the fault identification device 300 for radiant tubes in a continuous annealing furnace provided in an embodiment of the present application obtains flue gas temperature data at the burners of all radiant tubes in a target continuous annealing furnace, and determines whether there are abnormal values and abnormal categories of the abnormal values in the flue gas temperature data corresponding to all radiant tubes based on all flue gas temperature data, a first preset abnormal value detection algorithm, and a second preset abnormal value detection algorithm. In addition, when there are abnormal values in all flue gas temperature data, the fault type of the radiant tube matching the abnormal value is determined based on the abnormal category of the abnormal value. The combustion abnormality and fault of the radiant tube can be discovered in time, the real-time performance of determining the fault of the radiant tube is improved, the energy consumption of the radiant tube during combustion is greatly reduced, and the output and quality of the cold-rolled products are improved on the basis of improving the service life of the radiant tube.
[0135] The embodiment provided by the present application can perform visual monitoring and early warning of the post-combustion status of all radiation tubes in the target continuous annealing furnace in real time, and can promptly check and replace radiation tubes with problems, thereby solving the blindness and uncertainty of manual adjustment. The embodiment provided by the present application determines the fault type of the radiation tube and adjusts the parameters in the target continuous annealing furnace to achieve combustion balance and efficient combustion of all radiation tubes. While ensuring complete and sufficient combustion, the annealing accuracy of the target continuous annealing furnace is improved. The embodiment provided by the present application adopts a graphical interface capable of human-computer interaction to facilitate operation and processing by operators.
[0136] See also Figure 4 , Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 4 As shown in , the electronic device 400 includes a processor 410 , a memory 420 and a bus 430 .
[0137] The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device 400 is running, the processor 410 communicates with the memory 420 via the bus 430. When the machine-readable instructions are executed by the processor 410, the above-mentioned Figures 1 to 3The steps of the method for identifying faults of radiant tubes in a continuous annealing furnace in the method embodiment shown in the figure, and the specific implementation methods can be referred to the method embodiment, which will not be described in detail here.
[0138] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figures 1 to 3 The steps of the method for identifying faults of radiant tubes in a continuous annealing furnace in the method embodiment shown in the figure, and the specific implementation methods can be referred to the method embodiment, which will not be described in detail here.
[0139] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0140] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0141] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-readable program code.
[0142] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0143] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0144] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0145] An embodiment of the present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes the process of the LDPC decoding method of the solid-state hard disk controller.
[0146] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.
[0147] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0148] In the several embodiments provided in the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.
[0149] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0150] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0151] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.
[0152] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.
[0153] Although the preferred embodiments of this specification have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of this specification.
[0154] Obviously, those skilled in the art can make various changes and modifications to this specification without departing from the spirit and scope of this specification. Thus, if these modifications and variations of this specification fall within the scope of the claims of this specification and their equivalents, this specification is also intended to include these modifications and variations.
Claims
1. A method for identifying faults of radiant tubes in a continuous annealing furnace, characterized in that: The method for identifying faults of the radiation tube in the continuous annealing furnace comprises: Obtain flue gas temperature data at all radiant tube burners in the target continuous annealing furnace; Based on all the flue gas temperature data, the first preset abnormal value detection algorithm and the second preset abnormal value detection algorithm, determining whether there are abnormal values in the flue gas temperature data corresponding to all the radiant tubes and the abnormal category of the abnormal value; If there are abnormal values in all the flue gas temperature data, the fault type of the radiation tube matching the abnormal value is determined based on the abnormal category of the abnormal value.
2. The method for identifying faults of radiant tubes in a continuous annealing furnace according to claim 1, characterized in that: The determining whether there are abnormal values in the flue gas temperature data corresponding to all the radiant tubes and the abnormality category of the abnormal value based on all the flue gas temperature data, the first preset abnormal value detection algorithm and the second preset abnormal value detection algorithm includes: Based on all the flue gas temperature data and a first preset abnormal value detection algorithm, determining whether there are abnormal values in the flue gas temperature data corresponding to all the radiant tubes; If there is an abnormal value in the flue gas temperature data, determining the abnormal value as a low-level abnormality; When the abnormal value is a low-level abnormality, it is determined whether the abnormal value is a high-level abnormality based on the abnormal flue gas temperature data and the second preset abnormal value detection algorithm, wherein the abnormality category judgment of the second preset abnormal value detection algorithm is higher than the abnormality category judgment of the first preset abnormal value detection algorithm.
3. The method for identifying faults of radiant tubes in a continuous annealing furnace according to claim 2, characterized in that: The determining whether there are abnormal values in the flue gas temperature data corresponding to all the radiant tubes based on all the flue gas temperature data and a first preset abnormal value detection algorithm includes: Based on all the flue gas temperature data, the number of the flue gas temperature data, and a preset abnormal value calculation formula in a first preset abnormal value detection algorithm, determining a high temperature abnormal value and a low temperature abnormal value corresponding to the flue gas temperature data under each radiant tube; Determining a first critical abnormal value based on the number of the radiation tubes in the target continuous annealing furnace and a first detection level coefficient in a first preset abnormal value detection algorithm; Based on the first critical abnormal value, the high temperature abnormal value and the low temperature abnormal value corresponding to the flue gas temperature data under each of the radiation tubes, it is determined whether there are abnormal values in the flue gas temperature data corresponding to all the radiation tubes.
4. The method for identifying faults of radiant tubes in a continuous annealing furnace according to claim 3, characterized in that: The determining whether there are abnormal values in the flue gas temperature data corresponding to all the radiant tubes based on the first critical abnormal value, the high temperature abnormal value and the low temperature abnormal value corresponding to the flue gas temperature data under each of the radiant tubes comprises: If the high temperature abnormal value corresponding to the flue gas temperature data under the radiation tube is greater than the low temperature abnormal value, and the high temperature abnormal value is greater than the first critical abnormal value, then the flue gas temperature data corresponding to the radiation tube is determined to be an abnormal value; If the low temperature abnormal value corresponding to the flue gas temperature data under the radiation tube is greater than the high temperature abnormal value, and the low temperature abnormal value is greater than the first critical abnormal value, the lowest flue gas temperature data in all the radiation tubes is determined to be an abnormal value.
5. The method for identifying faults of radiant tubes in a continuous annealing furnace according to claim 3, characterized in that: In the state where the abnormal value is a low-level abnormality, determining whether the abnormal value is a high-level abnormality based on the abnormal flue gas temperature data and a second preset abnormal value detection algorithm includes: When the abnormal value is a low-level abnormality, determining a second critical abnormal value based on the number of the radiation tubes in the target continuous annealing furnace and a second detection level coefficient in the first preset abnormal value detection algorithm; Based on the second critical abnormal value, the high temperature abnormal value and the low temperature abnormal value corresponding to each low-level abnormal flue gas temperature data, it is determined whether the abnormal value of the flue gas temperature data is a high-level abnormality.
6. The method for identifying faults of radiant tubes in a continuous annealing furnace according to claim 5, characterized in that: The determining whether the abnormal value of the flue gas temperature data is a high-level abnormality based on the second critical abnormal value, the high-temperature abnormal value and the low-temperature abnormal value corresponding to each low-level abnormal flue gas temperature data, includes: If the high temperature abnormality value corresponding to the flue gas temperature data with a low level abnormality is greater than the low temperature abnormality value, and the high temperature abnormality value is greater than the second critical abnormality value, it is determined that the flue gas temperature data corresponding to the radiation tube is a high level abnormality; If the low-temperature abnormality value corresponding to the flue gas temperature data with a low-level abnormality is greater than the high-temperature abnormality value, and the low-temperature abnormality value is greater than the second critical abnormality value, the lowest flue gas temperature data in all the radiation tubes is determined to be a high-level abnormality.
7. The method for identifying faults of radiant tubes in a continuous annealing furnace according to any one of claims 4 or 6, characterized in that: If there is an abnormal value in all the flue gas temperature data, determining the fault type of the radiation tube matching the abnormal value based on the abnormal category of the abnormal value includes: If there are abnormal values in all the flue gas temperature data, and the flue gas temperature data is highly abnormal, it is determined that the radiation tube matching the abnormal value is a leakage fault; If there are abnormal values in all the flue gas temperature data, and the flue gas temperature data is low-level abnormal, it is determined that the radiant tube matching the abnormal value is a load or air-fuel ratio fault; If there is an abnormal value in the minimum flue gas temperature data, and the minimum flue gas temperature data is highly abnormal, it is determined that the radiation tube matching the abnormal value is a load fault; If there is an abnormal value in the minimum flue gas temperature data, and the minimum flue gas temperature data is abnormal at a low level, it is determined that the radiation tube matching the abnormal value has an air-fuel ratio fault.
8. A fault identification device for radiant tubes in a continuous annealing furnace, characterized in that: The fault identification device for the radiant tube in the continuous annealing furnace comprises: An acquisition module is used to acquire the flue gas temperature data at all the radiant tube burners in the target continuous annealing furnace; A first determination module is used to determine whether there are abnormal values in the flue gas temperature data corresponding to all the radiant tubes and the abnormal category of the abnormal value based on all the flue gas temperature data, a first preset abnormal value detection algorithm and a second preset abnormal value detection algorithm; The second determination module is used to determine the fault type of the radiation tube matching the abnormal value based on the abnormal category of the abnormal value if there is an abnormal value in all the flue gas temperature data.
9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and the machine-readable instructions are executed by the processor to perform the steps of the method for identifying faults of radiant tubes in a continuous annealing furnace as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for identifying faults of radiant tubes in a continuous annealing furnace as described in any one of claims 1 to 7 are executed.