Fault monitoring and early warning method and system for air-cooled steel belt dry slag extractor

Abstract

The invention discloses a fault monitoring and early warning method and system for an air-cooled steel belt dry slag extractor, and the method comprises the steps: carrying out the data analysis of operation parameters of the air-cooled steel belt dry slag extractor, and determining the abnormal operation parameters of the air-cooled steel belt dry slag extractor; the fault type of the air-cooled steel belt dry slag extractor is determined based on the abnormal operation parameters, and data characteristics are determined by analyzing the abnormal operation parameters; based on the fault type and the data characteristics, the fault degree of the air-cooled steel belt dry slag extractor is evaluated and calculated to obtain a fault degree evaluation value; and the fault grade of the air-cooled steel belt dry slag extractor is determined according to the fault degree evaluation value, and a fault early warning prompt strategy is set based on the fault grade to carry out fault early warning on the air-cooled steel belt dry slag extractor in time. By monitoring and analyzing the operation parameter data of the air-cooled steel belt dry slag extractor in real time, abnormal conditions in equipment operation can be found as early as possible, potential faults can be predicted, and corresponding early warning prompts can be made, so that adverse effects of equipment faults on production can be avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of slag discharge machine fault monitoring and early warning, and particularly to a fault monitoring and early warning method and system for an air-cooled steel belt dry slag discharge machine. Background Art

[0002] The air-cooled steel belt dry slag discharge machine is an important device for handling steelmaking slag during the steel smelting process. In steel production, these devices undertake key slag discharge tasks, so their normal operation is crucial for the continuity and efficiency of the production line. However, due to long-term harsh working environments such as high temperature and high pressure, as well as the wear and aging of the equipment itself, the air-cooled steel belt dry slag discharge machine is prone to various faults, such as bearing faults, transmission system faults, cooling system faults, etc. To ensure the reliable operation of the air-cooled steel belt dry slag discharge machine, fault monitoring and early warning are particularly important.

[0003] However, traditional fault monitoring and early warning methods often rely on the experience and intuition of operators to judge whether the equipment is operating normally. Such subjective judgments are easily affected by personal experience and subjectivity, lacking objectivity and accuracy. Moreover, traditional methods usually conduct inspections on the equipment status based on regular inspections or periodic maintenance. This method cannot obtain real-time equipment operation data and status changes, resulting in a slow response speed to potential faults. Additionally, traditional methods often lack a quantitative assessment of the severity of equipment faults and cannot rank different faults, leading to the inability to achieve intelligent early warning decisions. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a fault monitoring and early warning method and system for an air-cooled steel belt dry slag discharge machine, including: Obtain the operation parameter data of the air-cooled steel belt dry slag discharge machine, and analyze the operation parameter data to determine the abnormal operation parameters in the operation parameter data; Based on the abnormal operation parameters in the operation parameter data, determine the fault type of the air-cooled steel belt dry slag discharge machine, and analyze the abnormal operation parameters in the operation parameter data to determine the data characteristics of the abnormal operation parameters; Based on the fault type and data characteristics, evaluate and calculate the fault severity of the air-cooled steel belt dry slag discharge machine to obtain the fault severity evaluation value of the air-cooled steel belt dry slag discharge machine; Determine the fault level of the air-cooled steel belt dry slag discharge machine according to the fault severity evaluation value, and set a fault early warning prompt strategy based on the fault level; Conduct timely fault early warning on the air-cooled steel belt dry slag discharge machine according to the fault early warning prompt strategy.

[0005] Further, acquiring the operation parameter data of the air-cooled steel belt dry slag extractor, analyzing the operation parameter data, and determining the abnormal operation parameters in the operation parameter data, including: Acquiring the operation parameter data of the air-cooled steel belt dry slag extractor, grouping the operation parameter data according to the parameter type, and obtaining several groups of operation parameter data; Inputting each group of operation parameter data into a preset screening model corresponding to the parameter type, and obtaining the abnormal data corresponding to each group of operation parameter data output by each preset screening model; Counting the number of abnormal data in each group of operation parameter data, and determining the parameters corresponding to the group of operation parameter data with the number of abnormal data greater than the preset number as the abnormal operation parameters in the operation parameter data.

[0006] Further, determining the fault type of the air-cooled steel belt dry slag extractor based on the abnormal operation parameters in the operation parameter data, including: Presetting several preset fault types and the abnormal operation parameters corresponding to each fault type; Comparing and analyzing each abnormal operation parameter of each fault type with all the abnormal operation parameters in the operation parameter data one by one, and counting the number of parameters with consistent abnormal operation parameter comparisons; Determining the preset fault type corresponding to the largest number of parameters as the fault type of the air-cooled steel belt dry slag extractor.

[0007] Further, analyzing the abnormal operation parameters in the operation parameter data and determining the data characteristics of the abnormal operation parameters, including: Acquiring the group of operation parameter data corresponding to each abnormal operation parameter in the operation parameter data, and determining the ratio of abnormal data in the group of operation parameter data and the average value of the group of operation parameter data; Determining the ratio of abnormal data in the group of operation parameter data and the average value of the group of operation parameter data as the data characteristics of each abnormal operation parameter.

[0008] Further, evaluating and calculating the fault degree of the air-cooled steel belt dry slag extractor based on the fault type and data characteristics, and obtaining the fault degree evaluation value of the air-cooled steel belt dry slag extractor, including: Acquiring the ratio of abnormal data in the group of operation parameter data corresponding to each abnormal operation parameter and the average value of the group of operation parameter data, and taking the ratio of abnormal data in the group of operation parameter data as the abnormal coefficient of the abnormal operation parameter; Presetting the fault degree coefficient of the fault type of the air-cooled steel belt dry slag extractor, and evaluating and taking values for the average value of the group of operation parameter data to obtain the abnormal evaluation value of the abnormal operation parameter; Calculate the fault degree evaluation value of the air-cooled steel belt dry slag discharger based on the fault degree coefficient, the abnormal coefficient of abnormal operating parameters, and the abnormal evaluation value. The calculation formula for the fault degree evaluation value of the air-cooled steel belt dry slag discharger is as follows: , Where P is the fault degree evaluation value of the air-cooled steel belt dry slag discharger, α is the fault degree coefficient of the fault type of the air-cooled steel belt dry slag discharger, ai is the abnormal coefficient of the i-th abnormal operating parameter, Di is the data average value of the i-th abnormal operating parameter, and n is the number of abnormal operating parameters.

[0009] Further, determining the fault level of the air-cooled steel belt dry slag discharger according to the fault degree evaluation value includes: Preset the corresponding relationship between the fault level and the fault degree evaluation value interval. For each change amount interval of the corresponding relationship between the fault level and the fault degree evaluation value interval, a corresponding fault level is associated; Obtain the fault degree evaluation value, and based on the mapping relationship of the fault degree evaluation value interval to which the fault degree evaluation value belongs in the corresponding relationship between the fault level and the fault degree evaluation value interval, select the fault level corresponding to the fault degree evaluation value interval as the fault level of the air-cooled steel belt dry slag discharger.

[0010] Further, setting the fault warning prompt strategy based on the fault level includes: Judge the level of the fault level of the air-cooled steel belt dry slag discharger, and set the fault warning prompt strategy of the air-cooled steel belt dry slag discharger according to the judgment result; If the level of the fault level is greater than the first preset value, set the fault warning prompt strategy of the air-cooled steel belt dry slag discharger to issue a first-level warning notice and prompt the operation and maintenance personnel to stop the machine; If the level of the fault level is greater than the second preset value, set the fault warning prompt strategy of the air-cooled steel belt dry slag discharger to issue a second-level warning notice and prompt the operation and maintenance personnel to perform shutdown maintenance; If the level of the fault level is greater than the third preset value, set the fault warning prompt strategy of the air-cooled steel belt dry slag discharger to issue a third-level warning notice and prompt the operation and maintenance personnel to stop the machine and evacuate personnel; Where the first preset value is less than the second preset value, and the second preset value is less than the third preset value.

[0011] The present invention also provides a fault monitoring and warning system for an air-cooled steel belt dry slag discharger, including: An acquisition module for acquiring the operation parameter data of the air-cooled steel belt dry slag discharger, analyzing the operation parameter data, and determining the abnormal operation parameters in the operation parameter data; An analysis module, configured to determine the fault type of the air-cooled steel belt dry slag extractor based on the abnormal operating parameters in the operating parameter data, and analyze the abnormal operating parameters in the operating parameter data to determine the data characteristics of the abnormal operating parameters; An evaluation module, configured to evaluate and calculate the fault degree of the air-cooled steel belt dry slag extractor based on the fault type and data characteristics, and obtain the fault degree evaluation value of the air-cooled steel belt dry slag extractor; A determination module, configured to determine the fault level of the air-cooled steel belt dry slag extractor according to the fault degree evaluation value, and set a fault warning prompt strategy based on the fault level; A warning module, configured to perform timely fault warning on the air-cooled steel belt dry slag extractor according to the fault warning prompt strategy.

[0012] Compared with the prior art, the beneficial effects of the fault monitoring and warning method and system for an air-cooled steel belt dry slag extractor in an embodiment of the present invention are as follows: By analyzing the operating parameter data, the present invention can timely identify the abnormal operating parameters in the equipment, thereby realizing real-time monitoring of potential faults; By analyzing the data characteristics of the abnormal operating parameters, the present invention can help determine the possible fault types, which helps engineers and technicians to locate faults more quickly and accurately; By comprehensively evaluating the data characteristics and fault types of the abnormal operating parameters, the present invention can obtain the fault degree evaluation value, help determine the severity of the fault, and contribute to the formulation of fault handling and maintenance plans; The warning strategy set based on the fault level in the present invention can realize intelligent equipment maintenance management, improve the accuracy and efficiency of equipment maintenance, reduce maintenance costs, and extend the service life of the equipment; By analyzing and evaluating the operating parameter data, the present invention can provide data support for equipment maintenance and management, enabling managers to make more informed decisions based on the data, and improving the stability and reliability of the production line. Description of the Drawings

[0013] Figure 1 It is a schematic flow structure diagram of the fault monitoring and warning method for the air-cooled steel belt dry slag extractor in an embodiment of the present invention; Figure 2 It is a schematic composition diagram of the fault monitoring and warning system for the air-cooled steel belt dry slag extractor in an embodiment of the present invention. Detailed Embodiments

[0014] The following further describes in detail the specific embodiments of the present application in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0015] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the platform or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application.

[0016] The terms "first", "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0017] In the description of the present application, it should be noted that unless otherwise clearly specified and limited, the terms "installed", "connected", "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0018] As Figure 1 shown, in an embodiment of the present application, a method for fault monitoring and early warning of an air-cooled steel belt dry slag discharger is provided, including: S100: Obtain the operation parameter data of the air-cooled steel belt dry slag discharger, and analyze the operation parameter data to determine the abnormal operation parameters in the operation parameter data; S200: Determine the fault type of the air-cooled steel belt dry slag discharger based on the abnormal operation parameters in the operation parameter data, and analyze the abnormal operation parameters in the operation parameter data to determine the data characteristics of the abnormal operation parameters; S300: Evaluate and calculate the fault degree of the air-cooled steel belt dry slag discharger based on the fault type and data characteristics to obtain the fault degree evaluation value of the air-cooled steel belt dry slag discharger; S400: Determine the fault level of the air-cooled steel belt dry slag discharger according to the fault degree evaluation value, and set a fault early warning prompt strategy based on the fault level; S500: Perform timely fault early warning on the air-cooled steel belt dry slag discharger according to the fault early warning prompt strategy.

[0019] Furthermore, the present invention analyzes the operation parameter data to promptly identify abnormal operation parameters in the device, thereby achieving real-time monitoring of potential faults. By analyzing the data characteristics of the abnormal operation parameters, the present invention can help determine possible fault types, which is conducive to engineers and technicians locating faults more quickly and accurately. By comprehensively evaluating the data characteristics and fault types of the abnormal operation parameters, the present invention can obtain a fault severity evaluation value, which helps determine the severity of the fault and is conducive to formulating fault handling and maintenance plans. The early warning strategy based on the fault level setting of the present invention can realize intelligent device maintenance management, improve the accuracy and efficiency of device maintenance, reduce maintenance costs, and extend the service life of the device. By analyzing and evaluating the operation parameter data, the present invention can provide data support for device maintenance and management, enabling managers to make more informed decisions based on data and improving the stability and reliability of the production line.

[0020] In an embodiment of the present application, a method for fault monitoring and early warning of an air-cooled steel belt dry slag extractor is provided. The method includes obtaining operation parameter data of the air-cooled steel belt dry slag extractor and analyzing the operation parameter data to determine abnormal operation parameters in the operation parameter data, including: obtaining operation parameter data of the air-cooled steel belt dry slag extractor, grouping the operation parameter data according to parameter types to obtain several operation parameter data groups; inputting each operation parameter data group into a preset screening model corresponding to the parameter type to obtain abnormal data corresponding to each operation parameter data group output by each preset screening model; counting the number of abnormal data in each operation parameter data group, and determining the parameter corresponding to the operation parameter data group with the number of abnormal data greater than a preset number as the abnormal operation parameter in the operation parameter data.

[0021] Specifically, data of various operation parameters are obtained from the air-cooled steel belt dry slag extractor, and these data may include various parameters such as temperature, pressure, current, rotation speed, vibration, etc. The obtained operation parameter data are grouped according to parameter types. For example, all temperature data are placed in one group, all pressure data are placed in another group, and so on. For each operation parameter data group, it is input into a preset screening model corresponding to the parameter type. These models are models constructed based on statistical methods, machine learning models, or other data analysis techniques for identifying abnormal data. The model outputs abnormal data corresponding to each operation parameter data group, and these abnormal data may represent abnormal situations in device operation, which may be abnormal data points caused by device faults, wear, or other problems. Count the number of abnormal data in each operation parameter data group. If the number of abnormal data is greater than a preset threshold, it can be determined that the parameter corresponding to the operation parameter data group is an abnormal operation parameter. This step can achieve real-time fault early warning of the air-cooled steel belt dry slag extractor and promptly detect abnormal situations in device operation through real-time monitoring and analysis of the operation parameter data.

[0022] In an embodiment of the present application, a method for fault monitoring and early warning of an air-cooled steel belt dry slag discharger is provided. Determining the fault type of the air-cooled steel belt dry slag discharger based on abnormal operation parameters in the operation parameter data includes: presetting several preset fault types and the abnormal operation parameters corresponding to each fault type; comparing and analyzing each abnormal operation parameter corresponding to each fault type with all the abnormal operation parameters in the operation parameter data one by one, and counting the number of parameters with consistent abnormal operation parameter comparisons; determining the preset fault type corresponding to the largest number of parameters as the fault type of the air-cooled steel belt dry slag discharger.

[0023] Specifically, several possible fault types are preset, as well as the abnormal operation parameters corresponding to each fault type, and these abnormal operation parameters are determined through expert knowledge or data analysis before; comparing and analyzing each abnormal operation parameter corresponding to each fault type with the abnormal operation parameters in the operation parameter data of the air-cooled steel belt dry slag discharger one by one, and for each fault type, counting the number of parameters that are consistent with the abnormal operation parameters in the actual operation parameter data; determining the preset fault type corresponding to the largest number of parameters as the fault type of the air-cooled steel belt dry slag discharger. Through this comparison and analysis step, automatic diagnosis of the fault type of the air-cooled steel belt dry slag discharger can be realized, eliminating the need for manual analysis of abnormal parameters one by one, improving the diagnosis efficiency; by counting the number of parameters with consistent abnormal operation parameter comparisons, the fault type can be determined more accurately, avoiding the influence of subjective factors on fault diagnosis; accurately determining the fault type is helpful for intelligent equipment maintenance management, enabling engineers and technicians to locate faults more quickly and accurately, improving the maintenance efficiency; through this method, the fault types and corresponding abnormal operation parameters that occur during the actual operation of the air-cooled steel belt dry slag discharger can be gradually accumulated, providing valuable experience and data support for future fault diagnosis and prevention.

[0024] In an embodiment of the present application, a method for fault monitoring and early warning of an air-cooled steel belt dry slag discharger is provided. Analyzing the abnormal operation parameters in the operation parameter data to determine the data characteristics of the abnormal operation parameters includes: obtaining the operation parameter data group corresponding to each abnormal operation parameter in the operation parameter data, and determining the ratio of abnormal data in the operation parameter data group and the average value of the operation parameter data group; determining the ratio of abnormal data in the operation parameter data group and the average value of the operation parameter data group as the data characteristics of each abnormal operation parameter.

[0025] Specifically, operation parameter data groups corresponding to each abnormal operation parameter are screened out from the overall operation parameter data. These data groups contain all data points of the abnormal operation parameter. For each operation parameter data group, calculate the ratio of abnormal data therein, that is, the ratio of the number of abnormal data points to the total number of data points, to determine the distribution of abnormal data in the overall data. For each operation parameter data group, calculate the average value of all its data points to reflect the average performance of this abnormal operation parameter. Determine the data characteristics of each abnormal operation parameter as the ratio of abnormal data and the average value of the operation parameter data group. By calculating the ratio of abnormal data, this step can understand the distribution of abnormal data in the overall data, whether it is an occasional event or a persistent problem. By calculating the average value of the operation parameter data group, it can understand the typical performance of the abnormal operation parameter, which helps to determine the typical characteristics of the abnormal data. The data characteristics can be used as the basis for fault diagnosis. When the ratio of abnormal data of a certain abnormal operation parameter is high, or its average value deviates from the normal value, it can be an important reference for judging the degree of the fault. The determination of data characteristics helps to realize intelligent equipment maintenance management, enabling engineers and technicians to locate faults more quickly and accurately and improve maintenance efficiency.

[0026] In an embodiment of the present application, a method for fault monitoring and early warning of an air-cooled steel belt dry slag extractor is provided. Based on the fault type and data characteristics, the fault degree of the air-cooled steel belt dry slag extractor is evaluated and calculated to obtain the fault degree evaluation value of the air-cooled steel belt dry slag extractor, including: obtaining the ratio of abnormal data and the average value of the operation parameter data group in the operation parameter data group corresponding to each abnormal operation parameter, and taking the ratio of abnormal data in the operation parameter data group as the abnormal coefficient of the abnormal operation parameter; presetting the fault degree coefficient of the fault type of the air-cooled steel belt dry slag extractor, and evaluating and taking values for the average value of the operation parameter data group to obtain the abnormal evaluation value of the abnormal operation parameter; calculating the fault degree evaluation value of the air-cooled steel belt dry slag extractor based on the fault degree coefficient, the abnormal coefficient, and the abnormal evaluation value of the abnormal operation parameter. The calculation formula for the fault degree evaluation value of the air-cooled steel belt dry slag extractor is: , where P is the fault degree evaluation value of the air-cooled steel belt dry slag extractor, α is the fault degree coefficient of the fault type of the air-cooled steel belt dry slag extractor, ai is the abnormal coefficient of the i-th abnormal operation parameter, Di is the data average value of the i-th abnormal operation parameter, and n is the number of abnormal operation parameters.

[0027] Specifically, for each abnormal operating parameter, calculate the proportion of abnormal data in the corresponding operating parameter data group, and use this proportion as the abnormal coefficient of the abnormal operating parameter. The abnormal coefficient reflects the degree of abnormality of the abnormal operating parameter in the overall operating parameter data; preset the fault degree coefficients for the fault types of the air-cooled steel belt dry slag extractor, and these coefficients are determined based on historical data analysis to reflect the severity of different fault types; for each abnormal operating parameter, based on the fault degree coefficient, evaluate and obtain the average value of its corresponding operating parameter data group to get the abnormal evaluation value of the abnormal operating parameter, which reflects the performance of the abnormal operating parameter in terms of fault degree; based on the fault degree coefficient, the abnormal coefficient and the abnormal evaluation value of the abnormal operating parameter, calculate the fault degree evaluation value of the air-cooled steel belt dry slag extractor, which reflects the comprehensive situation of the overall fault degree. This step can achieve a comprehensive evaluation of the fault degree of the air-cooled steel belt dry slag extractor by combining the abnormal coefficient, the fault degree coefficient and the abnormal evaluation value, avoiding the one-sidedness of a single index; the fault degree evaluation value can help determine the severity of the fault of the air-cooled steel belt dry slag extractor, contribute to giving priority to dealing with serious faults and improving the reliability of the equipment; the determination of the fault degree evaluation value helps to realize intelligent equipment maintenance management, enabling engineers and technicians to locate faults more quickly and accurately and improving the maintenance efficiency; through the fault degree evaluation value, it is possible to predict the possible future fault types and development trends, which helps to take maintenance measures in advance and reduce the fault risk.

[0028] In an embodiment of the present application, a fault monitoring and warning method for an air-cooled steel belt dry slag extractor is provided. Determining the fault level of the air-cooled steel belt dry slag extractor according to the fault degree evaluation value includes: presetting the correspondence between the fault level - fault degree evaluation value interval, and for each change amount interval of the correspondence between the fault level - fault degree evaluation value interval, a corresponding fault level is associated; obtaining the fault degree evaluation value, and based on the mapping relationship of the fault degree evaluation value interval to which the fault degree evaluation value belongs in the correspondence between the fault level - fault degree evaluation value interval, selecting the fault level corresponding to the fault degree evaluation value interval as the fault level of the air-cooled steel belt dry slag extractor.

[0029] Specifically, for each variation interval of each key feature parameter, a corresponding relationship between the fault level - fault degree evaluation value interval is preset. This corresponding relationship is determined based on historical data and is used to map the fault degree evaluation value to the corresponding fault level. From the fault degree evaluation values obtained in the previous step, the fault degree evaluation value of the air-cooled steel belt dry slag extractor is obtained. Based on the fault degree evaluation value interval, a mapping relationship is searched within the corresponding relationship between the fault level - fault degree evaluation value interval, and the fault level corresponding to the fault degree evaluation value interval is selected and determined as the fault level of the air-cooled steel belt dry slag extractor. Through this mapping relationship, the fault level of the air-cooled steel belt dry slag extractor can be determined according to the fault degree evaluation value, which helps to classify and evaluate the severity of the fault; the determination of the fault level helps to realize intelligent equipment maintenance management, enabling engineers and technicians to process faults of different levels more quickly and accurately, and improving the maintenance efficiency.

[0030] In an embodiment of the present application, a fault monitoring and early warning method for an air-cooled steel belt dry slag extractor is provided. The fault early warning prompt strategy based on the fault level includes: judging the level of the fault level of the air-cooled steel belt dry slag extractor, and setting the fault early warning prompt strategy of the air-cooled steel belt dry slag extractor according to the judgment result; if the level of the fault level is greater than the first preset value, setting the fault early warning prompt strategy of the air-cooled steel belt dry slag extractor to issue a first-level early warning notice and prompt the operation and maintenance personnel to stop the machine; if the level of the fault level is greater than the second preset value, setting the fault early warning prompt strategy of the air-cooled steel belt dry slag extractor to issue a second-level early warning notice and prompt the operation and maintenance personnel to perform shutdown maintenance; if the level of the fault level is greater than the third preset value, setting the fault early warning prompt strategy of the air-cooled steel belt dry slag extractor to issue a third-level early warning notice and prompt the operation and maintenance personnel to stop the machine and evacuate personnel; where the first preset value is less than the second preset value, and the second preset value is less than the third preset value.

[0031] Specifically, according to the corresponding relationship between the fault level - fault degree evaluation value intervals in the previous steps, determine the fault level of the air-cooled steel belt dry slag discharger. This level is determined based on the mapping relationship and reflects the severity of the current fault of the air-cooled steel belt dry slag discharger. If the level of the fault grade is greater than the first preset value, set the fault warning prompt strategy of the air-cooled steel belt dry slag discharger to issue a first-level warning notice and prompt the operation and maintenance personnel to stop the machine. If the level of the fault grade is greater than the second preset value, set the fault warning prompt strategy of the air-cooled steel belt dry slag discharger to issue a second-level warning notice and prompt the operation and maintenance personnel to perform shutdown maintenance. If the level of the fault grade is greater than the third preset value, set the fault warning prompt strategy of the air-cooled steel belt dry slag discharger to issue a third-level warning notice and prompt the operation and maintenance personnel to stop the machine and evacuate personnel. According to the requirements, the first preset value is less than the second preset value, and the second preset value is less than the third preset value. These preset values are determined according to safety standards to ensure that appropriate warning and maintenance measures can be taken in a timely manner. By setting fault warning prompt strategies at different levels in this step, it can be ensured that when faults of different severities occur, the operation and maintenance personnel can be notified in a timely manner and corresponding safety measures can be taken to ensure the safety of equipment and personnel. For faults of different levels, setting different warning prompt strategies helps to improve the maintenance efficiency, enabling the operation and maintenance personnel to carry out maintenance and handling targeted, reducing the downtime and losses. Through the setting of preset values and the formulation of warning strategies, risks can be effectively managed, avoiding losses caused by serious faults to equipment and personnel, and improving the production safety and reliability.

[0032] As Figure 2 shown, in the embodiment of the present application, a fault monitoring and warning system for an air-cooled steel belt dry slag discharger is provided, including: an acquisition module for acquiring the operation parameter data of the air-cooled steel belt dry slag discharger and analyzing the operation parameter data to determine the abnormal operation parameters in the operation parameter data; an analysis module for determining the fault type of the air-cooled steel belt dry slag discharger based on the abnormal operation parameters in the operation parameter data and analyzing the abnormal operation parameters in the operation parameter data to determine the data characteristics of the abnormal operation parameters; an evaluation module for evaluating and calculating the fault degree of the air-cooled steel belt dry slag discharger based on the fault type and data characteristics to obtain the fault degree evaluation value of the air-cooled steel belt dry slag discharger; a determination module for determining the fault grade of the air-cooled steel belt dry slag discharger according to the fault degree evaluation value and setting the fault warning prompt strategy based on the fault grade; and a warning module for giving a timely fault warning to the air-cooled steel belt dry slag discharger according to the fault warning prompt strategy.

[0033] In summary, the embodiments of the present invention provide a method and system for fault monitoring and early warning of an air-cooled steel belt dry slag discharger, which include: analyzing the operation parameter data of the air-cooled steel belt dry slag discharger to determine the abnormal operation parameters therein; determining the fault type of the air-cooled steel belt dry slag discharger based on the abnormal operation parameters, and determining the data characteristics by analyzing the abnormal operation parameters; evaluating and calculating the fault degree of the air-cooled steel belt dry slag discharger based on the fault type and data characteristics to obtain a fault degree evaluation value; determining the fault level of the air-cooled steel belt dry slag discharger according to the fault degree evaluation value, and setting a fault early warning prompt strategy based on the fault level to give an early warning of the fault to the air-cooled steel belt dry slag discharger in a timely manner. By monitoring and analyzing the operation parameter data of the air-cooled steel belt dry slag discharger in real time, the present invention can detect abnormal situations in the equipment operation early, predict potential faults, and give corresponding early warning prompts to avoid adverse effects on production caused by equipment faults.

[0034] Finally, it should be noted that: Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

[0035] The above is only one embodiment of the present invention, but it cannot be used to limit the scope of the present invention. Any structural changes made according to the present invention, as long as they do not deviate from the essence of the present invention, should be regarded as falling within the protection scope of the present invention and being restricted. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process and related descriptions of the above-described platform can refer to the corresponding process in the foregoing platform embodiments, and will not be repeated here.

[0036] The term "including" or any other similar term is intended to cover non-exclusive inclusion, so that a process, platform, article, or device / platform including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent in these processes, platforms, articles, or devices / platforms.

[0037] So far, the technical solutions of the present invention have been described in combination with the further embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to closely related technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0038] The above is only the preferred embodiment of the present invention, and is not used to limit the protection scope of the present invention.

Claims

1. A fault monitoring and early warning method for an air-cooled steel strip dry slag discharger, characterized in that: include: Obtaining operating parameter data of the air-cooled steel strip dry slag discharger, analyzing the operating parameter data, and determining abnormal operating parameters in the operating parameter data; Determine the fault type of the air-cooled steel strip dry slag discharge machine based on the abnormal operating parameters in the operating parameter data, analyze the abnormal operating parameters in the operating parameter data, and determine the data characteristics of the abnormal operating parameters; Based on the fault type and data characteristics, the fault degree of the air-cooled steel strip dry slag discharger is evaluated and calculated to obtain the fault degree evaluation value of the air-cooled steel strip dry slag discharger; Determine the fault level of the air-cooled steel strip dry slag discharger according to the fault level assessment value, and set a fault warning prompt strategy based on the fault level; According to the fault warning prompt strategy, timely fault warning is given to the air-cooled steel strip dry slag discharge machine.

2. A fault monitoring and early warning method for an air-cooled steel strip dry slag discharger according to claim 1, characterized in that: The obtaining of the operating parameter data of the air-cooled steel strip dry slag discharger, and analyzing the operating parameter data to determine abnormal operating parameters in the operating parameter data include: Acquire the operating parameter data of the air-cooled steel strip dry slag discharge machine, and group the operating parameter data according to parameter types to obtain a plurality of operating parameter data groups; Input each operating parameter data group into a preset screening model corresponding to the parameter type, and obtain abnormal data corresponding to each operating parameter data group output by each preset screening model; The number of abnormal data in each operating parameter data group is counted, and the parameters corresponding to the operating parameter data group whose number of abnormal data is greater than a preset number are determined as abnormal operating parameters in the operating parameter data.

3. A fault monitoring and early warning method for an air-cooled steel strip dry slag discharger according to claim 2, characterized in that: The method of determining the fault type of the air-cooled steel strip dry slag discharger based on the abnormal operating parameters in the operating parameter data includes: Preset several preset fault types and abnormal operation parameters corresponding to each fault type; Compare and analyze all abnormal operating parameters of each fault type with all abnormal operating parameters in the operating parameter data one by one, and count the number of parameters with consistent abnormal operating parameters; The preset fault type corresponding to the largest number of parameters is determined as the fault type of the air-cooled steel strip dry slag discharge machine.

4. A fault monitoring and early warning method for an air-cooled steel strip dry slag discharger according to claim 3, characterized in that: The analyzing the abnormal operating parameters in the operating parameter data to determine the data characteristics of the abnormal operating parameters includes: Obtaining an operating parameter data group corresponding to each abnormal operating parameter in the operating parameter data, and determining a proportion of abnormal data in the operating parameter data group and an average value of the operating parameter data group; The proportion of abnormal data in the operating parameter data group and the average value of the operating parameter data group are determined as data features of each abnormal operating parameter.

5. A fault monitoring and early warning method for an air-cooled steel strip dry slag discharger according to claim 4, characterized in that: The fault degree of the air-cooled steel strip dry slag discharger is evaluated and calculated based on the fault type and data characteristics to obtain a fault degree evaluation value of the air-cooled steel strip dry slag discharger, including: Obtain the proportion of abnormal data in the operating parameter data group corresponding to each abnormal operating parameter and the average value of the operating parameter data group, and use the proportion of abnormal data in the operating parameter data group as the abnormal coefficient of the abnormal operating parameter; Presetting the fault degree coefficient of the air-cooled steel strip dry slag discharge machine fault type, and evaluating and valuing the average value of the operating parameter data group to obtain the abnormal evaluation value of the abnormal operating parameter; The fault degree evaluation value of the air-cooled steel strip dry slag discharger is calculated based on the fault degree coefficient and the abnormal coefficient and abnormal evaluation value of the abnormal operation parameter. The calculation formula of the fault degree evaluation value of the air-cooled steel strip dry slag discharger is: ， Among them, P is the fault degree evaluation value of the air-cooled steel strip dry slag discharger, α is the fault degree coefficient of the fault type of the air-cooled steel strip dry slag discharger, ai is the abnormal coefficient of the i-th abnormal operating parameter, Di is the data average of the i-th abnormal operating parameter, and n is the number of abnormal operating parameters.

6. A fault monitoring and early warning method for an air-cooled steel strip dry slag discharger according to claim 5, characterized in that: Determining the fault level of the air-cooled steel strip dry slag discharger according to the fault level evaluation value includes: A corresponding relationship between fault level and fault degree evaluation value interval is preset, and the corresponding relationship between fault level and fault degree evaluation value interval is associated with a corresponding fault level for each variation interval; Obtain a fault severity assessment value, and based on the mapping relationship between the fault severity assessment value interval to which the fault severity assessment value belongs and the fault level corresponding to the fault severity assessment value interval, select the fault level corresponding to the fault severity assessment value interval as the fault level of the air-cooled steel strip dry slag discharger.

7. A fault monitoring and early warning method for an air-cooled steel strip dry slag discharger according to claim 6, characterized in that: The fault warning prompt strategy is set based on the fault level, including: Determine the level of the fault level of the air-cooled steel strip dry slag discharger, and set the fault early warning prompt strategy of the air-cooled steel strip dry slag discharger according to the determination result; If the fault level is greater than the first preset value, the fault warning prompt strategy of the air-cooled steel strip dry slag discharge machine is set to issue a first-level warning notification and prompt the operation and maintenance personnel to shut down the machine; If the fault level is greater than the second preset value, the fault warning prompt strategy of the air-cooled steel strip dry slag discharger is set to issue a second-level warning notification and prompt the operation and maintenance personnel to shut down the machine for maintenance; If the fault level is greater than the third preset value, the fault warning prompt strategy of the air-cooled steel strip dry slag discharger is set to issue a third-level warning notification and prompt the operation and maintenance personnel to shut down the machine and evacuate personnel; The first preset value is smaller than the second preset value, and the second preset value is smaller than the third preset value.

8. A fault monitoring and early warning system for an air-cooled steel strip dry slag discharger, characterized in that: include: An acquisition module is used to acquire the operating parameter data of the air-cooled steel strip dry slag discharge machine, analyze the operating parameter data, and determine abnormal operating parameters in the operating parameter data; An analysis module, used to determine the fault type of the air-cooled steel strip dry slag discharge machine based on the abnormal operating parameters in the operating parameter data, and to analyze the abnormal operating parameters in the operating parameter data to determine the data characteristics of the abnormal operating parameters; An evaluation module is used to evaluate and calculate the fault degree of the air-cooled steel strip dry slag discharger based on the fault type and data characteristics, and obtain a fault degree evaluation value of the air-cooled steel strip dry slag discharger; A determination module is used to determine the fault level of the air-cooled steel strip dry slag discharge machine according to the fault level evaluation value, and to set a fault early warning prompt strategy based on the fault level; The early warning module is used to provide timely fault warning for the air-cooled steel strip dry slag discharge machine according to the fault early warning prompt strategy.

Images