Intelligent detection method and device for blast furnace blower static vane oil cylinder leakage

Abstract

The application discloses a kind of blast furnace blower static vane oil cylinder leakage intelligent detection method and device, the method includes: obtaining the current first collection parameter and second collection parameter of blast furnace blower static vane oil cylinder;Determine whether the first collection parameter and second collection parameter satisfy first detection condition;Determine whether the first collection parameter and second collection parameter satisfy second detection condition;When not satisfying second detection condition, generate first detection result and first early warning information, when satisfying second detection condition, generate second detection result and second early warning information.The first collection parameter and second collection parameter of the present application are judged by collection to determine whether the current static vane oil cylinder exists leakage and judge the severity of leakage, and when confirming that the corresponding leakage condition is detected, the detection result generates corresponding early warning information to remind technical personnel, realize the fault early warning of equipment in production process, to reduce the failure of hydraulic system.

Description

Technical Field

[0001] This invention relates to the field of blast furnace blasting technology, and in particular to an intelligent detection method and device for leakage in the oil cylinder of the stationary blade of a blast furnace blower. Background Technology

[0002] The blast furnace blower is the "heart" of the blast furnace and the most important power equipment for blast furnace smelting. It not only directly provides the oxygen needed for blast furnace smelting, but also provides the necessary power for the movement of the gas flow in the furnace to overcome the resistance of the burden column.

[0003] The stationary blade cylinder is an important actuator of the blast furnace blower and a core part of the axial flow fan. The size and precise control of the stationary blade opening are crucial for the blower to provide a constant air flow and pressure for the blast furnace. When the stationary blade malfunctions, it can cause the blower's anti-surge valve to activate, resulting in air pressure fluctuations or even complete venting, which can lead to slag charging accidents at the blast furnace tuyeres. Ensuring the normal operation of the stationary blade adjustment plays a vital role in blast furnace production.

[0004] The stator cylinder is directly related to the normal operation of the blower and is a component with a relatively high probability of defects. The most common defect in the stator cylinder system is internal leakage caused by malfunctions of internal components in the control housing, which leads to the stator cylinder's inability to operate normally. Internal leakage in the cylinder is often insidious, lacks reliable means for timely detection, and is difficult to assess. Multiple instances of blower shutdowns due to internal leakage have occurred.

[0005] Therefore, there is an urgent need for a method or device that can intelligently detect leakage in the oil cylinder of the stationary blade of a blast furnace blower. Summary of the Invention

[0006] In view of the above problems, the present invention is proposed to provide an intelligent detection method and device for leakage of the blast furnace blower stator cylinder that overcomes or at least partially solves the above problems.

[0007] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0008] According to a first aspect of the present invention, an intelligent detection method for leakage in the stationary blade cylinder of a blast furnace blower is provided, the intelligent detection method for leakage in the stationary blade cylinder of a blast furnace blower includes:

[0009] Collect field data of the blast furnace blower, and obtain the first and second collection parameters of the current blast furnace blower stationary blade cylinder. The first collection parameter is the threshold of the oil inlet and return oil temperature difference of the cylinder, and the second collection parameter is the threshold of the stationary blade angle difference.

[0010] The first detection operation is performed to determine whether the first acquisition parameter and the second acquisition parameter meet the first detection condition. The first detection condition includes a first necessary condition and a second necessary condition that are met simultaneously. The first necessary condition is that the oil inlet and return oil temperature difference threshold of the oil cylinder meets the first threshold range. The second necessary condition is that the stationary vane angle difference threshold meets the second threshold range.

[0011] When the first acquisition parameter and the second acquisition parameter meet the first detection condition, the second detection operation is performed to determine whether the first acquisition parameter and the second acquisition parameter meet the second detection condition. The second detection condition includes a third necessary condition and a fourth necessary condition that are met simultaneously. The third necessary condition is that the oil inlet and return oil temperature difference threshold of the oil cylinder meets the third threshold range. The fourth necessary condition is that the stationary vane angle difference threshold meets the fourth threshold range. The minimum value of the third threshold range is greater than the minimum value of the first threshold range, and the minimum value of the fourth threshold range is greater than the minimum value of the second threshold range.

[0012] When the first acquisition parameter and the second acquisition parameter do not meet the second detection condition, a first detection result is generated and fed back to the terminal device to generate a first warning message accordingly. When the first acquisition parameter and the second acquisition parameter meet the second detection condition, a second detection result is generated and fed back to the terminal device to generate a second warning message accordingly.

[0013] In some embodiments of the present invention, the method includes:

[0014] The oil inlet temperature of the blast furnace blower's stationary blade oil cylinder and the oil return temperature of the oil cylinder on the oil outlet line are collected respectively. The difference between the oil inlet and return temperatures of the oil cylinder is calculated to obtain the oil cylinder inlet and return temperature difference threshold.

[0015] Collect the feedback value of the blower's stationary blade angle and the set value of the blower's stationary blade angle, and calculate the difference based on the feedback value and the set value of the blower's stationary blade angle to obtain the stationary blade angle difference threshold.

[0016] In some embodiments of the present invention, before acquiring the first acquisition parameter and the second acquisition parameter, the method further includes:

[0017] When the blast furnace blower stator cylinder is running normally, the corresponding detection parameters during normal operation are obtained as standard detection parameters. The detection parameters include hydraulic oil pressure threshold, blower intake air volume threshold, and synchronous motor stator current threshold.

[0018] It also acquires the detection parameters of the blast furnace blower stator cylinder in real time and uses them as auxiliary detection parameters.

[0019] In some embodiments of the present invention, when at least one of the first necessary condition and the second necessary condition is satisfied, the method further includes:

[0020] The third detection operation compares the auxiliary detection parameters with the standard detection parameters to determine whether the auxiliary detection parameters of the current blast furnace blower stationary blade cylinder meet the third detection condition. The third detection condition is that when the stationary blade angle of the blast furnace blower stationary blade cylinder is adjusted, the auxiliary detection parameters conform to linear change, and the amplitude range of the auxiliary detection parameter change is within the range of the change value corresponding to the standard detection parameter.

[0021] When the auxiliary detection parameters meet the third detection condition, an auxiliary detection result is generated and fed back to the terminal device to generate corresponding auxiliary warning information based on the auxiliary detection result.

[0022] In some embodiments of the present invention, the method further includes:

[0023] Record the first acquisition parameter, the second acquisition parameter, the standard detection parameter, and the auxiliary detection parameter respectively to generate an acquisition parameter set;

[0024] Record the detection results corresponding to the first acquisition parameter, the second acquisition parameter, the standard detection parameter, and the auxiliary detection parameter respectively, and generate a set of detection results;

[0025] Collect the physical and environmental parameters of the current blast furnace blower stator cylinder to generate a set of basic parameters;

[0026] Based on AI neural networks, a leakage detection model for the stator cylinder is established using big data and machine learning based on the collected parameter set, the detection result set, and the basic parameter set. The coupling relationship between the collected parameter set, the detection result set, and the basic parameter set is analyzed and confirmed, and the first detection condition, the second detection condition, and / or the third detection condition are adjusted accordingly.

[0027] According to a second aspect of the present invention, a smart detection device for leakage in the stationary blade cylinder of a blast furnace blower is provided, the smart detection device for leakage in the stationary blade cylinder of a blast furnace blower includes:

[0028] The data acquisition module is used to collect field data of the blast furnace blower and obtain the first and second acquisition parameters of the current stationary blade cylinder of the blast furnace blower. The first acquisition parameter is the threshold value of the oil inlet and outlet oil temperature difference of the cylinder, and the second acquisition parameter is the threshold value of the stationary blade angle difference.

[0029] The first detection module is used to perform a first detection operation to determine whether the first acquisition parameter and the second acquisition parameter meet the first detection condition. The first detection condition includes a first necessary condition and a second necessary condition that are met simultaneously. The first necessary condition is that the oil inlet and outlet oil temperature difference threshold of the oil cylinder meets the first threshold range. The second necessary condition is that the stationary vane angle difference threshold meets the second threshold range.

[0030] The second detection module is used to perform a second detection operation when the first acquisition parameter and the second acquisition parameter meet the first detection condition, and to determine whether the first acquisition parameter and the second acquisition parameter meet the second detection condition. The second detection condition includes a third necessary condition and a fourth necessary condition that are met simultaneously. The third necessary condition is that the oil inlet and return oil temperature difference threshold of the oil cylinder meets the third threshold range. The fourth necessary condition is that the stationary vane angle difference threshold meets the fourth threshold range. The minimum value of the third threshold range is greater than the minimum value of the first threshold range, and the minimum value of the fourth threshold range is greater than the minimum value of the second threshold range.

[0031] The early warning module is used to generate a first detection result and feed it back to the terminal device when the first acquisition parameter and the second acquisition parameter do not meet the second detection condition, so as to generate a first early warning information accordingly based on the first detection result; and to generate a second detection result and feed it back to the terminal device when the first acquisition parameter and the second acquisition parameter meet the second detection condition, so as to generate a second early warning information accordingly based on the second detection result.

[0032] In some embodiments of the present invention, the data acquisition module is specifically used for:

[0033] The oil inlet temperature of the blast furnace blower's stationary blade oil cylinder and the oil return temperature of the oil cylinder on the oil outlet line are collected respectively. The difference between the oil inlet and return temperatures of the oil cylinder is calculated to obtain the oil cylinder inlet and return temperature difference threshold.

[0034] Collect the feedback value of the blower's stationary blade angle and the set value of the blower's stationary blade angle, and calculate the difference based on the feedback value and the set value of the blower's stationary blade angle to obtain the stationary blade angle difference threshold.

[0035] In some embodiments of the present invention, before acquiring the first acquisition parameter and the second acquisition parameter, the data acquisition module is further configured to:

[0036] When the blast furnace blower stator cylinder is running normally, the corresponding detection parameters during normal operation are obtained as standard detection parameters. The detection parameters include hydraulic oil pressure threshold, blower intake air volume threshold, and synchronous motor stator current threshold.

[0037] It also acquires the detection parameters of the blast furnace blower stator cylinder in real time and uses them as auxiliary detection parameters.

[0038] In some embodiments of the present invention, the apparatus further includes:

[0039] The third detection module is used to perform a third detection operation when at least one of the first necessary condition and the second necessary condition is met, comparing the auxiliary detection parameters with the standard detection parameters, and determining whether the auxiliary detection parameters of the current blast furnace blower stationary blade cylinder meet the third detection condition. The third detection condition is that when the stationary blade angle of the blast furnace blower stationary blade cylinder is adjusted, the auxiliary detection parameters conform to linear change, and the amplitude range of the change of the auxiliary detection parameters is within the range of the change value corresponding to the standard detection parameters.

[0040] If the auxiliary detection parameters meet the third detection condition, the auxiliary detection result is generated through the early warning module and fed back to the terminal device to generate auxiliary early warning information accordingly.

[0041] In some embodiments of the present invention, the apparatus further includes a model training module for:

[0042] Record the first acquisition parameter, the second acquisition parameter, the standard detection parameter, and the auxiliary detection parameter respectively to generate an acquisition parameter set;

[0043] Record the detection results corresponding to the first acquisition parameter, the second acquisition parameter, the standard detection parameter, and the auxiliary detection parameter respectively, and generate a set of detection results;

[0044] Collect the physical and environmental parameters of the current blast furnace blower stator cylinder to generate a set of basic parameters;

[0045] Based on AI neural networks, a leakage detection model for the stator cylinder is established using big data and machine learning based on the collected parameter set, the detection result set, and the basic parameter set. The coupling relationship between the collected parameter set, the detection result set, and the basic parameter set is analyzed and confirmed, and the first detection condition, the second detection condition, and / or the third detection condition are adjusted accordingly.

[0046] The technical solutions provided in this embodiment of the invention, specifically a method and device for intelligent detection of leakage in the stationary blade cylinder of a blast furnace blower, have at least the following technical effects or advantages:

[0047] 1. This invention uses first and second acquisition parameters to determine whether there is leakage in the current stationary vane cylinder. The severity of the leakage is determined based on whether the first and second acquisition parameters meet the first or second detection conditions. When a corresponding leakage is detected, the detection results are used to generate corresponding early warning information to remind technicians. This achieves fault warning and alarm for equipment in the production process, thereby reducing hydraulic system failures, improving hydraulic system efficiency, preventing environmental pollution and reducing hydraulic medium loss, and achieving the goal of reducing or even avoiding leakage, thus reducing the impact of equipment damage on production.

[0048] 2. The standard detection parameters and auxiliary detection parameters collected in the embodiments of the present invention are compared with the standard detection parameters by performing a third detection operation to determine whether the auxiliary detection parameters of the current blast furnace blower stationary blade cylinder meet the third detection conditions. Based on the auxiliary detection results obtained from the third detection conditions, the accuracy of the stationary blade cylinder is greatly improved.

[0049] 3. This invention utilizes an AI neural network to analyze and confirm the coupling relationship between the collected parameter set, detection result set, and basic parameter set by combining real-world examples such as recorded collection parameter sets, detection result sets, and basic parameter sets. This ensures that the actual operating state of the blast furnace blower stator cylinder is consistent with real-world scenarios. Furthermore, the threshold ranges corresponding to the first, second, and / or third detection conditions can be adjusted based on the analysis results. This makes it suitable for leak detection of blast furnace blower stator cylinders of different specifications, types, and production environments, demonstrating good applicability.

[0050] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart illustrating an intelligent detection method for leakage in the stationary blade cylinder of a blast furnace blower, provided in an embodiment of the present invention;

[0053] Figure 2 This is a flowchart illustrating another embodiment of the present invention;

[0054] Figure 3 This is a flowchart illustrating another embodiment of the present invention;

[0055] Figure 4 This is a flowchart illustrating another embodiment of the present invention;

[0056] Figure 5 This is a flowchart illustrating another embodiment of the present invention;

[0057] Figure 6 This is a schematic diagram of the principle structure of an intelligent detection device for leakage of the oil cylinder of the stationary blade of a blast furnace blower, provided in an embodiment of the present invention. Detailed Implementation

[0058] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings.

[0059] The accompanying drawings illustrate various structural schematics according to embodiments of the present disclosure. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0060] In the context of this disclosure, when a layer / component is referred to as being "above" another layer / component, that layer / component may be directly above the other layer / component, or there may be an intermediate layer / component between them. Additionally, if a layer / component is "above" another layer / component in one orientation, then when the orientation is reversed, that layer / component may be "below" the other layer / component. In the context of this disclosure, similar or identical components may be denoted by the same or similar reference numerals.

[0061] To better understand the above technical solutions, the following will describe the above technical solutions in detail with reference to specific implementation methods. It should be understood that the embodiments of this disclosure and the specific features in the embodiments are detailed descriptions of the technical solutions of this application, rather than limitations on the technical solutions of this application. In the absence of conflict, the embodiments of this application and the technical features in the embodiments can be combined with each other.

[0062] Figure 1 This is a flowchart illustrating an intelligent detection method for leakage in the stationary blade cylinder of a blast furnace blower, as provided in an embodiment of the present invention. Figure 1 As shown, the intelligent detection method for leakage in the blast furnace blower stator cylinder includes:

[0063] S101. Collect field data of the blast furnace blower, and obtain the first and second collection parameters of the current blast furnace blower stationary blade cylinder. The first collection parameter is the threshold value of the oil inlet and outlet oil temperature difference of the cylinder, and the second collection parameter is the threshold value of the stationary blade angle difference.

[0064] S102. Perform the first detection operation to determine whether the first acquisition parameter and the second acquisition parameter meet the first detection condition. The first detection condition includes a first necessary condition and a second necessary condition that are met simultaneously. The first necessary condition is that the oil cylinder inlet and outlet oil temperature difference threshold meets the first threshold range. The second necessary condition is that the stationary vane angle difference threshold meets the second threshold range.

[0065] S103. When the first acquisition parameter and the second acquisition parameter meet the first detection condition, the second detection operation is performed to determine whether the first acquisition parameter and the second acquisition parameter meet the second detection condition. The second detection condition includes a third necessary condition and a fourth necessary condition that are met simultaneously. The third necessary condition is that the oil inlet and return oil temperature difference threshold of the oil cylinder meets the third threshold range. The fourth necessary condition is that the stationary vane angle difference threshold meets the fourth threshold range. The minimum value of the third threshold range is greater than the minimum value of the first threshold range, and the minimum value of the fourth threshold range is greater than the minimum value of the second threshold range.

[0066] S104. When the first acquisition parameters and the second acquisition parameters do not meet the second detection conditions, a first detection result is generated and fed back to the terminal device to generate a first warning message accordingly based on the first detection result. When the first acquisition parameters and the second acquisition parameters meet the second detection conditions, a second detection result is generated and fed back to the terminal device to generate a second warning message accordingly based on the second detection result.

[0067] In this embodiment of the invention, the terminal device is a computer or server with a display device, used to calculate the corresponding detection results and generate corresponding early warning information, and to remind the operator through the display function of the display device.

[0068] The intelligent detection method for leakage in the stationary blade cylinder of the blast furnace blower described in this embodiment of the invention determines whether there is leakage in the current stationary blade cylinder by collecting first and second acquisition parameters. It also determines the severity of the leakage based on whether the first and second acquisition parameters meet the first or second detection conditions. Upon confirming the detection of a leakage, the method generates corresponding early warning information to alert technicians. This achieves fault warning and alarm for equipment in the production process, thereby reducing hydraulic system failures, improving hydraulic system efficiency, preventing environmental pollution, and reducing hydraulic medium loss. Ultimately, it aims to reduce or even avoid leakage, minimizing the impact of equipment damage on production.

[0069] See Figure 2 As shown, in this embodiment of the invention, the method obtains the first acquisition parameter and the second acquisition parameter through the following steps:

[0070] S201. Collect the oil inlet temperature on the oil inlet pipeline and the oil return temperature on the oil outlet pipeline of the blast furnace blower stator blade oil cylinder respectively, and calculate the difference between the oil inlet and return oil temperatures to obtain the oil cylinder inlet and return oil temperature difference threshold.

[0071] For example, in this embodiment of the invention, patch temperature sensors can be installed on the inlet and outlet oil lines of the stationary vane cylinder to obtain the cylinder inlet temperature and cylinder return temperature. The cylinder inlet temperature is T1, the cylinder return temperature is T2, and the threshold value for the temperature difference between the cylinder inlet and return is ΔT. During energy transfer, if the stationary vane cylinder components wear out or leak internally, the volumetric loss of the stationary vane cylinder increases, and the lost energy is converted into heat, causing the oil temperature to rise. Therefore, the cylinder return temperature T2 is greater than the cylinder inlet temperature T1. Thus, the first threshold range is T2 - T1 ≥ 5℃, and when the first necessary condition is met, ΔT ≥ 5℃. In other embodiments, the first threshold range can also be adjusted according to the actual application scenario, and this embodiment of the invention does not limit this.

[0072] S202. Collect the feedback value of the blower stationary blade angle and the set value of the blower stationary blade angle. Calculate the difference based on the feedback value of the blower stationary blade angle and the set value of the blower stationary blade angle to obtain the stationary blade angle difference threshold.

[0073] For example, in this embodiment of the invention, the corresponding cylinder stroke can be read through the blower PLC control system, including the blower stator angle feedback value and the blower stator angle setpoint value. The blower stator angle feedback value is L1, the blower stator angle setpoint value is L2, and the stator angle difference threshold is ΔL. The second threshold range is L2-L1≥3°. Therefore, when the second necessary condition is met, ΔL≥3°. In other embodiments, the second threshold range can also be adjusted according to the actual application scenario, and this embodiment of the invention does not limit this.

[0074] For example, in this embodiment of the invention, the third threshold range is T2-T1≥6℃, then when the third necessary condition is met, ΔT≥6℃. In other embodiments, the third threshold range can also be adjusted according to the actual application scenario, and this embodiment of the invention does not limit this.

[0075] For example, in this embodiment of the invention, the fourth threshold range is L2-L1≥5°, then when the fourth necessary condition is met, ΔL≥5°. In other embodiments, the fourth threshold range can also be adjusted according to the actual application scenario, and this embodiment of the invention does not limit this.

[0076] In this embodiment of the invention, if the first detection condition is met, further detection is needed to confirm whether the leakage is severe. This embodiment of the invention determines whether the first acquisition parameter and the second acquisition parameter meet the second detection condition.

[0077] If, when the first detection condition is met, the corresponding first acquisition parameter satisfies 6℃>ΔT≥5℃ or the second acquisition parameter satisfies 5°>ΔL≥3°, then the second detection condition is considered not met. At this time, the first detection result is generated and fed back to the terminal device to generate the first warning information accordingly based on the first detection result. It can be considered that the stationary vane cylinder is in a state of slight leakage.

[0078] In this embodiment of the invention, if both ΔT≥6℃ and ΔL≥5° are satisfied, the second detection condition is considered to be met, a second detection result is generated, and feedback is sent to the terminal device to generate a second warning message based on the second detection result. It can be considered that the stationary vane cylinder is in a state of severe leakage.

[0079] Of course, it should be noted that in some implementations, the leakage of the stationary vane cylinder may be in a very slight state, or the first and second detection parameters may not be completely accurate due to human or non-human environmental factors. In this case, only one of the first necessary condition and the second necessary condition may be met when performing the first detection operation. In this case, some auxiliary means are needed to further confirm whether the stationary vane cylinder is in a leaking state.

[0080] Based on this, in this embodiment of the invention, before obtaining the first acquisition parameter and the second acquisition parameter, refer to... Figure 3 As shown, the method further includes:

[0081] S301. When the blast furnace blower stator cylinder is running normally, the detection parameters corresponding to the normal operation are obtained as standard detection parameters. The detection parameters include hydraulic oil pressure threshold, blower intake air volume threshold and synchronous motor stator current threshold.

[0082] S302. Real-time acquisition of detection parameters of the blast furnace blower stationary blade cylinder during operation and use as auxiliary detection parameters.

[0083] In embodiments of the present invention, when at least one of the first necessary condition and the second necessary condition is satisfied, see [reference needed]. Figure 4 As shown, the method further includes:

[0084] S401. Perform the third detection operation to compare the auxiliary detection parameters with the standard detection parameters, and determine whether the auxiliary detection parameters of the current blast furnace blower stationary blade cylinder meet the third detection condition. The third detection condition is that when the stationary blade angle of the blast furnace blower stationary blade cylinder is adjusted, the auxiliary detection parameters conform to linear change, and the amplitude range of the auxiliary detection parameter change is within the range of the change value corresponding to the standard detection parameter.

[0085] S402. When the auxiliary detection parameters meet the third detection condition, generate the auxiliary detection result and feed it back to the terminal device to generate auxiliary early warning information accordingly based on the auxiliary detection result.

[0086] When the stator cylinder is operating normally, the corresponding detection parameters are linearly proportional to the change in the stator angle. For example, when the stator angle is increased, the hydraulic oil pressure threshold, the blower intake air volume threshold, and the synchronous motor stator current threshold all increase accordingly; conversely, when the stator angle is decreased, these thresholds decrease accordingly. However, when the stator cylinder is in a leaking state, the changes in the hydraulic oil pressure threshold, blower intake air volume threshold, and synchronous motor stator current threshold during stator angle adjustment are no longer linear but fluctuate, potentially increasing or decreasing, and the magnitude of these changes exceeds the range of values ​​corresponding to the standard detection parameters.

[0087] The embodiments of the present invention further collect standard detection parameters and auxiliary detection parameters. By performing a third detection operation, the auxiliary detection parameters are compared with the standard detection parameters to determine whether the auxiliary detection parameters of the current blast furnace blower stationary blade cylinder meet the third detection conditions. Based on the auxiliary detection results obtained from the third detection conditions, the accuracy of the stationary blade cylinder is greatly improved.

[0088] In the embodiments of the present invention, see Figure 5 As shown, the method further includes:

[0089] S501. Record the first acquisition parameter, the second acquisition parameter, the standard detection parameter, and the auxiliary detection parameter respectively, and generate an acquisition parameter set;

[0090] S502. Record the detection results corresponding to the first acquisition parameter, the second acquisition parameter, the standard detection parameter, and the auxiliary detection parameter respectively, and generate a set of detection results;

[0091] S503. Collect the physical and environmental parameters of the current blast furnace blower stationary blade cylinder, generate a set of basic parameters, and use physical parameters (such as space size, model, material, etc.) and environmental parameters (such as temperature, humidity, etc.) to determine the applicable specifications and types of blast furnace blower stationary blade cylinders, as well as the production environment in which the current blast furnace blower stationary blade cylinder is located, to make it suitable for different application scenarios of blast furnace blower stationary blade cylinders.

[0092] S504. Based on AI neural network, establish a leakage detection model for the stationary vane cylinder by big data and machine learning according to the collected parameter set, detection result set and basic parameter set, analyze and confirm the coupling relationship between the collected parameter set, detection result set and basic parameter set, and adjust the first detection condition, second detection condition and / or third detection condition accordingly.

[0093] This invention utilizes an AI neural network to analyze and confirm the coupling relationship between the collected parameter sets, detection result sets, and basic parameter sets, based on real-world examples. This ensures the analysis aligns with the actual operating conditions of the blast furnace blower stator cylinders in real-world scenarios. Furthermore, the threshold ranges corresponding to the first, second, and / or third detection conditions can be adjusted based on the analysis results. This makes it suitable for leak detection of blast furnace blower stator cylinders of different specifications, types, and production environments, demonstrating good applicability.

[0094] Based on the above embodiments, this invention also provides an intelligent detection device for leakage in the blast furnace blower stator cylinder, see reference. Figure 6 The diagram shown is a schematic representation of the intelligent detection device for leakage in the blast furnace blower stator cylinder. The intelligent detection device for leakage in the blast furnace blower stator cylinder includes:

[0095] The data acquisition module 100 is used to collect field data of the blast furnace blower and obtain the first acquisition parameter and the second acquisition parameter of the current stationary blade cylinder of the blast furnace blower. The first acquisition parameter is the threshold value of the oil inlet and return oil temperature difference of the cylinder, and the second acquisition parameter is the threshold value of the stationary blade angle difference.

[0096] The first detection module 200 is used to perform a first detection operation to determine whether the first acquisition parameter and the second acquisition parameter meet the first detection condition. The first detection condition includes a first necessary condition and a second necessary condition that are met simultaneously. The first necessary condition is that the oil inlet and outlet oil temperature difference threshold of the oil cylinder meets the first threshold range. The second necessary condition is that the stationary vane angle difference threshold meets the second threshold range.

[0097] The second detection module 300 is used to perform a second detection operation when the first acquisition parameter and the second acquisition parameter meet the first detection condition, and to determine whether the first acquisition parameter and the second acquisition parameter meet the second detection condition. The second detection condition includes a third necessary condition and a fourth necessary condition that are met simultaneously. The third necessary condition is that the oil inlet and return oil temperature difference threshold of the oil cylinder meets the third threshold range. The fourth necessary condition is that the stationary vane angle difference threshold meets the fourth threshold range. The minimum value of the third threshold range is greater than the minimum value of the first threshold range, and the minimum value of the fourth threshold range is greater than the minimum value of the second threshold range.

[0098] The early warning module 400 is used to generate a first detection result and feed it back to the terminal device when the first acquisition parameter and the second acquisition parameter do not meet the second detection condition, so as to generate a first early warning information accordingly based on the first detection result; and to generate a second detection result and feed it back to the terminal device when the first acquisition parameter and the second acquisition parameter meet the second detection condition, so as to generate a second early warning information accordingly based on the second detection result.

[0099] In this embodiment of the invention, the data acquisition module 100 is specifically used for:

[0100] The oil inlet temperature on the oil inlet pipeline and the oil return temperature on the oil outlet pipeline of the blast furnace blower stator cylinder are collected respectively. The difference between the oil inlet and return temperatures is calculated to obtain the oil inlet and return temperature difference threshold. The blower stator angle feedback value and the blower stator angle set value are collected. The difference between the blower stator angle feedback value and the blower stator angle set value is calculated to obtain the stator angle difference threshold.

[0101] In this embodiment of the invention, before acquiring the first acquisition parameter and the second acquisition parameter, the data acquisition module 100 is further configured to: acquire the detection parameters corresponding to normal operation as standard detection parameters when the blast furnace blower stator cylinder is running normally, the detection parameters including hydraulic oil pressure threshold, blower intake air volume threshold and synchronous motor stator current threshold; and acquire the detection parameters of the blast furnace blower stator cylinder during operation in real time as auxiliary detection parameters.

[0102] In this embodiment of the invention, the device further includes a third detection module 500, which is used to perform a third detection operation when at least one of the first necessary condition and the second necessary condition is met, to compare the auxiliary detection parameters with the standard detection parameters, and to determine whether the auxiliary detection parameters of the current blast furnace blower stationary blade cylinder meet the third detection condition. The third detection condition is that when the stationary blade angle of the blast furnace blower stationary blade cylinder is adjusted, the auxiliary detection parameters conform to linear change, and the amplitude range of the auxiliary detection parameter change is within the range of the change value corresponding to the standard detection parameter. If the auxiliary detection parameters meet the third detection condition, the early warning module 400 generates an auxiliary detection result and feeds it back to the terminal device to generate auxiliary early warning information according to the auxiliary detection result.

[0103] In this embodiment of the invention, the device further includes a model training module 600, used for: recording a first acquisition parameter, a second acquisition parameter, a standard detection parameter, and an auxiliary detection parameter respectively to generate an acquisition parameter set; recording the detection results corresponding to the first acquisition parameter, the second acquisition parameter, the standard detection parameter, and the auxiliary detection parameter respectively to generate a detection result set; acquiring the physical parameters and environmental parameters of the current blast furnace blower stator cylinder to generate a basic parameter set; establishing a stator cylinder leakage detection model based on AI neural network, using big data and machine learning based on the acquisition parameter set, the detection result set, and the basic parameter set; analyzing and confirming the coupling relationship between the acquisition parameter set, the detection result set, and the basic parameter set; and adjusting the first detection condition, the second detection condition, and / or the third detection condition accordingly.

[0104] The intelligent detection device for leakage of the blast furnace blower stationary blade cylinder described in this embodiment can execute the intelligent detection method for leakage of the blast furnace blower stationary blade cylinder provided in the above embodiment. The intelligent detection device for leakage of the blast furnace blower stationary blade cylinder has the corresponding functional steps and beneficial effects of the intelligent detection method for leakage of the blast furnace blower stationary blade cylinder described in the above embodiment. For details, please refer to the embodiment of the intelligent detection method for leakage of the blast furnace blower stationary blade cylinder described above. The embodiments of this invention will not be repeated here.

[0105] This invention also provides an electronic device, which may include a processor and a memory, wherein the processor and memory can be connected via a bus or other means. The processor may be a Central Processing Unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof. The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the intelligent detection method for leakage in the blast furnace blower stator cylinder in this invention. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory, thereby realizing the intelligent detection method for leakage in the blast furnace blower stator cylinder in the above method embodiments.

[0106] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. The one or more modules are stored in the memory and, when executed by the processor, perform actions such as... Figure 1 The illustrated embodiment presents an intelligent detection method for leakage in the blast furnace blower stator cylinder. Specific details of the aforementioned electronic equipment can be found in the corresponding references. Figure 1The relevant descriptions and effects in the illustrated embodiments are for understanding purposes only and will not be repeated here. Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0107] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0108] Similarly, it should be understood that, in order to simplify this disclosure and aid in understanding one or more of the various aspects of the invention, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0109] It should be noted that the above embodiments are illustrative of the invention and not restrictive of the invention, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims.

Claims

1. A method for intelligent detection of leakage in the hydraulic cylinder of a blast furnace blower stator, characterized in that, The intelligent detection method for leakage in the blast furnace blower stator cylinder includes: Collect field data of the blast furnace blower, and obtain the first and second collection parameters of the current blast furnace blower stationary blade cylinder. The first collection parameter is the threshold of the oil inlet and return oil temperature difference of the cylinder, and the second collection parameter is the threshold of the stationary blade angle difference. The first detection operation is performed to determine whether the first acquisition parameter and the second acquisition parameter meet the first detection condition. The first detection condition includes a first necessary condition and a second necessary condition that are met simultaneously. The first necessary condition is that the oil inlet and return oil temperature difference threshold of the oil cylinder meets the first threshold range. The second necessary condition is that the stationary vane angle difference threshold meets the second threshold range. When the first acquisition parameter and the second acquisition parameter meet the first detection condition, the second detection operation is performed to determine whether the first acquisition parameter and the second acquisition parameter meet the second detection condition. The second detection condition includes a third necessary condition and a fourth necessary condition that are met simultaneously. The third necessary condition is that the oil inlet and return oil temperature difference threshold of the oil cylinder meets the third threshold range. The fourth necessary condition is that the stationary vane angle difference threshold meets the fourth threshold range. The minimum value of the third threshold range is greater than the minimum value of the first threshold range, and the minimum value of the fourth threshold range is greater than the minimum value of the second threshold range. When the first acquisition parameter and the second acquisition parameter do not meet the second detection condition, a first detection result is generated and fed back to the terminal device to generate a first warning message accordingly. When the first acquisition parameter and the second acquisition parameter meet the second detection condition, a second detection result is generated and fed back to the terminal device to generate a second warning message accordingly.

2. The intelligent detection method for leakage in the blast furnace blower stator cylinder according to claim 1, characterized in that, The method includes: The oil inlet temperature of the blast furnace blower's stationary blade oil cylinder and the oil return temperature of the oil cylinder on the oil outlet line are collected respectively. The difference between the oil inlet and return temperatures of the oil cylinder is calculated to obtain the oil cylinder inlet and return temperature difference threshold. Collect the feedback value of the blower's stationary blade angle and the set value of the blower's stationary blade angle, and calculate the difference based on the feedback value and the set value of the blower's stationary blade angle to obtain the stationary blade angle difference threshold.

3. The intelligent detection method for leakage in the blast furnace blower stator cylinder according to claim 1, characterized in that, Before acquiring the first acquisition parameter and the second acquisition parameter, the method further includes: When the blast furnace blower stator cylinder is running normally, the corresponding detection parameters during normal operation are obtained as standard detection parameters. The detection parameters include hydraulic oil pressure threshold, blower intake air volume threshold, and synchronous motor stator current threshold. It also acquires the detection parameters of the blast furnace blower stator cylinder in real time and uses them as auxiliary detection parameters.

4. The intelligent detection method for leakage in the blast furnace blower stator cylinder according to claim 3, characterized in that, The method further includes, when at least one of the first necessary condition and the second necessary condition is satisfied: The third detection operation compares the auxiliary detection parameters with the standard detection parameters to determine whether the auxiliary detection parameters of the current blast furnace blower stationary blade cylinder meet the third detection condition. The third detection condition is that when the stationary blade angle of the blast furnace blower stationary blade cylinder is adjusted, the auxiliary detection parameters conform to linear change, and the amplitude range of the auxiliary detection parameter change is within the range of the change value corresponding to the standard detection parameter. When the auxiliary detection parameters meet the third detection condition, an auxiliary detection result is generated and fed back to the terminal device to generate corresponding auxiliary warning information based on the auxiliary detection result.

5. The intelligent detection method for leakage in the blast furnace blower stator cylinder according to claim 4, characterized in that, The method further includes: Record the first acquisition parameter, the second acquisition parameter, the standard detection parameter, and the auxiliary detection parameter respectively to generate an acquisition parameter set; Record the detection results corresponding to the first acquisition parameter, the second acquisition parameter, the standard detection parameter, and the auxiliary detection parameter respectively, and generate a set of detection results; Collect the physical and environmental parameters of the current blast furnace blower stator cylinder to generate a set of basic parameters; Based on AI neural networks, a leakage detection model for the stator cylinder is established using big data and machine learning based on the collected parameter set, the detection result set, and the basic parameter set. The coupling relationship between the collected parameter set, the detection result set, and the basic parameter set is analyzed and confirmed, and the first detection condition, the second detection condition, and / or the third detection condition are adjusted accordingly.

6. A smart detection device for leakage in the oil cylinder of the stationary blade of a blast furnace blower, characterized in that, The intelligent detection device for leakage in the blast furnace blower stator cylinder includes: The data acquisition module is used to collect field data of the blast furnace blower and obtain the first and second acquisition parameters of the current stationary blade cylinder of the blast furnace blower. The first acquisition parameter is the threshold value of the oil inlet and outlet oil temperature difference of the cylinder, and the second acquisition parameter is the threshold value of the stationary blade angle difference. The first detection module is used to perform a first detection operation to determine whether the first acquisition parameter and the second acquisition parameter meet the first detection condition. The first detection condition includes a first necessary condition and a second necessary condition that are met simultaneously. The first necessary condition is that the oil inlet and outlet oil temperature difference threshold of the oil cylinder meets the first threshold range. The second necessary condition is that the stationary vane angle difference threshold meets the second threshold range. The second detection module is used to perform a second detection operation when the first acquisition parameter and the second acquisition parameter meet the first detection condition, and to determine whether the first acquisition parameter and the second acquisition parameter meet the second detection condition. The second detection condition includes a third necessary condition and a fourth necessary condition that are met simultaneously. The third necessary condition is that the oil inlet and return oil temperature difference threshold of the oil cylinder meets the third threshold range. The fourth necessary condition is that the stationary vane angle difference threshold meets the fourth threshold range. The minimum value of the third threshold range is greater than the minimum value of the first threshold range, and the minimum value of the fourth threshold range is greater than the minimum value of the second threshold range. The early warning module is used to generate a first detection result and feed it back to the terminal device when the first acquisition parameter and the second acquisition parameter do not meet the second detection condition, so as to generate a first early warning information accordingly based on the first detection result; and to generate a second detection result and feed it back to the terminal device when the first acquisition parameter and the second acquisition parameter meet the second detection condition, so as to generate a second early warning information accordingly based on the second detection result.

7. The intelligent detection device for leakage of the blast furnace blower stator cylinder according to claim 6, characterized in that, The data acquisition module is specifically used for: The oil inlet temperature of the blast furnace blower's stationary blade oil cylinder and the oil return temperature of the oil cylinder on the oil outlet line are collected respectively. The difference between the oil inlet and return temperatures of the oil cylinder is calculated to obtain the oil cylinder inlet and return temperature difference threshold. Collect the feedback value of the blower's stationary blade angle and the set value of the blower's stationary blade angle, and calculate the difference based on the feedback value and the set value of the blower's stationary blade angle to obtain the stationary blade angle difference threshold.

8. The intelligent detection device for leakage of the blast furnace blower stator cylinder according to claim 6, characterized in that, Before acquiring the first acquisition parameter and the second acquisition parameter, the data acquisition module is also used for: When the blast furnace blower stator cylinder is running normally, the corresponding detection parameters during normal operation are obtained as standard detection parameters. The detection parameters include hydraulic oil pressure threshold, blower intake air volume threshold, and synchronous motor stator current threshold. It also acquires the detection parameters of the blast furnace blower stator cylinder in real time and uses them as auxiliary detection parameters.

9. The intelligent detection device for leakage of the blast furnace blower stationary blade cylinder according to claim 8, characterized in that, The device further includes: The third detection module is used to perform a third detection operation when at least one of the first necessary condition and the second necessary condition is met, comparing the auxiliary detection parameters with the standard detection parameters, and determining whether the auxiliary detection parameters of the current blast furnace blower stationary blade cylinder meet the third detection condition. The third detection condition is that when the stationary blade angle of the blast furnace blower stationary blade cylinder is adjusted, the auxiliary detection parameters conform to linear change, and the amplitude range of the change of the auxiliary detection parameters is within the range of the change value corresponding to the standard detection parameters. If the auxiliary detection parameters meet the third detection condition, the auxiliary detection result is generated through the early warning module and fed back to the terminal device to generate auxiliary early warning information accordingly.

10. The intelligent detection device for leakage of the blast furnace blower stator cylinder according to claim 9, characterized in that, The device further includes a model training module for: Record the first acquisition parameter, the second acquisition parameter, the standard detection parameter, and the auxiliary detection parameter respectively to generate an acquisition parameter set; Record the detection results corresponding to the first acquisition parameter, the second acquisition parameter, the standard detection parameter, and the auxiliary detection parameter respectively, and generate a set of detection results; Collect the physical and environmental parameters of the current blast furnace blower stator cylinder to generate a set of basic parameters; Based on AI neural networks, a leakage detection model for the stator cylinder is established using big data and machine learning based on the collected parameter set, the detection result set, and the basic parameter set. The coupling relationship between the collected parameter set, the detection result set, and the basic parameter set is analyzed and confirmed, and the first detection condition, the second detection condition, and / or the third detection condition are adjusted accordingly.

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