Dedusting network diagnosis intelligent analysis system

Abstract

The invention relates to an intelligent analysis system for dust removal network diagnosis, which realizes diagnosis and identification of pipe network blockage points. The system comprises an intelligent monitoring subsystem for collecting dust remover data in real time; the intelligent analysis subsystem comprises statistical analysis, fusion analysis and fault judgment and decision; the statistical analysis comprises basic statistic analysis, probability distribution study and judgment and reasoning mechanism decision; fusion analysis: performing deep fusion analysis on wind pressure and wind speed data in the collected dust remover data and inlet and outlet temperature and equipment operation state data; and the fault judgment and decision-making module judges the operation state and the fault reason of the system according to the statistical analysis and fusion analysis results, and outputs a decision and judgment.

Description

Technical Field

[0001] This application relates to the field of dust removal, specifically to an intelligent analysis system for dust removal network diagnosis. Background Technology

[0002] In the grain industry, large amounts of dust are often generated during operations. To prevent dust explosions, negative pressure dust collectors are typically used to collect the dust and reduce its concentration. To ensure the stability of dust collection equipment and avoid the risk of dust explosions due to equipment damage, various sensors are often installed to monitor the equipment's operating parameters. However, these methods only monitor the dust collection equipment itself and are insufficient for controlling the entire dust collection network (dust collection equipment + dust collection air ducts). Furthermore, they lack follow-up analysis and control, making the monitoring level and methods relatively rudimentary. Summary of the Invention

[0003] The present invention aims to overcome at least one of the defects of the prior art and provide a dust removal network diagnosis and analysis system, which fills the industry gap in the field of intelligent management and control of dust removal systems, diagnoses and identifies pipeline blockages, and improves management efficiency.

[0004] A dust removal network diagnostic intelligent analysis system is provided, the system comprising an intelligent monitoring subsystem and an intelligent analysis subsystem;

[0005] The intelligent monitoring subsystem collects dust collector data in real time, including air pressure, air velocity, inlet and outlet temperatures, and equipment operating status.

[0006] The intelligent analysis subsystem includes statistical analysis, fusion analysis, fault diagnosis and decision-making;

[0007] The statistical analysis includes basic statistical analysis, probability distribution assessment, and inference mechanism decision-making.

[0008] The basic statistical analysis involves calculating the collected wind pressure and wind speed data to obtain basic statistics, including the mean, standard deviation, maximum value, and minimum value, thereby obtaining the central tendency and dispersion of the data.

[0009] The probability distribution analysis involves determining the probability distribution of wind pressure and wind speed data, including normal distribution and Poisson distribution, and assessing the degree of data anomaly by fitting the probability distribution.

[0010] The reasoning mechanism makes decisions based on collected wind pressure and wind speed data, using forward and backward reasoning mechanisms to match and reason about rules.

[0011] The fusion analysis involves deeply integrating and analyzing the wind pressure and wind speed data from the collected dust collector data with the inlet and outlet temperatures and equipment operating status data.

[0012] The fault diagnosis and decision-making process determines the system's operating status and fault causes based on the results of statistical analysis and fusion analysis, and outputs decisions and judgments.

[0013] The main advantages of this invention are as follows:

[0014] 1. Innovative and filling a gap, it is the first in the industry to test dust collection pipelines;

[0015] 2. This is the first time in the industry that a complete method for diagnosing and identifying blockages in a dust removal ventilation system has been proposed. Attached Figure Description

[0016] Figure 1 This is a structural diagram of a dust removal network diagnosis and intelligent analysis system according to the present invention.

[0017] Figure 2 This is a trend chart of air pressure and inlet / outlet pressure difference during filter bag blockage faults in this invention.

[0018] Figure 3 This is a trend chart of air pressure and inlet / outlet pressure difference during filter bag damage according to the present invention.

[0019] Figure 4 For the purpose of displaying monitoring information of this invention Figure 1 .

[0020] Figure 5 For the purpose of displaying monitoring information of this invention Figure 2 . Detailed Implementation

[0021] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the invention. To better illustrate the following embodiments, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions; it is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0022] This application provides an intelligent analysis system for diagnosing dust removal networks, such as... Figure 1 The system includes an intelligent monitoring subsystem and an intelligent analysis subsystem;

[0023] The intelligent monitoring subsystem involves installing various sensors such as air pressure, differential pressure, speed, and temperature at appropriate locations on the dust collector body to monitor the operating status of the dust collector in real time and provide early warnings.

[0024] The intelligent analysis subsystem involves installing wind speed and pressure detection devices on the main wind network and its branches to accurately identify blockages.

[0025] The intelligent monitoring subsystem collects dust collector data in real time, including air pressure, air velocity, inlet and outlet temperatures, and equipment operating status.

[0026] The intelligent analysis subsystem includes statistical analysis, fusion analysis, fault diagnosis and decision-making;

[0027] The statistical analysis includes basic statistical analysis, probability distribution assessment, and inference mechanism decision-making.

[0028] The basic statistical analysis involves calculating the collected wind pressure and wind speed data to obtain basic statistics, including the mean, standard deviation, maximum value, and minimum value, thereby obtaining the central tendency and dispersion of the data.

[0029] Significant fluctuations in the standard deviation often indicate potential system instability. In some embodiments, the average value of wind pressure data over a period of time is calculated. If the average value is significantly higher than the normal range, it may indicate an abnormal pressure in the system.

[0030] The probability distribution analysis involves determining the probability distribution of wind pressure and wind speed data, including normal distribution and Poisson distribution, and assessing the degree of data anomaly by fitting the probability distribution.

[0031] If a data point falls in the tail region of the probability distribution, it can be considered an outlier. In some embodiments, statistical software is used to fit wind pressure data, and if a data point is found to fall in the tail region of the probability distribution, it is considered a possible outlier.

[0032] The reasoning mechanism makes decisions based on collected wind pressure and wind speed data, using forward and backward reasoning mechanisms to match and reason about rules.

[0033] Matching and reasoning based on rules is akin to navigating a predetermined logical route. This approach allows us to determine the system's operational status and potential causes of failure. The system acts like a wise advisor, integrating real-time data and prior knowledge, enabling rapid decision-making and providing timely and effective guidance for its operation. In some embodiments, forward reasoning infers possible causes of system failure based on known failure rules and current data; backward reasoning, on the other hand, starts from possible failure outcomes and searches for data features that lead to those outcomes.

[0034] The fusion analysis involves deeply integrating and analyzing the wind pressure and wind speed data from the collected dust collector data with the inlet and outlet temperatures and equipment operating status data.

[0035] The fault diagnosis and decision-making process determines the system's operating status and fault causes based on the results of statistical analysis and fusion analysis, and outputs decisions and judgments.

[0036] By comprehensively considering multiple factors, the operating status of the system can be judged more comprehensively and accurately. In some embodiments, by combining dust concentration data, if the dust emission concentration also increases while the wind speed decreases and the wind pressure increases, it is as if multiple clues are pointing in the same direction, and it is more likely that the failure is caused by filter bag damage, which provides stronger support for accurate fault diagnosis.

[0037] This approach organically integrates different analytical models, such as statistical models and machine learning models. In some embodiments, a statistical model is first used for preliminary anomaly detection, much like using a coarse sieve to identify potentially problematic areas; then, a machine learning model is used for precise fault type identification; finally, the system verifies and makes decisions. This multi-model fusion approach improves the accuracy and reliability of fault diagnosis, providing a more robust guarantee for the stable operation of the dust collector system.

[0038] In some embodiments, if statistical analysis shows that the standard deviation of wind pressure data fluctuates greatly, and fusion analysis finds that the wind speed decreases and the dust concentration increases, it is judged that the filter bag may be clogged. The system can issue an alarm and provide corresponding handling suggestions.

[0039] Specifically, in some embodiments, the device for collecting dust collector data includes:

[0040] A dust concentration detector is used to read dust concentration values.

[0041] Differential pressure transmitters and pressure transmitters are used to measure the pressure and differential pressure of gases.

[0042] Wind speed and pressure transmitters are used to measure gas wind speed and pressure.

[0043] Current transformers are used to identify the start-up and shutdown status of dust collectors;

[0044] A pulse power signal detector is used in conjunction with a current transformer to determine the dust removal operation status.

[0045] The airlock speed detector is used to determine the operating status of the airlock motor;

[0046] The scraper speed detector is used to determine the operating status of the scraper motor;

[0047] Differential pressure gauge is used to determine the condition of dust collector bags;

[0048] Temperature sensors are used to determine the internal temperature of the dust collector to prevent smoldering.

[0049] Specifically, in some embodiments, the system further includes a cloud platform data processing subsystem for querying the status of the dust collector, recording values, displaying information, and querying historical data. The information display includes visualizing the status and values ​​of wind pressure and wind speed, providing early warnings for monitoring data, and providing a digital twin display of the dust collection equipment.

[0050] Specifically, in some embodiments, the system includes model matching, which includes model construction and comparison of real-time data;

[0051] The model construction involves theoretical analysis of historical data, constructing a normal operation model of dust collector pipeline air pressure and air velocity, deriving a mathematical model based on fluid mechanics principles, and using machine learning technology to construct a data-driven model.

[0052] The real-time data comparison involves comparing the real-time collected wind pressure and wind speed data with the normal operation model, setting a threshold in advance, and issuing an alarm when the threshold is exceeded.

[0053] Specifically, the model matching principle is as follows:

[0054] Normal Operation Model Construction: Based on abundant historical data and rigorous theoretical analysis, a normal operation model for the dust collector duct pressure and velocity is constructed. This mathematical model is derived from fluid mechanics principles and is also a data-driven model built using advanced machine learning technology. Like an intelligent butler with a smart brain, it can adaptively learn and adapt to the system's operating mode. The normal operation model describes the reasonable range and trends of air pressure and velocity under different operating conditions, providing clear guidance for the normal operation trajectory of the software system.

[0055] Real-time data and model comparison: A detailed comparison is made between the real-time collected wind pressure and speed data and the normal operation model, much like comparing actual actions with a pre-set plan. If the deviation between the real-time data and the model predictions is small, it indicates that the system is operating smoothly and according to normal rhythm; however, if the deviation exceeds a pre-set threshold, it's like an alarm sounding, indicating a possible abnormality in the system. In some embodiments, when the real-time wind speed is significantly lower than the model prediction, while the wind pressure is higher, it's like a distress signal from the system, potentially indicating a blockage in the pipeline, like a boulder blocking the road, affecting the normal flow of gas.

[0056] The principles of fault propagation and causal reasoning are as follows:

[0057] Insight into Fault Propagation Mechanisms: In the complex ecosystem of a dust collector system, a failure in any component or link can affect air pressure and velocity through gas flow and pressure transmission within the pipeline. In some embodiments, a fan failure is like a power source malfunction, potentially causing a drop in air velocity, similar to a sailboat losing power, which in turn alters the pressure distribution within the pipeline. A deep understanding of fault propagation mechanisms helps infer potential fault sources from subtle changes in air pressure and velocity.

[0058] Causal Reasoning Analysis: Utilizing causal relationship networks or the extensive knowledge of domain experts, in-depth causal reasoning is conducted to analyze abnormal changes in wind pressure and wind speed. By comprehensively analyzing various possible causal relationships, the most probable cause of the failure is determined. In some embodiments, when a sudden increase in wind pressure and a decrease in wind speed are detected, combined with the system's structure and operating principles, it can be inferred that the cause may be due to factors such as filter bag blockage, pipeline valve malfunction, or fan failure.

[0059] Data correlation mining and feature extraction: Wind pressure and velocity data, along with dust collector inlet and outlet temperatures, dust concentration, and fan speed, interact and influence each other, forming a complex and subtle operational network. Through in-depth mining and detailed analysis of massive amounts of historical data, the correlation characteristics and potential patterns among these data can be extracted. In some embodiments, as dust concentration gradually increases, much like the increase in obstacles on a road, the resistance of the pipeline increases accordingly, causing wind pressure to rise like a compressed spring, while wind speed decreases like obstructed water flow.

[0060] Specifically, in some embodiments, the system includes filter bag clogging fault inference, the inference method including:

[0061] Wind pressure and wind speed analysis: Set first standard wind pressure and first standard wind speed values. If the average value of the calculated wind pressure data is greater than the first standard wind pressure value, the system pressure is confirmed to be unstable. If the average value of the calculated wind speed data is greater than the first standard wind speed value, the system wind speed is diagnosed to be unstable.

[0062] Inlet and outlet temperature analysis: Set first standard temperature values ​​for inlet and outlet. When the measured inlet temperature is greater than the first standard temperature value, an abnormal heat transfer is diagnosed.

[0063] Fan current analysis: Set a first standard current value. When the measured fan current value is greater than the first standard current value, diagnose abnormal fan current.

[0064] Probability distribution analysis shows that wind pressure data deviates from the fitted normal distribution, with some data points falling in the high-value region on the right side of the distribution, and wind speed data having some data points falling in the low-value region on the left side.

[0065] Analysis of the reasoning mechanism: When using forward reasoning, it is known that filter bag blockage will lead to increased pipeline resistance, which in turn will cause the air pressure to rise and the air velocity to decrease. When the collected air pressure rises and the air velocity decreases, it is inferred that the filter bag is blocked.

[0066] Based on the combined analysis and inference of factors such as increased wind pressure, decreased wind speed, changes in inlet and outlet temperatures, and increased fan current, a filter bag blockage fault was diagnosed.

[0067] In some embodiments, the filter bag blockage fault inference method is as follows: first, statistical analysis is performed for inference:

[0068] Regarding basic statistics, calculating the average wind pressure data reveals a significant increase and large fluctuations in the standard deviation, indicating unstable system pressure. Figure 2 The pressure differential increases significantly. The average wind speed data decreases, and the standard deviation may also fluctuate greatly, reflecting wind speed instability. In some embodiments, the average wind pressure is 1000 Pa and the standard deviation is 50 Pa during normal operation. When the filter bag is clogged, the average wind pressure rises to 1500 Pa and the standard deviation becomes 100 Pa; the normal average wind speed is 8 m / s and the standard deviation is 1 m / s. When clogged, the average wind speed drops to 3 m / s and the standard deviation becomes 2 m / s.

[0069] Analysis of inlet and outlet temperatures reveals that due to impaired gas flow, localized temperature increases may occur near the blockage site. In some embodiments, the normal inlet temperature is 30°C, but after blockage, the temperature near the inlet may rise to 50°C.

[0070] In terms of probability distribution, wind pressure data may deviate from the originally fitted normal distribution, with more data points falling on the right side of the distribution (high value region); while wind speed data falls more on the left side (low value region).

[0071] In terms of reasoning mechanism, through forward reasoning, it is known that filter bag blockage will lead to increased pipeline resistance, which in turn will cause the wind pressure to rise and the wind speed to decrease. The current data of increased wind pressure and decreased wind speed can be inferred to be that the filter bag is blocked.

[0072] Through fusion analysis, it was inferred that increased wind pressure and decreased wind speed are typical wind pressure and speed characteristics of pipeline blockage; changes in inlet and outlet temperatures reflect abnormal heat transfer caused by obstructed gas flow; increased fan current and abnormalities in the dust removal system further confirm the presence of increased resistance within the pipeline. Based on these data, the problem can be identified as pipeline blockage.

[0073] Specifically, in some embodiments, the system includes filter bag damage fault inference, and the inference method includes:

[0074] Wind pressure and wind speed analysis: Set second standard wind pressure and second standard wind speed values. If the measured average wind pressure data is less than the second standard wind pressure value, the system pressure is confirmed to be unstable; if the measured average wind speed data is greater than the second standard wind speed value, the system wind speed is diagnosed to be unstable.

[0075] Inlet and outlet temperature analysis: Set second standard temperature values ​​for inlet and outlet. When the average inlet temperature is measured to be greater than the second standard temperature value, an abnormal heat transfer is diagnosed.

[0076] Equipment operation status analysis shows that the dust removal equipment of the intelligent monitoring subsystem is operating at a normal frequency, but the dust emission concentration is increasing.

[0077] Based on the combined analysis and inference, the increased wind speed, decreased wind pressure, increased outlet temperature, and increased dust emission concentration led to the diagnosis of a filter bag rupture fault.

[0078] In some embodiments, the method for inferring filter bag damage is as follows:

[0079] Regarding data feature representation:

[0080] Wind pressure and velocity data show that after the filter bag is damaged, the resistance of gas passing through the filter bag decreases, which usually results in increased wind speed and decreased wind pressure. Figure 4 The pressure difference caused by filter bag damage is 0. In some embodiments, the wind speed is stable at 12 m / s and the wind pressure is 1200 Pa during normal operation; after the filter bag is damaged, the wind speed may rise to 15 m / s and the wind pressure may drop to 900 Pa.

[0081] Analysis of inlet and outlet temperatures reveals that due to filter bag damage, some gas that has not undergone sufficient filtration and heat exchange passes directly through, potentially causing abnormal fluctuations in the outlet temperature. In some embodiments, the normal outlet temperature is around 30°C, but after damage, it may rise to 50°C within a short period.

[0082] The equipment operation status display shows that the operating frequency of the dust removal system may not have changed significantly, but the dust emission monitoring device shows that the emission concentration has increased significantly, and the original emission standard of 10mg / m³ may have risen to 50mg / m³.

[0083] A comprehensive analysis, combining the above data, revealed that increased wind speed and decreased wind pressure indicate a change in the filter bag's filtration resistance; increased outlet temperature suggests that the gas heat exchange process is affected; and increased dust emission concentration directly reflects a deterioration in the filter bag's filtration efficiency. These data corroborate each other, pointing to filter bag damage as the cause of the malfunction.

[0084] Specifically, in some embodiments, the system includes pipeline valve fault inference, and the inference method includes:

[0085] Wind pressure and wind speed analysis: Set a third standard wind pressure value and a third standard wind speed value. If the measured average wind pressure data is less than the third standard wind pressure value, the system pressure is confirmed to be unstable; if the measured average wind speed data is greater than the third standard wind speed value, the system wind speed is diagnosed to be unstable.

[0086] Probability distribution analysis shows that the wind pressure data deviates from the fitted normal distribution, with the wind pressure data shifting to the left and the wind speed data shifting to the right.

[0087] The reasoning mechanism analysis adopts forward reasoning. It is known that valve failure will change the flow area of ​​the pipeline, affecting wind pressure and wind speed. The current situation of decreased wind pressure and increased wind speed is consistent with the failure characteristics of pipeline valve not being fully opened.

[0088] By integrating and analyzing data on the overall equipment operating status, when pipeline valves show open or closed signals but the feedback from the pipeline valves does not match the actual status, and by combining data on decreased wind pressure and increased wind speed, a pipeline valve malfunction can be diagnosed.

[0089] In some embodiments, such as Figure 4 and Figure 5 By comparing and analyzing the wind pressure and wind speed values, it was found that the opening of some butterfly valves in the pipeline was not properly adjusted, that is, they could not be fully opened even when "open", which effectively revealed the blockage problem in the pipeline caused by the valve opening.

[0090] The analysis process is as follows:

[0091] In the selected process operation, DT3-3 carried out dust removal operations on BC107 head unit, SC03, W03, and BC110. During the operation, it was found that the wind speed of BC110 was only 3.82 m / s, which was much lower than the 8.76 m / s of the previous stage W03.

[0092] For comparison, DT3-4, which has exactly the same partition performance, structure, operating conditions, and ventilation network duct layout as DT3-3, was selected:

[0093] Comparing the DT3-4 process, which has identical hardware configuration, pipeline structure, and operation mode, it can be seen that the wind speed at the dust removal point W04, which is at the same level as W03, is basically the same, at about 8.5 m / s. Therefore, it can be determined that the upstream pipeline resistance and the working status of the dust removal equipment are the same.

[0094] Next, BC110 and BC111 were analyzed and compared. Firstly, considering the pipe location and installation structure, the two dust collection points have identical physical characteristics, meaning they share the same non-adjustable characteristics such as pipe diameter, bends, and valve layout. However, the wind speed at BC110 is only 3.82 m / s, while the wind speed at BC111 reaches 7.41 m / s, a significant difference in magnitude. Therefore, it can be determined that there is a significant blockage in the ductwork section of BC111, indicating dust accumulation or improper valve adjustment.

[0095] Therefore, the inference and analysis of this section of the ventilation network pipeline may have the following issues:

[0096] Dust accumulates in the horizontal section of the duct where the wind pressure and wind speed switch is installed;

[0097] The electric butterfly valve below the wind pressure and wind speed switch is not open enough, resulting in increased resistance.

[0098] It was deduced that the fault was caused by insufficient opening of the electric butterfly valve in section BC110, which led to increased resistance in the pipeline, a significant decrease in wind speed, and affected the dust removal effect.

[0099] This embodiment demonstrates that the use of wind pressure and wind speed switches is completely effective in monitoring, detecting, and locating blockages.

[0100] In some embodiments, the method for inferring pipeline valve failures is as follows:

[0101] First, statistical analysis and inference are performed, including:

[0102] In terms of basic statistics, if the valve is not fully open, the average wind pressure may decrease because pipe resistance is reduced; the average wind speed may increase. In some embodiments, the normal average wind pressure is 80 Pa, which drops to 60 Pa when the valve fails; the normal average wind speed is 8 m / s, which rises to 11 m / s when the valve fails. The standard deviation will also change accordingly, reflecting the instability of the data.

[0103] In terms of probability distribution, wind pressure data may be shifted to the left, while wind speed data may be shifted to the right.

[0104] In terms of reasoning mechanism, forward reasoning: it is known that valve failure will change the flow area of ​​the pipeline, affecting wind pressure and wind speed. The current situation of reduced wind pressure and increased wind speed is consistent with the failure characteristics of valve not being fully open, so it can be inferred that it may be a valve failure.

[0105] By performing fusion analysis and inference, and combining the equipment operating status data, if the valve has feedback signals for opening and closing, and it is found that the valve feedback does not match the actual state it should be in, such as it should be fully open but the feedback is half open, and combined with the data of reduced wind pressure and increased wind speed, it can be determined that the pipeline valve is faulty.

[0106] Specifically, in some embodiments, the system includes wind turbine fault inference, and the inference method includes:

[0107] Wind pressure and wind speed analysis: Set fourth standard wind pressure and fourth standard wind speed values. If the measured average wind pressure data is less than the fourth standard wind pressure value, the system pressure is confirmed to be unstable; if the measured average wind speed data is less than the fourth standard wind speed value, the system wind speed is diagnosed to be unstable.

[0108] Inlet and outlet temperature analysis: Set a first inlet and outlet temperature difference value. When the measured inlet and outlet temperature difference is less than the first inlet and outlet temperature difference value, it is inferred that the fan is in an abnormal state.

[0109] Fan current analysis: Set a second standard current value. When the measured fan current value is greater than the second standard current value, diagnose abnormal fan current.

[0110] Probability distribution analysis shows that the wind pressure data deviates from the fitted normal distribution, with some wind pressure data falling into the low-value region on the left, and the wind speed data deviating from the normal distribution.

[0111] The reasoning mechanism analysis uses forward reasoning. It is known that a fan failure will lead to insufficient power, resulting in reduced wind speed and wind pressure. Combined with data analysis, this is consistent with the characteristics of a fan failure.

[0112] Based on the combined analysis and inference of factors such as decreased wind speed, decreased wind pressure, reduced inlet and outlet temperature difference, and increased current, a fan malfunction was diagnosed.

[0113] In some embodiments, the wind turbine fault inference method includes:

[0114] First, statistical analysis and inference are performed:

[0115] Regarding basic statistics, the average wind speed will be significantly lower due to insufficient power from the fans. Wind pressure data may also decrease, with large fluctuations in standard deviation. In some embodiments, the normal average wind speed is 15 m / s, which drops to 5 m / s when the fans fail; the normal average wind pressure is 1000 Pa, which drops to 600 Pa when the fans fail.

[0116] Regarding inlet and outlet temperatures, due to the slowed gas flow and reduced heat exchange efficiency, the temperature difference between the inlet and outlet will decrease. Originally, the inlet and outlet temperature difference was 20℃, but during a malfunction, it may shrink to 12℃.

[0117] In terms of probability distribution, wind speed data largely falls in the low-value area on the left, and wind pressure data also deviates from the normal distribution.

[0118] In terms of reasoning mechanism, forward reasoning: it is known that a wind turbine failure will lead to insufficient power, resulting in reduced wind speed and wind pressure. Based on the data characteristics collected so far, it can be inferred that the failure may be due to a wind turbine failure.

[0119] By performing fusion analysis, the decrease in wind speed and wind pressure directly reflects insufficient fan power; the reduction in the inlet and outlet temperature difference reflects the impact of gas flow on heat exchange; through the fusion analysis of these data, fan malfunctions can be accurately determined. Simultaneously, abnormalities may also appear in the fan's operating status data, such as increased current and temperature; combining this information can further confirm the fault.

[0120] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solution of the present invention, and are not intended to limit the specific implementation of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A dust removal network diagnostic intelligent analysis system, characterized in that: The system includes an intelligent monitoring subsystem and an intelligent analysis subsystem; The intelligent monitoring subsystem collects dust collector data in real time, including air pressure, air velocity, inlet and outlet temperatures, and equipment operating status. The intelligent analysis subsystem includes statistical analysis, fusion analysis, fault diagnosis and decision-making; The statistical analysis includes basic statistical analysis, probability distribution assessment, and inference mechanism decision-making. The basic statistical analysis involves calculating the collected wind pressure and wind speed data to obtain basic statistics, including the mean, standard deviation, maximum value, and minimum value, thereby obtaining the central tendency and dispersion of the data. The probability distribution analysis involves determining the probability distribution of wind pressure and wind speed data, including normal distribution and Poisson distribution, and assessing the degree of data anomaly by fitting the probability distribution. The reasoning mechanism makes decisions based on collected wind pressure and wind speed data, using forward and backward reasoning mechanisms to match and reason about rules. The fusion analysis involves deeply integrating and analyzing the wind pressure and wind speed data from the collected dust collector data with the inlet and outlet temperatures and equipment operating status data. The fault diagnosis and decision-making process determines the system's operating status and fault causes based on the results of statistical analysis and fusion analysis, and outputs decisions and judgments.

2. The intelligent analysis system for dust removal network diagnosis according to claim 1, characterized in that, The equipment for collecting dust collector data includes: A dust concentration detector is used to read dust concentration values. Differential pressure transmitters and pressure transmitters are used to measure the pressure and differential pressure of gases. Wind speed and pressure transmitters are used to measure gas wind speed and pressure. Current transformers are used to identify the start-up and shutdown status of dust collectors; A pulse power signal detector is used in conjunction with a current transformer to determine the dust removal operation status. The airlock speed detector is used to determine the operating status of the airlock motor; The scraper speed detector is used to determine the operating status of the scraper motor; Differential pressure gauge is used to determine the condition of dust collector bags; Temperature sensors are used to determine the internal temperature of the dust collector to prevent smoldering.

3. The intelligent analysis system for dust removal network diagnosis according to claim 1, characterized in that, The system also includes a cloud platform data processing subsystem, which is used for status query, value recording, information display, and historical data query of the dust collector. The information display includes visualizing the status and values ​​of wind pressure and wind speed, providing early warnings for monitoring data, and providing a digital twin display of the dust collection equipment.

4. The intelligent analysis system for dust removal network diagnosis according to claim 1, characterized in that, The system includes model matching, which includes model construction and real-time data comparison. The model construction involves theoretical analysis of historical data, constructing a normal operation model of dust collector pipeline air pressure and air velocity, deriving a mathematical model based on fluid mechanics principles, and using machine learning technology to construct a data-driven model. The real-time data comparison involves comparing the real-time collected wind pressure and wind speed data with the normal operation model, setting a threshold in advance, and issuing an alarm when the threshold is exceeded.

5. The intelligent analysis system for dust removal network diagnosis according to claim 1, characterized in that, The system includes a filter bag clogging fault inference method, which includes: Wind pressure and wind speed analysis: Set first standard wind pressure and first standard wind speed values. If the average value of the calculated wind pressure data is greater than the first standard wind pressure value, the system pressure is confirmed to be unstable. If the average value of the calculated wind speed data is greater than the first standard wind speed value, the system wind speed is diagnosed to be unstable. Inlet and outlet temperature analysis: Set first standard temperature values ​​for inlet and outlet. When the measured inlet temperature is greater than the first standard temperature value, an abnormal heat transfer is diagnosed. Fan current analysis: Set a first standard current value. When the measured fan current value is greater than the first standard current value, diagnose abnormal fan current. Probability distribution analysis shows that wind pressure data deviates from the fitted normal distribution, with some data points falling in the high-value region on the right side of the distribution, and wind speed data having some data points falling in the low-value region on the left side. Analysis of the reasoning mechanism: When using forward reasoning, it is known that filter bag blockage will lead to increased pipeline resistance, which in turn will cause the air pressure to rise and the air velocity to decrease. When the collected air pressure rises and the air velocity decreases, it is inferred that the filter bag is blocked. Based on the combined analysis and inference of factors such as increased wind pressure, decreased wind speed, changes in inlet and outlet temperatures, and increased fan current, a filter bag blockage fault was diagnosed.

6. The intelligent analysis system for dust removal network diagnosis according to claim 1, characterized in that, The system includes a filter bag damage fault inference method, which includes: Wind pressure and wind speed analysis: Set second standard wind pressure and second standard wind speed values. If the measured average wind pressure data is less than the second standard wind pressure value, the system pressure is confirmed to be unstable; if the measured average wind speed data is greater than the second standard wind speed value, the system wind speed is diagnosed to be unstable. Inlet and outlet temperature analysis: Set second standard temperature values ​​for inlet and outlet. When the average inlet temperature is measured to be greater than the second standard temperature value, an abnormal heat transfer is diagnosed. Equipment operation status analysis shows that the dust removal equipment of the intelligent monitoring subsystem is operating at a normal frequency, but the dust emission concentration is increasing. Based on the combined analysis and inference, the increased wind speed, decreased wind pressure, increased outlet temperature, and increased dust emission concentration led to the diagnosis of a filter bag rupture fault.

7. The intelligent analysis system for dust removal network diagnosis according to claim 1, characterized in that, The system includes pipeline valve fault inference, and the inference method includes: Wind pressure and wind speed analysis: Set a third standard wind pressure value and a third standard wind speed value. If the measured average wind pressure data is less than the third standard wind pressure value, the system pressure is confirmed to be unstable; if the measured average wind speed data is greater than the third standard wind speed value, the system wind speed is diagnosed to be unstable. Probability distribution analysis shows that the wind pressure data deviates from the fitted normal distribution, with the wind pressure data shifting to the left and the wind speed data shifting to the right. The reasoning mechanism analysis adopts forward reasoning. It is known that valve failure will change the flow area of ​​the pipeline, affecting wind pressure and wind speed. The current situation of decreased wind pressure and increased wind speed is consistent with the failure characteristics of pipeline valve not being fully opened. By integrating and analyzing data on the overall equipment operating status, when pipeline valves show open or closed signals but the feedback from the pipeline valves does not match the actual status, and by combining data on decreased wind pressure and increased wind speed, a pipeline valve malfunction can be diagnosed.

8. The intelligent analysis system for dust removal network diagnosis according to claim 1, characterized in that, The system includes wind turbine fault inference, and the inference method includes: Wind pressure and wind speed analysis: Set fourth standard wind pressure and fourth standard wind speed values. If the measured average wind pressure data is less than the fourth standard wind pressure value, the system pressure is confirmed to be unstable; if the measured average wind speed data is less than the fourth standard wind speed value, the system wind speed is diagnosed to be unstable. Inlet and outlet temperature analysis: Set a first inlet and outlet temperature difference value. When the measured inlet and outlet temperature difference is less than the first inlet and outlet temperature difference value, it is inferred that the fan is in an abnormal state. Fan current analysis: Set a second standard current value. When the measured fan current value is greater than the second standard current value, diagnose abnormal fan current. Probability distribution analysis shows that the wind pressure data deviates from the fitted normal distribution, with some wind pressure data falling into the low-value region on the left, and the wind speed data deviating from the normal distribution. The reasoning mechanism analysis uses forward reasoning. It is known that a fan failure will lead to insufficient power, resulting in reduced wind speed and wind pressure. Combined with data analysis, this is consistent with the characteristics of a fan failure. Based on the combined analysis and inference of factors such as decreased wind speed, decreased wind pressure, reduced inlet and outlet temperature difference, and increased current, a fan malfunction was diagnosed.

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