Method for predicting residual life of filter bag of bag type dust collector based on multiple pressure sensors

Abstract

The invention relates to the technical field of bag type dust collectors, in particular to a method for predicting the residual life of a filter bag of a bag type dust collector based on multiple pressure sensors. The multiple pressure sensors are evenly distributed in the bag type dust collector, the sound sensor collects air pressure signals generated in the running process of the filter bag in real time, the cumulative load and the blocking condition of the filter bag are determined by analyzing the local air pressure gradient value of the time domain air pressure signals, and therefore the health state of the filter bag can be judged; the data of the pressure sensors at different positions are fused through the fusion model, so that the accuracy of the monitoring data can be greatly improved, the influence of interference factors can be reduced, the residual life of the filter bag can be intuitively reflected, and the situation that a technician cannot replace the filter bag in time due to early warning information lag is avoided.

Description

Technical Field

[0001] This invention relates to the field of bag filter technology, and in particular to a method for predicting the remaining life of filter bags in bag filters based on multiple pressure sensors. Background Technology

[0002] Baghouse dust collectors, as important environmental protection equipment in industrial production, are widely used in industries such as steel, chemical, and building materials. They purify gas by capturing particulate matter from flue gas through filter bags. However, filter bags gradually age over time, leading to decreased filtration efficiency and even damage, causing dust leaks and resulting in serious environmental pollution and economic losses. Therefore, predicting the remaining lifespan of filter bags before they break has become a crucial issue in the maintenance and management of baghouse dust collectors.

[0003] Currently, filter bag life prediction mainly relies on periodic replacement or manual inspection based on experience. However, these methods often fail to accurately grasp the actual operating status of the filter bags, which may lead to premature or delayed replacement of the filter bags, increasing maintenance costs and potentially causing unplanned equipment downtime.

[0004] With the development of sensor technology, real-time monitoring of filter bag operation using sensors, combined with data analysis techniques for lifespan prediction, has gradually become an effective method. Existing technology uses sensors to monitor changes in flue gas concentration between the filter bag's inlet and outlet in real time, calculates the filter bag's filtration resistance, and thus predicts the filter bag's remaining lifespan. However, this method suffers from drawbacks such as excessive interference factors and delayed early warning information. For example, an increase in flue gas concentration is not solely caused by filter bag damage or aging; various non-filter bag factors can cause concentration fluctuations, potentially leading technicians to mistakenly attribute normal operating conditions to filter bag issues, resulting in inaccurate predicted remaining lifespan. Furthermore, filter bag aging and damage is a gradual process, and flue gas concentration changes typically only become significant after the filter bag has already suffered obvious damage, by which time the optimal maintenance window has passed. Summary of the Invention

[0005] The main objective of this invention is to provide a method for predicting the remaining lifespan of filter bags in baghouse dust collectors based on multiple pressure sensors, in order to solve the technical problems of inaccurate prediction results caused by excessive interference factors and delayed early warning information in the prior art.

[0006] To achieve the above objectives, the present invention provides a method for predicting the remaining lifespan of filter bags in a baghouse dust collector based on multiple pressure sensors, comprising the following steps: S1, Get the current time step. The air pressure signal from the first pressure sensor is preprocessed to obtain the first... The effective time-domain air pressure signal of a pressure sensor; wherein, the pressure sensor is installed inside the bag filter to collect air pressure changes during the operation of the filter bag. ≥1, It is a positive integer; S2, Obtain the first The filter bag length, highest pressure value, and lowest pressure value are measured within the monitoring range of each pressure sensor to determine the first... Local pressure gradient values ​​of a pressure sensor; S3. Determine the cumulative load and local blockage development rate of the filter bag based on the local pressure gradient value; S4, Judgment Is it greater than or equal to? If not, then +1 is assigned to Then return to step S1; if so, output the time-domain air pressure signal, local air pressure gradient value, cumulative load, and local blockage development speed of all pressure sensors, thereby obtaining the feature vector of each pressure sensor; where, This represents the number of pressure sensors; S5. Obtain the fusion model of the pressure sensor and the structural parameters of the entire filter bag. Then, fuse the feature vectors of each pressure sensor using the fusion model and the structural parameters to obtain the filter bag aging index at the current moment. S6. Obtain the average aging rate of the filter bag, and determine the remaining lifespan of the filter bag at the current moment based on the average aging rate and the filter bag aging index.

[0007] Furthermore, step S2 specifically includes the following steps: Get the The monitoring range of the first pressure sensor is used to obtain the monitoring range of the second pressure sensor. The filter bag length monitored by a pressure sensor, and the highest and lowest air pressure values ​​within the monitoring range at the current moment; Obtain the first model, and determine the second model based on the filter bag length, the highest point air pressure value, and the lowest point air pressure value. The local pressure gradient values ​​of each pressure sensor; wherein, the first model is: ; in, Indicates the first Local pressure gradient values ​​of a pressure sensor; Indicates the first Time-domain air pressure signal from a pressure sensor; Indicates the first The length of the filter bag is monitored within the range of each pressure sensor. It is a time variable.

[0008] More preferably, determining the cumulative load of the filter bag based on the local pressure gradient value in step S3 specifically includes the following steps: Using formula Integrating the local pressure gradient over the entire time period yields the gradient integral. To reflect the cumulative load on the filter bag; among which, For the maximum time.

[0009] More preferably, step S3, which determines the local clogging development rate of the filter bag based on the local pressure gradient value, specifically includes the following steps: Using formula Differentiating the local pressure gradient value yields the gradient change rate. To reflect the speed at which local congestion develops.

[0010] More preferably, obtaining the feature vector of each pressure sensor in step S4 specifically includes the following steps: Differentiate the time-domain pressure signal of each pressure sensor to obtain the time-domain pressure change rate; The characteristic vector for each pressure sensor is based on the time-domain air pressure signal, time-domain air pressure change rate, cumulative load of the filter bag, and local blockage development rate. The feature vector is specifically: ; Among them, the For the first The feature vector of a pressure sensor.

[0011] More preferably, step S5 specifically includes the following steps: The length and diameter of the filter bag are obtained. A fusion model is established based on the feature vector of each pressure sensor and the length and diameter of the filter bag. The aging index of the filter bag at the current moment is obtained through the fusion model. The fusion model is specifically as follows: The structural parameters of the entire filter bag include the length of the filter bag. and diameter , All are weighted coefficients; The attenuation coefficient; This represents the current filter bag aging index. This is the ratio of the filter bag height.

[0012] More preferably, step S6 specifically includes the following steps: The average aging rate of the filter bag is obtained, and an aging prediction model for the filter bag is established based on the average aging rate and the filter bag aging index. The remaining lifespan of the filter bag at the current moment is determined according to the filter bag aging prediction model. Specifically, the filter bag aging prediction model is as follows: ; in, For the current moment, and ; The average aging rate of the filter bag.

[0013] Preferably, the number of pressure sensors is not less than three, and they are evenly arranged along the airflow direction and cross-sectional direction inside the filter bag.

[0014] More preferably, the pressure sensor adopts a "three-layer four-point" arrangement structure, that is, the pressure sensor is arranged in the ash hopper area and the middle area clean air outlet area of ​​the bag filter, and the pressure sensor is distributed in a rectangular or rhomboid shape in cross-section.

[0015] Preferably, the angle between the sensing direction of the pressure sensor and the airflow direction is 0° to 30°, and the sampling frequency is ≥10Hz.

[0016] Compared with the prior art, the present invention has the following beneficial effects: In this invention, multiple pressure sensors are evenly distributed in the bag filter, and sound sensors collect the air pressure signals generated during the operation of the filter bags in real time. By analyzing the local air pressure gradient values ​​of the time-domain air pressure signals, the cumulative load and clogging status of the filter bags are determined, thereby judging the health status of the filter bags. In this invention, the data from pressure sensors in different locations are fused using a fusion model, which not only significantly improves the accuracy of monitoring data and reduces the influence of interference factors, but also intuitively reflects the remaining lifespan of the filter bags, avoiding situations where delayed early warning information prevents technicians from replacing the filter bags in a timely manner. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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 the structures shown in these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a method for predicting the remaining lifespan of a bag filter bag based on multiple pressure sensors, according to an embodiment of the present invention.

[0019] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0023] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0024] Please see Figure 1 This embodiment provides a method for predicting the remaining lifespan of filter bags in a baghouse dust collector based on multiple pressure sensors, including the following steps: S1, Get the current time step. The air pressure signal from the first pressure sensor is preprocessed to obtain the first... The effective time-domain air pressure signal of a pressure sensor; wherein, the pressure sensor is installed inside the bag filter to collect air pressure changes during the operation of the filter bag. ≥1, It is a positive integer.

[0025] In this embodiment, multiple pressure sensors are arranged next to the filter bags inside the baghouse dust collector to monitor the air pressure signal during filter bag operation in real time. Changes in the air pressure signal can reflect the wear, blockage, and leakage of the filter bags. By preprocessing the air pressure signal through filtering and noise reduction, a stable, reliable, and effective time-domain air pressure signal is obtained. .

[0026] S2, Obtain the first The filter bag length, highest pressure value, and lowest pressure value are measured within the monitoring range of each pressure sensor to determine the first... The local pressure gradient value of each pressure sensor; the local pressure gradient value is used to quantify the local blockage, wear and leakage status of the filter bag.

[0027] S3. Determine the cumulative load and local blockage development rate of the filter bag based on the local pressure gradient value; S4, Judgment Is it greater than or equal to? If not, then +1 is assigned to Then return to step S1; if so, output the time-domain air pressure signal, local air pressure gradient value, cumulative load, and local blockage development speed of all pressure sensors, thereby obtaining the feature vector of each pressure sensor; where, This represents the number of pressure sensors; S5. Obtain the fusion model of the pressure sensor and the structural parameters of the entire filter bag. Then, fuse the feature vectors of each pressure sensor using the fusion model and the structural parameters to obtain the filter bag aging index at the current moment. S6. Obtain the average aging rate of the filter bag, and determine the remaining lifespan of the filter bag at the current moment based on the average aging rate and the filter bag aging index.

[0028] In one embodiment, step S2 specifically includes the following steps: Get the The monitoring range of the first pressure sensor is used to obtain the monitoring range of the second pressure sensor. The filter bag length monitored by a pressure sensor, and the highest and lowest air pressure values ​​within the monitoring range at the current moment; Obtain the first model, and determine the second model based on the filter bag length, the highest point air pressure value, and the lowest point air pressure value. The local pressure gradient values ​​of each pressure sensor; wherein, the first model is: ; in, Indicates the first Local pressure gradient values ​​of a pressure sensor; Indicates the first Time-domain air pressure signal from a pressure sensor; Indicates the first The length of the filter bag is monitored within the range of each pressure sensor. It is a time variable.

[0029] In this embodiment, as a further preferred embodiment, the step S3 of determining the cumulative load of the filter bag based on the local air pressure gradient value specifically includes the following steps: Using formula Integrating the local pressure gradient over the entire time period yields the gradient integral. To reflect the cumulative load on the filter bag; among which, For the maximum time.

[0030] As a further preferred embodiment, the step S3 of determining the local clogging development rate of the filter bag based on the local pressure gradient value specifically includes the following steps: Using formula Differentiating the local pressure gradient value yields the gradient change rate. To reflect the speed at which local congestion develops.

[0031] As a further preferred embodiment, obtaining the feature vector of each pressure sensor in step S4 specifically includes the following steps: Differentiate the time-domain pressure signal of each pressure sensor to obtain the time-domain pressure change rate; The characteristic vector for each pressure sensor is based on the time-domain air pressure signal, time-domain air pressure change rate, cumulative load of the filter bag, and local blockage development rate. The feature vector is specifically: ; Among them, the For the first The feature vector of a pressure sensor.

[0032] More preferably, step S5 specifically includes the following steps: The length and diameter of the filter bag are obtained. A fusion model is established based on the feature vector of each pressure sensor and the length and diameter of the filter bag. The aging index of the filter bag at the current moment is obtained through the fusion model. The fusion model is specifically as follows: The structural parameters of the entire filter bag include the length of the filter bag. and diameter , These are all weighting coefficients used to balance the contributions of different sensors and features to the prediction of filter bag lifespan. The optimal values ​​can be obtained through experimental calibration or machine learning methods. This is the attenuation coefficient, which is related to the pressure sensor's position and is used to correct for the influence of pressure signals at different locations. It can be calculated through height or position parameterization, for example... ,in For the height of the pressure sensor, This is the proportionality coefficient; This represents the current filter bag aging index. This represents the ratio of the filter bag heights. ,in For the first The height of each pressure sensor from the bottom of the filter bag, where H is the total height of the filter bag.

[0033] In this embodiment, in the above fusion model The normalization coefficient is used to standardize the weighted contributions of different pressure sensors, ensuring that the fusion result is independent of the number of sensors. It also unifies the dimensions, keeping the results within a reasonable numerical range, which is beneficial for subsequent calculation and prediction of the remaining filter bag life. The normalization operation enhances the stability and operability of the fusion model.

[0034] More preferably, step S6 specifically includes the following steps: The average aging rate of the filter bag is obtained, and an aging prediction model for the filter bag is established based on the average aging rate and the filter bag aging index. The remaining lifespan of the filter bag at the current moment is determined according to the filter bag aging prediction model. Specifically, the filter bag aging prediction model is as follows: ; in, For the current moment, and ; The average aging rate of the filter bag.

[0035] In one embodiment, preferably, the number of pressure sensors is no less than three, to ensure that the collected signals have spatial distribution characteristics and are uniformly arranged along the airflow direction and cross-sectional direction inside the filter bag.

[0036] It is worth noting that for large dust collectors (≥1000 filter bags), it is preferable to place one sensor point for every 10 to 20 filter bags.

[0037] The pressure sensor adopts a "three-layer four-point" arrangement structure, that is, pressure sensors are arranged in the ash hopper area and the central area clean air outlet area of ​​the bag filter. The pressure sensors are distributed in a rectangular or rhomboid shape in cross-section to achieve uniform spatial sampling.

[0038] The pressure sensor has a sensing direction with an angle of 0° to 30° with the airflow direction and a sampling frequency of ≥10Hz to avoid high-disturbance areas such as blowpipes and access doors.

[0039] The monitoring of filter bags should cover both new filter bags and the operation of existing filter bags. A difference of less than 5% in the air pressure signal within the monitoring area can be considered as a characteristic point of the same area, thus reducing redundant sensors.

[0040] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for predicting the remaining lifespan of filter bags in a baghouse dust collector based on multiple pressure sensors, characterized in that, Includes the following steps: S1, Get the current time step. The air pressure signal from the first pressure sensor is preprocessed to obtain the first... The effective time-domain air pressure signal of a pressure sensor; wherein, the pressure sensor is installed inside the bag filter to collect air pressure changes during the operation of the filter bag. ≥1, It is a positive integer; S2, Obtain the first The filter bag length, highest pressure value, and lowest pressure value are measured within the monitoring range of each pressure sensor to determine the first... Local pressure gradient values ​​of a pressure sensor; S3. Determine the cumulative load and local blockage development rate of the filter bag based on the local pressure gradient value; S4, Judgment Is it greater than or equal to? If not, then +1 is assigned to Then return to step S1; if so, output the time-domain air pressure signal, local air pressure gradient value, cumulative load, and local blockage development speed of all pressure sensors, thereby obtaining the feature vector of each pressure sensor; where, This represents the number of pressure sensors; S5. Obtain the fusion model of the pressure sensor and the structural parameters of the entire filter bag. Then, fuse the feature vectors of each pressure sensor using the fusion model and the structural parameters to obtain the filter bag aging index at the current moment. S6. Obtain the average aging rate of the filter bag, and determine the remaining lifespan of the filter bag at the current moment based on the average aging rate and the filter bag aging index.

2. The method for predicting the remaining lifespan of bag filters based on multiple pressure sensors according to claim 1, characterized in that, Step S2 specifically includes the following steps: Get the The monitoring range of the first pressure sensor is used to obtain the monitoring range of the second pressure sensor. The filter bag length monitored by a pressure sensor, and the highest and lowest air pressure values ​​within the monitoring range at the current moment; Obtain the first model, and determine the second model based on the filter bag length, the highest point air pressure value, and the lowest point air pressure value. The local pressure gradient values ​​of each pressure sensor; wherein, the first model is: ； in, Indicates the first Local pressure gradient values ​​of a pressure sensor; Indicates the first Time-domain air pressure signal from a pressure sensor; Indicates the first The length of the filter bag is monitored within the range of each pressure sensor. It is a time variable.

3. The method for predicting the remaining lifespan of bag filters based on multiple pressure sensors according to claim 2, characterized in that, The step S3, which determines the cumulative load of the filter bag based on the local pressure gradient value, specifically includes the following steps: Using formula Integrating the local pressure gradient over the entire time period yields the gradient integral. To reflect the cumulative load on the filter bag; among which, For the maximum time.

4. The method for predicting the remaining lifespan of bag filters based on multiple pressure sensors according to claim 3, characterized in that, Step S3, which determines the local clogging development rate of the filter bag based on the local pressure gradient value, specifically includes the following steps: Using formula Differentiating the local pressure gradient value yields the gradient change rate. To reflect the speed at which local congestion develops.

5. The method for predicting the remaining lifespan of bag filters based on multiple pressure sensors according to claim 4, characterized in that, The step S4, which involves obtaining the feature vector of each pressure sensor, specifically includes the following steps: Differentiate the time-domain pressure signal of each pressure sensor to obtain the time-domain pressure change rate; The characteristic vector for each pressure sensor is based on the time-domain air pressure signal, time-domain air pressure change rate, cumulative load of the filter bag, and local blockage development rate. The feature vector is specifically: ； Among them, the For the first The feature vector of a pressure sensor.

6. The method for predicting the remaining lifespan of bag filters based on multiple pressure sensors according to claim 5, characterized in that, Step S5 specifically includes the following steps: The length and diameter of the filter bag are obtained. A fusion model is established based on the feature vector of each pressure sensor and the length and diameter of the filter bag. The aging index of the filter bag at the current moment is obtained through the fusion model. The fusion model is specifically as follows: The structural parameters of the entire filter bag include the length of the filter bag. and diameter , All are weighted coefficients; The attenuation coefficient; This represents the current filter bag aging index. This is the ratio of the filter bag height.

7. The method for predicting the remaining lifespan of bag filters based on multiple pressure sensors according to claim 6, characterized in that, Step S6 specifically includes the following steps: The average aging rate of the filter bag is obtained, and an aging prediction model for the filter bag is established based on the average aging rate and the filter bag aging index. The remaining lifespan of the filter bag at the current moment is determined according to the filter bag aging prediction model. Specifically, the filter bag aging prediction model is as follows: ； in, For the current moment, and ; This represents the average aging rate of the filter bag.

8. The method for predicting the remaining lifespan of bag filters based on multiple pressure sensors according to claim 1, characterized in that, The number of pressure sensors is no less than three, and they are evenly arranged along the airflow direction and cross-sectional direction inside the filter bag.

9. The method for predicting the remaining lifespan of bag filters based on multiple pressure sensors according to claim 8, characterized in that, The pressure sensor adopts a "three-layer four-point" arrangement structure, that is, the pressure sensor is arranged in the ash hopper area and the middle area clean air outlet area of ​​the bag filter, and the pressure sensor is distributed in a rectangular or rhomboid shape in cross-section.

10. The method for predicting the remaining lifespan of bag filters based on multiple pressure sensors according to claim 1, characterized in that, The pressure sensor has a sensing direction with an angle of 0° to 30° with the airflow direction, and a sampling frequency of ≥10Hz.

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