A fault diagnosis system and method for bag type dust removal of a coal-fired power plant

Abstract

The application discloses a coal-fired power plant bag type dust removal fault diagnosis system and method, and the system comprises a filter bag performance online sensing module, a dust cleaning device airflow state monitoring module, a pulse valve operation characteristic acquisition module, a multi-source data fusion and feature extraction module, a bag type dust removal fault intelligent diagnosis module, and a full life cycle management and operation and maintenance decision module. Through the filter bag performance online sensing module, the filter bag operation state full parameter real-time acquisition is realized, the filter bag comprehensive inactivation index calculation formula is combined to complete the inactivation degree quantitative evaluation, the offline detection operation such as fluorescent powder leak detection is not needed to carry out, the filter bag fault online accurate judgment is realized, and the continuous and stable operation of the dust remover is ensured.

Description

Technical Field

[0001] This invention relates to the field of flue gas purification technology for coal-fired power plants, and in particular to a fault diagnosis system and method for baghouse dust collectors in coal-fired power plants. Background Technology

[0002] Against the backdrop of the comprehensive implementation of ultra-low emission standards for flue gas pollutants from coal-fired power units, baghouse dust collectors, as the core equipment for particulate matter control in coal-fired power plants, directly determine the environmental compliance, safe operation, and energy conservation levels of the units through their operational stability. Filter bags are the core component of baghouse dust collectors, while the cleaning device and pulse valves are key actuators for stable system operation. The operating status of these three components jointly affects dust removal efficiency, system resistance, filter bag lifespan, and emission compliance. Currently, baghouse dust collectors in coal-fired power plants face several intractable technical bottlenecks in practical engineering applications, hindering the industry's intelligent and refined development.

[0003] Existing bag filter fault diagnosis technologies have significant problems: First, filter bag performance testing relies solely on offline laboratory testing, making it impossible to quantify the degree of filter bag inactivation online. There is a lack of a comprehensive inactivation evaluation model covering operating pressure differential, emission concentration, and air permeability. Filter bag failure judgment depends on manual experience, resulting in a high misjudgment rate and delayed early warning. Second, the uniformity of the cleaning device's pulse jet cleaning is judged only through qualitative observation, lacking quantitative evaluation indicators and calculation formulas. Problems such as uneven airflow distribution in the pulse jet pipe and the bag cage head occupying the effective filtration area cannot be accurately identified through data, leaving no quantitative basis for optimizing the cleaning system. Third, pulse valve failure... Fault diagnosis can only monitor the continuity of coil circuits and cannot identify diaphragm mechanical faults. It also requires the installation of sensors on individual valves, resulting in complex hardware deployment and high maintenance costs. There is no non-contact quantitative diagnostic method based on stress wave signals. Fourth, the multi-source operating data are independent of each other and cannot achieve integrated analysis of filter bag performance, dust removal airflow, pulse valve status, and flue gas conditions. Fault feature extraction is incomplete, making it difficult to achieve accurate location and early warning. Fifth, there is a lack of an integrated system that combines online sensing, quantitative diagnosis, intelligent identification, and full life-cycle management, which cannot meet the digital and intelligent operation and maintenance needs of bag filters in coal-fired power plants. Summary of the Invention

[0004] The present invention aims to solve at least one of the problems existing in the prior art, and provides a fault diagnosis system and method for bag filter dust collectors in coal-fired power plants.

[0005] One aspect of the present invention provides a bag filter fault diagnosis system for coal-fired power plants, including an online filter bag performance sensing module, an airflow status monitoring module for the dust removal device, a pulse valve operation feature acquisition module, a multi-source data fusion and feature extraction module, a bag filter fault intelligent diagnosis module, and a full life cycle management and operation and maintenance decision module. The filter bag performance online sensing module is used to collect in real time the operating condition parameters, filter bag performance parameters and dust emission parameters of a single compartment and a single row of filter bags in a baghouse dust collector of a coal-fired power plant, calculate the air permeability of the filter bag online and output the raw sensing data. The airflow state monitoring module of the dust removal device is used to collect the blowing pressure, induced airflow velocity and airflow distribution parameters of the bag filter dust removal device, statistically analyze the uniformity characteristics of the blowing air distribution and output airflow state data. The pulse valve operation feature acquisition module is used to acquire the stress wave signal and the injection timing signal generated by the pulse valve action, extract the pulse valve diaphragm action features and output the pulse valve operation feature data. The multi-source data fusion and feature extraction module is used to preprocess, spatiotemporally align and fuse the raw sensing data, airflow state data and pulse valve operation feature data, construct a standardized fault feature library and output fused feature data. The intelligent fault diagnosis module for bag filters is used to perform pattern matching based on fused feature data and fault feature library, identify bag filter fault types, assess the remaining life of filter bags, and output fault diagnosis results and early warning information. The full life cycle management and operation and maintenance decision module is used to establish a full life cycle file of filter bags based on fault diagnosis results, generate targeted operation and maintenance strategies, and output decision instructions.

[0006] Optionally, the filter bag performance online sensing module includes a filter bag differential pressure monitoring unit, a dust concentration detection unit, a flue gas operating condition acquisition unit, a filter bag air permeability online calculation unit, and a filter bag comprehensive deactivation index calculation unit; The filter bag differential pressure monitoring unit is used to collect the inlet and outlet differential pressure data of a single row of filter bags; The dust concentration detection unit is used to collect dust emission concentration data at the filter bag outlet; The flue gas condition acquisition unit is used to collect flue gas temperature and humidity data inside the dust collector; The filter bag air permeability online calculation unit is used to calculate the real-time air permeability of the filter bag based on pressure difference, flue gas flow rate and filter bag area parameters. The filter bag comprehensive deactivation index calculation unit is used to calculate the filter bag comprehensive deactivation index by normalizing the filter bag's real-time operating pressure difference, real-time emission concentration, and real-time air permeability with the corresponding benchmark values ​​and then weighting and summing them. And / or, the filter bag comprehensive deactivation index calculation unit is used to calculate the filter bag comprehensive deactivation index according to the following formula: ; in, The filter bag deactivation index is dimensionless. , , These are the weighting coefficients for operating pressure difference, emission concentration, and permeability, respectively, to meet the following requirements. ; Real-time operating pressure differential of the filter bag; The rated design operating differential pressure for the filter bag; Real-time dust emission concentration at the filter bag outlet; The emission concentration limits for particulate matter from coal-fired power units; Real-time air permeability of the filter bag; This refers to the initial air permeability of the filter bag before it leaves the factory.

[0007] Optionally, the airflow status monitoring module of the dust removal device includes a blowpipe pressure acquisition unit, an induced airflow velocity detection unit, a bag cage head airflow distribution monitoring unit, a blowpipe gas distribution characteristic statistics unit, and a blowpipe airflow uniformity calculation unit. The blowpipe pressure acquisition unit is used to collect the outlet pressure data of each nozzle of the dust removal blowpipe. The induced airflow velocity detection unit is used to collect the velocity data of the primary and secondary induced airflows of the dust removal device. The airflow distribution monitoring unit at the filter bag cage head is used to collect airflow velocity distribution data in the filter bag cage head area; The jet gas distribution characteristic statistical unit is used to statistically analyze the standard deviation and arithmetic mean of the airflow pressure at the outlet of each nozzle of the dust removal jet pipe. The uniformity calculation unit for the jetting airflow is used to calculate the uniformity coefficient of the jetting airflow based on the ratio of the standard deviation to the arithmetic mean of the airflow pressure at the outlet of each nozzle of the cleaning jetting pipe.

[0008] Optionally, the jet airflow uniformity calculation unit is used to calculate the jet airflow uniformity coefficient according to the following formula: ; in, The uniformity coefficient of the jet airflow is dimensionless. The standard deviation of the airflow pressure at the outlet of each nozzle of the dust removal jet pipe; This is the arithmetic mean of the airflow pressure at the outlet of each nozzle of the dust removal blowpipe.

[0009] Optionally, the pulse valve operation feature acquisition module includes a stress wave sensing unit, a pulse timing acquisition unit, a diaphragm action feature extraction unit, a compressed air main pipe status monitoring unit, and a pulse valve fault feature matching calculation unit. The stress wave sensing unit is used to collect stress wave signals generated by the action of the pulse valve on the compressed air main pipe. The pulse timing acquisition unit is used to synchronously acquire the pulse jet timing signal of the dust removal system; The diaphragm motion feature extraction unit is used to extract the feature parameters corresponding to the opening and closing of the pulse valve diaphragm from the stress wave signal. The compressed air main pipe status monitoring unit is used to collect main pipe pressure and leakage status parameters; The pulse valve fault feature matching calculation unit is used to calculate the pulse valve fault feature matching degree based on the cosine similarity between the pulse valve stress wave signal feature value collected in real time and the normal operation standard stress wave signal feature value.

[0010] Optionally, the pulse valve fault feature matching calculation unit is used to calculate the pulse valve fault feature matching degree according to the following formula: ; in, The fault characteristic matching degree of the pulse valve is dimensionless; For the real-time acquisition of pulse valve stress wave signal One eigenvalue; The standard stress wave signal for normal operation of the pulse valve One eigenvalue; This is the sequence number of the stress wave characteristic value.

[0011] Optionally, the multi-source data fusion and feature extraction module includes a data preprocessing unit, a spatiotemporal feature alignment unit, a multi-source feature fusion unit, and a fault feature database construction unit; The data preprocessing unit is used to perform noise reduction, normalization and outlier removal on multi-source data. The spatiotemporal feature alignment unit is used to complete the spatial and temporal alignment of multi-source data according to the dust collector compartment, number, and time sequence dimensions. The multi-source feature fusion unit is used to perform feature-level fusion on the aligned data to generate a unified fault feature vector; The fault feature library construction unit is used to store standard feature data of filter bag deactivation, uneven blowing, and pulse valve failure.

[0012] Optionally, the intelligent fault diagnosis module for bag filters includes a fault mode recognition unit, a life assessment unit, a fault early warning unit, and a diagnostic result output unit. The fault mode recognition unit is used to match the fused feature vector with the fault feature library, and combine the filter bag comprehensive deactivation index, the uniformity coefficient of the blowing airflow, and the fault feature matching degree of the pulse valve to determine the fault type and fault location. The life assessment unit is used to calculate the remaining service life of the filter bag based on the filter bag comprehensive deactivation index; The fault early warning unit is used to generate graded early warning information according to the fault level; The diagnostic result output unit is used to visualize and output the fault location, type, level, and life assessment results.

[0013] Optionally, the full life cycle management and operation and maintenance decision module includes a life cycle file management unit, an operation and maintenance strategy generation unit, a replacement and scrapping decision unit, and a group-level guideline adaptation unit. The life cycle file management unit is used to record the entire life cycle data of filter bag selection, testing, operation, maintenance and replacement; The operation and maintenance strategy generation unit is used to generate operation and maintenance plans for dust removal parameter optimization, on-site inspection and maintenance, and component replacement based on the fault diagnosis results. The replacement and scrapping decision unit is used to determine the timing of filter bag replacement or scrapping based on the remaining lifespan of the filter bag. The group-level guideline adaptation unit is used to match and adapt operation and maintenance decisions with the group-level filter bag life management guidelines.

[0014] Another aspect of the present invention provides a diagnostic method for a bag filter fault diagnosis system in a coal-fired power plant, applied to the bag filter fault diagnosis system for a coal-fired power plant described above, the diagnostic method comprising the following steps: Step 1: Online acquisition of multi-source parameters. The filter bag performance, cleaning airflow and pulse valve operation data are collected simultaneously through the filter bag performance online sensing module, the dust removal device airflow status monitoring module and the pulse valve operation characteristic acquisition module. The comprehensive deactivation index of the filter bag, the uniformity coefficient of the blowing airflow and the pulse valve fault characteristic matching degree are quantitatively calculated respectively. Step 2: Multi-source data preprocessing and fusion. The multi-source data fusion and feature extraction module performs noise reduction, normalization, spatiotemporal alignment and feature fusion on the original data and quantization calculation results to generate fused feature data and match it with the fault feature library. Step 3: Intelligent fault diagnosis and life assessment. The intelligent fault diagnosis module for bag filters identifies the fault type and location based on fused feature data and quantitative calculation results, assesses the remaining life of the filter bags, and generates early warning information. Step 4: Full life cycle management and operation and maintenance decision-making. The full life cycle management and operation and maintenance decision-making module establishes a full life cycle file for the filter bag and generates operation and maintenance strategies and replacement and scrapping decision instructions. Step 5: Output and Iterative Optimization of Diagnostic Results. Visualize the results of fault diagnosis, life assessment, quantitative calculation, and operation and maintenance decisions, and update the fault feature library and diagnostic model simultaneously. Compared with the prior art, the present invention has the following advantages: This invention achieves real-time acquisition of all parameters of filter bag operating status through an online filter bag performance sensing module. Combined with the filter bag comprehensive deactivation index calculation formula, it quantitatively assesses the degree of deactivation, eliminating the need for offline testing operations such as fluorescent powder leak detection during system shutdown. This enables accurate online diagnosis of filter bag faults, ensuring continuous and stable operation of the dust collector. By calculating the uniformity coefficient of the pulsed airflow, it quantitatively evaluates the uniformity of the cleaning pulsed airflow, accurately identifying problems such as uneven pulsed airflow and airflow deviation. This provides data support for optimizing the pulsed air pipe orifice diameter and modifying the bag cage head, improving cleaning efficiency, reducing system operating resistance, and minimizing compressed air energy consumption and abnormal filter bag wear. Furthermore, by using the stress wave signal from the compressed air main pipe combined with the pulse valve fault characteristic matching degree calculation, it achieves non-contact quantitative diagnosis of pulse valve faults. This eliminates the need for sensors on individual pulse valves, reducing hardware fault points and accurately identifying various faults such as diaphragm damage, opening / closing malfunctions, and timing disorders, improving diagnostic reliability and maintenance efficiency. By integrating multi-dimensional data on filter bag performance, cleaning airflow, pulse valve status, and flue gas conditions, and combining this with quantitative calculations, a unified fault characteristic system is constructed. This avoids misjudgments caused by relying on single parameters, enabling early fault identification, precise location, and tiered warnings, thereby reducing the risk of exceeding environmental standards. The system also quantifies the degree of filter bag failure and remaining lifespan through a comprehensive filter bag deactivation index, establishing a full life-cycle electronic record. This scientifically determines the timing of filter bag repair, replacement, and disposal, filling the gap in the industry's quantitative management solutions for the entire filter bag lifecycle and reducing operation and maintenance costs. Attached Figure Description

[0015] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0016] Figure 1 This is a schematic diagram of the module of the bag filter fault diagnosis system for coal-fired power plants provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the online performance sensing module for filter bags provided in an embodiment of the present invention. Figure 3 A schematic diagram of the airflow status monitoring module for the dust removal device provided in this embodiment of the invention; Figure 4 This is a schematic diagram of the pulse valve operation feature acquisition module provided in an embodiment of the present invention; Figure 5 A flowchart illustrating the diagnostic method for a bag filter fault diagnosis system in a coal-fired power plant, as provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are presented in the embodiments of the present invention to facilitate a better understanding of the invention. However, the technical solutions claimed in the present invention can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with and referenced by each other without contradiction.

[0018] Please see Figures 1 to 4 This invention provides a fault diagnosis system for baghouse dust collectors in coal-fired power plants, including an online sensing module for filter bag performance, a module for monitoring the airflow status of the dust removal device, a module for acquiring the operating characteristics of pulse valves, a module for multi-source data fusion and feature extraction, a module for intelligent fault diagnosis of baghouse dust collectors, and a module for full life cycle management and operation and maintenance decision-making.

[0019] The online filter bag performance sensing module is used to collect real-time operating parameters, filter bag performance parameters, and dust emission parameters of single-compartment and single-row filter bags in baghouse dust collectors of coal-fired power plants. It calculates filter bag permeability online and uses a comprehensive filter bag deactivation index calculation formula to quantitatively assess the degree of filter bag deactivation, outputting standardized raw sensing data and deactivation index results. This provides fundamental data support for filter bag fault diagnosis and lifespan assessment. The module includes a filter bag differential pressure monitoring unit, a dust concentration detection unit, a flue gas operating condition acquisition unit, an online filter bag permeability calculation unit, and a comprehensive filter bag deactivation index calculation unit.

[0020] The filter bag differential pressure monitoring unit is used to collect real-time differential pressure data at the inlet and outlet of a single row of filter bags and distinguish the differential pressure change characteristics corresponding to different failure modes such as filter bag blockage and damage.

[0021] The dust concentration detection unit is used to collect dust emission concentration data on the clean air side of the filter bag outlet and to identify dust escape caused by filter bag damage.

[0022] The flue gas condition acquisition unit is used to collect parameters such as flue gas temperature, humidity, and flue gas velocity inside the dust collector, eliminating the interference of operating condition fluctuations on the performance judgment of filter bags.

[0023] The filter bag air permeability online measurement unit is used to measure the real-time air permeability of the filter bag based on parameters such as filter bag pressure difference, flue gas flow rate, and effective filtration area of ​​the filter bag, reflecting the degree of filter bag pore blockage and fiber damage.

[0024] The filter bag comprehensive deactivation index calculation unit is used to quantitatively calculate the degree of filter bag deactivation based on the collected pressure difference, concentration, and air permeability parameters, using the filter bag comprehensive deactivation index calculation formula.

[0025] Specifically, the filter bag comprehensive deactivation index calculation unit is used to calculate the filter bag comprehensive deactivation index according to the following filter bag comprehensive deactivation index calculation formula: ; in, The filter bag deactivation index is dimensionless. , , These are the weighting coefficients for operating pressure difference, emission concentration, and permeability, respectively, to meet the following requirements. ; Real-time operating pressure differential of the filter bag; The rated design operating differential pressure for the filter bag; Real-time dust emission concentration at the filter bag outlet; The emission concentration limits for particulate matter from coal-fired power units; Real-time air permeability of the filter bag; This refers to the initial air permeability of the filter bag before it leaves the factory.

[0026] The airflow status monitoring module for the dust removal device is used to collect parameters such as the pulse jet pressure, induced airflow velocity, and airflow distribution at the bag cage head of the bag filter dust collector. It quantifies and evaluates the uniformity of the pulse jet cleaning process using a formula for calculating the uniformity coefficient of the pulse jet airflow, identifies faults such as uneven cleaning, pulse jet failure, and airflow deviation, and outputs airflow status characteristic data and uniformity evaluation results. It includes a pulse jet pressure acquisition unit, an induced airflow velocity detection unit, a bag cage head airflow distribution monitoring unit, a pulse jet gas distribution characteristic statistics unit, and a pulse jet airflow uniformity calculation unit.

[0027] The blowpipe pressure acquisition unit is used to collect instantaneous pressure data at the outlet of each nozzle of the dust removal blowpipe, reflecting the airflow distribution status of the blowpipe.

[0028] The induced airflow velocity detection unit is used to collect real-time velocity data of the primary and secondary induced airflows of the dust removal device to evaluate the excitation effect of the dust removal airflow.

[0029] The filter bag cage head airflow distribution monitoring unit is used to collect airflow velocity distribution data in the filter bag cage head area and determine the impact of the head structure on the filtration area and airflow distribution.

[0030] The injection gas distribution characteristic statistical unit is used to calculate the standard deviation and arithmetic mean of the airflow pressure based on the pressure data of each nozzle of the injection pipe.

[0031] The jet airflow uniformity calculation unit is used to perform quantitative calculations of dust removal uniformity based on statistical pressure parameters and the jet airflow uniformity coefficient calculation formula.

[0032] Specifically, the jet airflow uniformity calculation unit is used to calculate the jet airflow uniformity coefficient according to the following jet airflow uniformity coefficient calculation formula: ; in, The uniformity coefficient of the jet airflow is dimensionless. The standard deviation of the airflow pressure at the outlet of each nozzle of the dust removal jet pipe; This is the arithmetic mean of the airflow pressure at the outlet of each nozzle of the dust removal blowpipe.

[0033] The pulse valve operation feature acquisition module is used to acquire stress wave signals and injection timing signals generated by the pulse valve's operation, extract feature parameters corresponding to the opening and closing timing of the pulse valve diaphragm, and complete the quantitative matching of fault features through the pulse valve fault feature matching degree calculation formula. This eliminates the need for sensors on individual valves, achieving contactless acquisition, feature extraction, and location of pulse valve faults, and outputting pulse valve operation feature data and matching degree results. It includes a stress wave sensing unit, a pulse timing acquisition unit, a diaphragm action feature extraction unit, a compressed air main pipe status monitoring unit, and a pulse valve fault feature matching calculation unit.

[0034] The stress wave sensing unit is used to set up acquisition nodes on the compressed air main pipe of the dust removal system to collect stress wave signals generated by the opening and closing of the pulse valve and distinguish the signal characteristics of normal operation and fault operation.

[0035] The pulse timing acquisition unit is used to synchronously acquire the pulse jet control timing signal of the dust removal system and establish a one-to-one correspondence between the stress wave signal and the pulse valve action.

[0036] The diaphragm motion feature extraction unit is used to extract the amplitude, frequency, and time delay characteristic parameters corresponding to the opening and closing of the pulse valve diaphragm from the stress wave signal, and to identify faults such as diaphragm damage and jamming.

[0037] The compressed air main pipe status monitoring unit is used to collect main pipe pressure and leakage status parameters, and eliminate the interference of abnormal air source on pulse valve diagnosis.

[0038] The pulse valve fault feature matching calculation unit is used to complete the quantitative matching of fault features based on real-time stress wave features and standard feature parameters, through the pulse valve fault feature matching degree calculation formula.

[0039] Specifically, the pulse valve fault feature matching calculation unit is used to calculate the pulse valve fault feature matching degree according to the following pulse valve fault feature matching degree calculation formula: ; in, The fault characteristic matching degree of the pulse valve is dimensionless; For the real-time acquisition of pulse valve stress wave signal One eigenvalue; The standard stress wave signal for normal operation of the pulse valve One eigenvalue; This is the sequence number of the stress wave characteristic value.

[0040] The multi-source data fusion and feature extraction module is used to preprocess, spatiotemporally align, and fuse raw data on filter bag performance, dust removal airflow status, and pulse valve operation characteristics. It constructs a standardized fault feature library covering the entire baghouse dust collector system and outputs unified fused feature data, providing standardized input for intelligent fault diagnosis. It includes a data preprocessing unit, a spatiotemporal feature alignment unit, a multi-source feature fusion unit, and a fault feature library construction unit.

[0041] The data preprocessing unit is used to perform noise reduction, normalization transformation, and outlier removal on multi-source acquired data to eliminate the interference of noise and abnormal data on diagnostic results.

[0042] The spatiotemporal feature alignment unit is used to align the spatial location and time series of multi-source data according to the dust collector compartment number, filter bag number, and pulse jet sequence, ensuring data correlation and consistency.

[0043] The multi-source feature fusion unit is used to perform feature-level fusion on the aligned data, converting discrete single-parameter features into a unified fault feature vector covering filter bags, dust removal, and pulse valves.

[0044] The fault feature library construction unit is used to store standard feature data of typical faults such as filter bag damage, filter bag blockage, uneven blowing, pulse valve diaphragm failure, and pulse valve timing disorder, as a basis for diagnosis and matching.

[0045] The intelligent fault diagnosis module for baghouse dust collectors is used to perform pattern matching based on fused feature data and a fault feature library. It combines the comprehensive deactivation index of the filter bag, the uniformity coefficient of the pulse jet airflow, and the matching degree of pulse valve fault features to automatically identify the fault type, locate the fault position, assess the remaining lifespan of the filter bag, generate graded early warning information, and output diagnostic results. It includes a fault mode recognition unit, a lifespan assessment unit, a fault early warning unit, and a diagnostic result output unit.

[0046] The fault mode recognition unit is used to perform similarity matching between the fused feature vector and the fault feature library, and combined with the results of the three major quantification calculations, to determine the fault type, fault compartment, fault number, and fault severity level.

[0047] The life assessment unit is used to calculate the remaining lifespan of the filter bag based on the filter bag's comprehensive deactivation index, combined with historical operating data and attenuation patterns.

[0048] The fault early warning unit is used to generate three levels of early warning information based on the fault level and development trend: immediate warning, time-limited handling, and close monitoring.

[0049] The diagnostic results output unit is used to visualize the fault location, fault type, severity level, life assessment results, and quantitative calculation indicators, and supports data traceability and historical query.

[0050] The full lifecycle management and operation and maintenance decision-making module is used to establish electronic files for the entire lifecycle of filter bags based on fault diagnosis results and quantitative calculation indicators, generate targeted operation and maintenance strategies, determine the timing of filter bag replacement and scrapping, and adapt to the group-level filter bag lifecycle management guidelines to achieve standardized and intelligent operation and maintenance of bag filters. It includes a lifecycle file management unit, an operation and maintenance strategy generation unit, a replacement and scrapping decision-making unit, and a group-level guideline adaptation unit.

[0051] The life cycle record management unit is used to record the entire life cycle data of filter bag selection parameters, factory test data, installation information, operating parameters, quantitative calculation results, maintenance records, and fault records, forming a traceable electronic record.

[0052] The operation and maintenance strategy generation unit is used to generate customized operation and maintenance solutions based on fault diagnosis results and quantitative indicators, such as dust removal parameter optimization, pulse jet device overhaul, pulse valve repair, and partial replacement of filter bags.

[0053] The replacement and scrapping decision unit is used to scientifically determine the timing of filter bag repair, partial replacement or overall scrapping based on the remaining life of the filter bag and the comprehensive deactivation index.

[0054] The group-level guideline adaptation unit is used to match and connect operation and maintenance decisions with the group-level filter bag life management guidelines, so as to achieve unified and standardized management of multiple units within the group.

[0055] Another embodiment of the present invention provides a diagnostic method for a bag filter fault diagnosis system in a coal-fired power plant, applied to the bag filter fault diagnosis system for coal-fired power plants described in the above embodiments. Figure 5 As shown, the diagnostic method for a bag filter fault diagnosis system in a coal-fired power plant provided in this embodiment includes steps 1 to 5. The following is a detailed explanation... Figure 5 Steps 1 through 5 will be explained in detail.

[0056] Step 1: Online acquisition and quantitative calculation of multi-source parameters: The filter bag performance, cleaning airflow and pulse valve operation data are collected simultaneously through the filter bag performance online sensing module, the dust removal device airflow status monitoring module and the pulse valve operation characteristic acquisition module. The comprehensive deactivation index of the filter bag, the uniformity coefficient of the blowing airflow and the pulse valve fault characteristic matching degree are quantitatively calculated respectively.

[0057] Specifically, after system startup, it enters real-time operation. The filter bag performance online sensing module collects data on pressure difference, emission concentration, flue gas conditions, and air permeability for a single compartment and a single row of filter bags through the filter bag differential pressure monitoring unit, dust concentration detection unit, flue gas condition acquisition unit, and filter bag air permeability online calculation unit. The filter bag comprehensive deactivation index calculation unit executes the filter bag comprehensive deactivation index calculation formula based on the collected data and outputs the filter bag comprehensive deactivation index. The dust removal device airflow status monitoring module collects data on jet pipe pressure acquisition unit, induced airflow velocity detection unit, and cage head airflow distribution monitoring unit to monitor jet pressure, induced airflow velocity, and cage head airflow distribution. The head airflow distribution data acquisition unit completes the statistical analysis of pressure standard deviation and average value, and the jet airflow uniformity calculation unit executes the jet airflow uniformity coefficient calculation formula and outputs the jet airflow uniformity coefficient. The pulse valve operation feature acquisition module completes the acquisition of stress wave signal, timing signal and diaphragm feature parameters through stress wave sensing unit, pulse timing acquisition unit and diaphragm action feature extraction unit. The pulse valve fault feature matching calculation unit executes the pulse valve fault feature matching degree calculation formula and outputs the pulse valve fault feature matching degree. The three types of raw data and the three major quantitative calculation results are uploaded to the system data processing unit in real time.

[0058] Step 2: Multi-source data preprocessing and fusion: The multi-source data fusion and feature extraction module performs noise reduction, normalization, spatiotemporal alignment and feature fusion on the original data and quantization calculation results to generate fused feature data and match it with the fault feature library.

[0059] Specifically, the multi-source data fusion and feature extraction module first performs noise reduction processing on the raw data and quantitative calculation results through the data preprocessing unit, eliminating outliers caused by operating condition fluctuations and electromagnetic interference, completing data normalization transformation, and eliminating the impact of dimensional differences on the diagnostic results. Then, the spatiotemporal feature alignment unit completes the spatial location and time sequence alignment of multi-source data according to the dust collector compartment number, filter bag number, and pulse jet timestamp, ensuring that data at the same location and time are interconnected and correspond one-to-one. Finally, the multi-source feature fusion unit performs feature-level fusion on filter bag performance, cleaning airflow, pulse valve characteristics, and quantitative indicators to generate a unified fault feature vector covering the entire system. The fault feature library construction unit then performs preliminary matching between the fused feature vector and the typical fault standard features stored in the library, such as filter bag damage, filter bag blockage, uneven pulse jet, pulse valve diaphragm failure, and timing disorder.

[0060] Step 3: Intelligent Fault Diagnosis and Lifespan Assessment: The intelligent fault diagnosis module for bag filters identifies the fault type and location based on fused feature data and quantitative calculation results, assesses the remaining lifespan of the filter bags, and generates early warning information.

[0061] Specifically, the intelligent fault diagnosis module for baghouse dust collectors uses a fault mode recognition unit to calculate the similarity between the fused feature vector and the fault feature database. Combining three quantitative indicators—the comprehensive deactivation index of the filter bag, the uniformity coefficient of the pulsed airflow, and the fault feature matching degree of the pulse valve—it accurately identifies the fault type, locates the compartment, serial number, and specific component where the fault occurs, and determines the severity level of the fault. The life assessment unit, based on the comprehensive deactivation index of the filter bag and combined with historical operating data and performance degradation patterns, quantitatively calculates the remaining service life of the filter bag. The fault early warning unit generates three levels of early warning information—immediate warning, time-limited handling, and observation—based on the severity and development trend of the fault. The diagnostic result output unit integrates the fault location, fault type, severity level, remaining filter bag life, and the three quantitative indicators to generate a standardized diagnostic report.

[0062] Step 4: Lifecycle Management and Operation and Maintenance Decisions: Establish a lifecycle file for the filter bag through the lifecycle management and operation and maintenance decision module, and generate operation and maintenance strategies and replacement and scrapping decision instructions.

[0063] Specifically, the full lifecycle management and operation and maintenance decision-making module synchronizes fault information, operating parameters, quantitative calculation results, and maintenance records to the electronic archive of the filter bag's full lifecycle through the lifecycle archive management unit, realizing full-process traceability management of the filter bag from commissioning to scrapping; the operation and maintenance strategy generation unit generates customized operation and maintenance plans for different fault types and quantitative indicators, including dust cleaning pressure optimization, jet pipe fitting modification, pulse valve maintenance and replacement, partial replacement of filter bags, system resistance control, etc.; the replacement and scrapping decision-making unit scientifically determines the best time for filter bag repair, partial replacement, or overall scrapping based on the remaining life of the filter bag and the comprehensive deactivation index; the group-level guideline adaptation unit matches and connects the operation and maintenance strategy with the group-level filter bag lifecycle management guidelines to ensure that the operation and maintenance decisions meet the group's standardized management requirements and realize unified and standardized operation and maintenance of multiple units within the group.

[0064] Step 5: Diagnostic Result Output and Iterative Optimization: Visualize the output of fault diagnosis, life assessment, quantitative calculation and operation and maintenance decision results, and update the fault feature library and diagnostic model simultaneously.

[0065] Specifically, the system outputs information such as fault diagnosis results, remaining filter bag life, three major quantitative calculation indicators, and operation and maintenance strategy suggestions through a visual human-machine interface. It supports functions such as historical data query, trend curve analysis, and fault record tracing. At the same time, it feeds back new fault data, diagnosis results, and quantitative calculation parameters to the fault feature library, continuously optimizes the fault feature matching model and quantitative calculation weight coefficient, improves the system's diagnostic accuracy and environmental adaptability, realizes the system's self-iterative improvement, and provides long-term stable service for the intelligent operation and maintenance of bag filters.

[0066] The following is a typical example of fault diagnosis implementation.

[0067] Example 1: Implementation of Filter Bag Damage Fault Diagnosis: The online filter bag performance sensing module monitors a decrease in the inlet and outlet pressure difference of a single row of filter bags, a significant increase in outlet dust concentration, and an abnormal increase in filter bag permeability. After calculating the comprehensive filter bag deactivation index, the deactivation index far exceeds the threshold. After multi-source data fusion, the fused feature vector matches the filter bag damage standard feature. The intelligent fault diagnosis module for bag filters determines that the row of filter bags has a damage fault, locates the specific compartment and row number, and generates an immediate warning. The full life cycle management and operation and maintenance decision module generates an operation and maintenance strategy for partial replacement of filter bags, and simultaneously updates the electronic file of the filter bag's full life cycle, completing the closed-loop fault handling.

[0068] Example 2: Implementation of Uneven Cleaning Pulse Testing Fault Diagnosis: The airflow status monitoring module of the cleaning device collected data showing significant differences in the outlet pressure of each nozzle of the blowpipe. After calculating the uniformity coefficient of the blowpipe airflow, the uniformity coefficient was found to be lower than the standard value. After multi-source data fusion, it was identified as an uneven cleaning pulse testing fault. The intelligent fault diagnosis module of the bag filter assessed the impact of the fault on the system resistance and filter bag life. The full life cycle management and operation and maintenance decision module generated operation and maintenance suggestions for optimizing the blowpipe orifice diameter and modifying the induced airflow pipe fittings, guiding the on-site completion of cleaning system optimization, improving the uniformity of the pulse testing, and reducing the system operating resistance.

[0069] Example 3: Implementation of Pulse Valve Diaphragm Fault Diagnosis: The stress wave signal characteristics collected by the pulse valve operation characteristic acquisition module differ significantly from the standard signal. After calculating the pulse valve fault characteristic matching degree, the matching degree is far below the normal threshold. The intelligent fault diagnosis module of the bag filter determines that the corresponding pulse valve has a diaphragm damage fault and locates the faulty pulse valve. The full life cycle management and operation and maintenance decision module generates pulse valve maintenance and replacement instructions to avoid chain failures such as filter bag blockage and increased system resistance caused by dust removal failure, and ensures stable operation of the dust collector.

[0070] Those skilled in the art will understand that the above embodiments are specific implementations of the present invention, and in practical applications, various changes in form and detail may be made without departing from the spirit and scope of the present invention.

Claims

1. A fault diagnosis system for baghouse dust collectors in coal-fired power plants, characterized in that, It includes a filter bag performance online sensing module, a dust removal device airflow status monitoring module, a pulse valve operation feature acquisition module, a multi-source data fusion and feature extraction module, a bag filter fault intelligent diagnosis module, and a full life cycle management and operation and maintenance decision module; The filter bag performance online sensing module is used to collect in real time the operating condition parameters, filter bag performance parameters and dust emission parameters of a single compartment and a single row of filter bags in a baghouse dust collector of a coal-fired power plant, calculate the air permeability of the filter bag online and output the raw sensing data. The airflow state monitoring module of the dust removal device is used to collect the blowing pressure, induced airflow velocity and airflow distribution parameters of the bag filter dust removal device, statistically analyze the uniformity characteristics of the blowing air distribution and output airflow state data. The pulse valve operation feature acquisition module is used to acquire the stress wave signal and the injection timing signal generated by the pulse valve action, extract the pulse valve diaphragm action features and output the pulse valve operation feature data. The multi-source data fusion and feature extraction module is used to preprocess, spatiotemporally align and fuse the raw sensing data, airflow state data and pulse valve operation feature data, construct a standardized fault feature library and output fused feature data. The intelligent fault diagnosis module for bag filters is used to perform pattern matching based on fused feature data and fault feature library, identify bag filter fault types, assess the remaining life of filter bags, and output fault diagnosis results and early warning information. The full life cycle management and operation and maintenance decision module is used to establish a full life cycle file of filter bags based on fault diagnosis results, generate targeted operation and maintenance strategies, and output decision instructions.

2. The fault diagnosis system for baghouse dust collectors in coal-fired power plants according to claim 1, characterized in that, The filter bag performance online sensing module includes a filter bag differential pressure monitoring unit, a dust concentration detection unit, a flue gas condition acquisition unit, a filter bag air permeability online calculation unit, and a filter bag comprehensive deactivation index calculation unit. The filter bag differential pressure monitoring unit is used to collect the inlet and outlet differential pressure data of a single row of filter bags; The dust concentration detection unit is used to collect dust emission concentration data at the filter bag outlet; The flue gas condition acquisition unit is used to collect flue gas temperature and humidity data inside the dust collector; The filter bag air permeability online calculation unit is used to calculate the real-time air permeability of the filter bag based on pressure difference, flue gas flow rate and filter bag area parameters. The filter bag comprehensive deactivation index calculation unit is used to calculate the filter bag comprehensive deactivation index by normalizing the filter bag's real-time operating pressure difference, real-time emission concentration, and real-time air permeability with the corresponding benchmark values ​​and then weighting and summing them. And / or, the filter bag comprehensive deactivation index calculation unit is used to calculate the filter bag comprehensive deactivation index according to the following formula: ； in, The filter bag deactivation index is dimensionless. , , These are the weighting coefficients for operating pressure difference, emission concentration, and permeability, respectively, to meet the following requirements. ; Real-time operating pressure differential of the filter bag; The rated design operating differential pressure for the filter bag; Real-time dust emission concentration at the filter bag outlet; The emission concentration limits for particulate matter from coal-fired power units; Real-time air permeability of the filter bag; This refers to the initial air permeability of the filter bag before it leaves the factory.

3. The fault diagnosis system for baghouse dust collectors in coal-fired power plants according to claim 1, characterized in that, The airflow status monitoring module of the dust removal device includes a jet pipe pressure acquisition unit, an induced airflow velocity detection unit, a bag cage head airflow distribution monitoring unit, a jet gas distribution characteristic statistics unit, and a jet airflow uniformity calculation unit. The blowpipe pressure acquisition unit is used to collect the outlet pressure data of each nozzle of the dust removal blowpipe. The induced airflow velocity detection unit is used to collect the velocity data of the primary and secondary induced airflows of the dust removal device. The airflow distribution monitoring unit at the filter bag cage head is used to collect airflow velocity distribution data in the filter bag cage head area; The jet gas distribution characteristic statistical unit is used to statistically analyze the standard deviation and arithmetic mean of the airflow pressure at the outlet of each nozzle of the dust removal jet pipe. The uniformity calculation unit for the jetting airflow is used to calculate the uniformity coefficient of the jetting airflow based on the ratio of the standard deviation to the arithmetic mean of the airflow pressure at the outlet of each nozzle of the cleaning jetting pipe.

4. The fault diagnosis system for baghouse dust collectors in coal-fired power plants according to claim 3, characterized in that, The jet airflow uniformity calculation unit is used to calculate the jet airflow uniformity coefficient according to the following formula: ； in, The uniformity coefficient of the jet airflow is dimensionless. The standard deviation of the airflow pressure at the outlet of each nozzle of the dust removal blowpipe; This is the arithmetic mean of the airflow pressure at the outlet of each nozzle of the dust removal blowpipe.

5. The fault diagnosis system for baghouse dust collectors in coal-fired power plants according to claim 1, characterized in that, The pulse valve operation feature acquisition module includes a stress wave sensing unit, a pulse timing acquisition unit, a diaphragm action feature extraction unit, a compressed air main pipe status monitoring unit, and a pulse valve fault feature matching calculation unit. The stress wave sensing unit is used to collect stress wave signals generated by the action of the pulse valve on the compressed air main pipe. The pulse timing acquisition unit is used to synchronously acquire the pulse jet timing signal of the dust removal system; The diaphragm motion feature extraction unit is used to extract the feature parameters corresponding to the opening and closing of the pulse valve diaphragm from the stress wave signal. The compressed air main pipe status monitoring unit is used to collect main pipe pressure and leakage status parameters; The pulse valve fault feature matching calculation unit is used to calculate the pulse valve fault feature matching degree based on the cosine similarity between the pulse valve stress wave signal feature value collected in real time and the normal operation standard stress wave signal feature value.

6. The fault diagnosis system for baghouse dust collectors in coal-fired power plants according to claim 5, characterized in that, The pulse valve fault feature matching calculation unit is used to calculate the pulse valve fault feature matching degree according to the following formula: ； in, The fault characteristic matching degree of the pulse valve is dimensionless; For the real-time acquisition of pulse valve stress wave signal One eigenvalue; The standard stress wave signal for normal operation of the pulse valve One eigenvalue; This is the sequence number of the stress wave characteristic value.

7. The fault diagnosis system for baghouse dust collectors in coal-fired power plants according to claim 1, characterized in that, The multi-source data fusion and feature extraction module includes a data preprocessing unit, a spatiotemporal feature alignment unit, a multi-source feature fusion unit, and a fault feature database construction unit; The data preprocessing unit is used to perform noise reduction, normalization and outlier removal on multi-source data. The spatiotemporal feature alignment unit is used to complete the spatial and temporal alignment of multi-source data according to the dust collector compartment, number, and time sequence dimensions. The multi-source feature fusion unit is used to perform feature-level fusion on the aligned data to generate a unified fault feature vector; The fault feature library construction unit is used to store standard feature data of filter bag deactivation, uneven blowing, and pulse valve failure.

8. The fault diagnosis system for baghouse dust collectors in coal-fired power plants according to claim 1, characterized in that, The intelligent fault diagnosis module for baghouse dust collectors includes a fault mode recognition unit, a lifespan assessment unit, a fault early warning unit, and a diagnostic result output unit. The fault mode identification unit is used to match the fused feature vector with the fault feature library, and combine the filter bag comprehensive deactivation index, the uniformity coefficient of the blowing airflow, and the fault feature matching degree of the pulse valve to determine the fault type and fault location. The life assessment unit is used to calculate the remaining service life of the filter bag based on the filter bag comprehensive deactivation index; The fault early warning unit is used to generate graded early warning information according to the fault level; The diagnostic result output unit is used to visualize and output the fault location, type, level, and life assessment results.

9. The fault diagnosis system for baghouse dust collectors in coal-fired power plants according to claim 1, characterized in that, The full life cycle management and operation and maintenance decision-making module includes a life cycle file management unit, an operation and maintenance strategy generation unit, a replacement and scrapping decision-making unit, and a group-level guideline adaptation unit. The life cycle file management unit is used to record the entire life cycle data of filter bag selection, testing, operation, maintenance and replacement; The operation and maintenance strategy generation unit is used to generate operation and maintenance plans for dust removal parameter optimization, on-site inspection and maintenance, and component replacement based on the fault diagnosis results. The replacement and scrapping decision unit is used to determine the timing of filter bag replacement or scrapping based on the remaining lifespan of the filter bag. The group-level guideline adaptation unit is used to match and adapt operation and maintenance decisions with the group-level filter bag life management guidelines.

10. A diagnostic method for a bag filter fault diagnosis system in a coal-fired power plant, characterized in that, The fault diagnosis system for baghouse dust collectors in coal-fired power plants according to any one of claims 1 to 9, the diagnosis method includes the following steps: Step 1: Online acquisition of multi-source parameters. The filter bag performance, cleaning airflow and pulse valve operation data are collected simultaneously through the filter bag performance online sensing module, the dust removal device airflow status monitoring module and the pulse valve operation characteristic acquisition module. The comprehensive deactivation index of the filter bag, the uniformity coefficient of the blowing airflow and the pulse valve fault characteristic matching degree are quantitatively calculated respectively. Step 2: Multi-source data preprocessing and fusion. The multi-source data fusion and feature extraction module performs noise reduction, normalization, spatiotemporal alignment and feature fusion on the original data and quantization calculation results to generate fused feature data and match it with the fault feature library. Step 3: Intelligent fault diagnosis and life assessment. The intelligent fault diagnosis module for bag filters identifies the fault type and location based on fused feature data and quantitative calculation results, assesses the remaining life of the filter bags, and generates early warning information. Step 4: Full life cycle management and operation and maintenance decision-making. The full life cycle management and operation and maintenance decision-making module establishes a full life cycle file for the filter bag and generates operation and maintenance strategies and replacement and scrapping decision instructions. Step 5: Output and iterative optimization of diagnostic results. Visualize the results of fault diagnosis, life assessment, quantitative calculation and operation and maintenance decision-making, and update the fault feature library and diagnostic model simultaneously.

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