A method and apparatus for defect detection of membrane stack disc filter elements

Abstract

This invention discloses a method and apparatus for defect detection of membrane stack disc filter elements, belonging to the field of filter element defect detection technology. The method includes: sampling membrane stack disc filter elements produced in the same batch to obtain sample test filter elements; extracting standard filter element characteristics from standard design information; performing filtration interference analysis based on filter element electromagnetic characteristic data to obtain filter element-related defects; conducting material quality testing on the sample test filter elements to obtain material-related defects and quality-related defects; integrating and analyzing material-related defects, quality-related defects, and filter element-related defects to obtain batch defect evaluation results; and optimizing the production process of the membrane stack disc filter element. This invention solves the technical problem of existing filter element defect detection methods, which struggle to efficiently and accurately detect internal defects in complex multi-layer membrane stack disc filter elements, leading to insufficient filter element quality and reliability. It achieves the technical effect of improving the detection accuracy, quality, and reliability of membrane stack disc filter elements.

Description

Technical Field

[0001] This application relates to the technical field of filter element defect detection, specifically to a method and apparatus for detecting defects in a membrane stack disc filter element. Background Technology

[0002] In modern manufacturing, especially in the production of precision filtration equipment, membrane stack disc filter cartridges are widely used in various fields such as water treatment, chemical, pharmaceutical, food and beverage industries due to their superior filtration performance. A membrane stack disc filter cartridge typically refers to a component used for liquid filtration, consisting of multiple circular filter discs stacked together to form a so-called "membrane stack." Each disc has a specific pore size that can trap particles of a certain size, thereby achieving filtration and purification. This filter cartridge design allows fluid to flow through the channels between the discs, while impurities are trapped on the disc surface or in the pores. During production and use, these filter cartridges often require high stability and reliability to ensure the quality and safety of the final product. However, due to the complexity of the manufacturing process and the characteristics of the materials themselves, various defects may occur in the production of membrane stack disc filter elements. These defects may seriously affect the performance of the filter elements and even lead to product failure. Due to the complexity and diversity of the production process, it is difficult to completely guarantee the consistency of products between batches. This makes defect detection even more important. How to effectively detect and control defects in membrane stack disc filter elements has become an important research topic. Traditional detection methods often rely on manual visual inspection or simple physical tests, which are not only inefficient but also difficult to fully cover all potential defect types, especially the identification of latent defects such as electromagnetic properties.

[0003] Therefore, current technologies for defect detection in membrane stack disc filter elements face the challenge of efficiently and accurately detecting internal defects in complex multi-layered membrane stack disc filter elements, leading to insufficient filter element quality and reliability. Summary of the Invention

[0004] This application provides a defect detection method and apparatus for membrane stack disc filter elements, which solves the technical problem that existing filter element defect detection methods are unable to efficiently and accurately detect internal defects in complex multi-layer membrane stack disc filter elements, resulting in insufficient filter element quality and reliability. This achieves the technical effect of improving the detection accuracy, quality and reliability of membrane stack disc filter elements.

[0005] This application provides a defect detection method for membrane stack disc filter elements. The method includes: sampling membrane stack disc filter elements produced in the same batch to obtain sample test filter elements; interactively obtaining standard design information of the sample test filter elements and extracting standard filter element characteristics from the standard design information; running a pulsed eddy current detection device to scan the sample test filter elements to obtain filter element electromagnetic characteristic data, and performing filtration interference analysis based on the filter element electromagnetic characteristic data to obtain filter element-related defects; performing material quality testing on the sample test filter elements according to the standard filter element characteristics to obtain material-related defects and quality-related defects; integrating and analyzing the material-related defects, quality-related defects, and filter element-related defects to obtain batch defect evaluation results; and optimizing the production process of the membrane stack disc filter elements based on the batch defect evaluation results.

[0006] In a possible implementation, the sample test filter element is subjected to material quality testing based on the standard filter element characteristics to obtain material-related defects and quality-related defects. The following processing is also performed: standard filter element material and standard filter element structure are retrieved from the standard filter element characteristics; material characteristic testing is performed on the sample test filter element based on the standard filter element material and standard filter element structure to generate the material-related defects, wherein the material-related defects include a material defect identifier and a material defect quantification coefficient; quality control testing is performed on the sample test filter element based on the standard filter element structure to generate the quality-related defects, wherein the quality-related defects include a quality defect identifier and a quality defect quantification coefficient.

[0007] In a possible implementation, the sample testing filter element is subjected to material property testing based on the standard filter element material and standard filter element structure to generate the material-related defects. The following processes are then performed: geometric modeling is performed based on the standard filter element structure to reconstruct the testing filter element model; material property analysis is performed based on the standard filter element material, and the analysis results are assigned to the testing filter element model to obtain the standard filter element model; the standard operating conditions of the sample testing filter element are interactively obtained, and a running simulation is performed after applying a working load to the standard filter element model based on the standard operating conditions to locate the key stress node set, wherein the key stress node set includes a stress node array and a stress parameter array; during the operation of the sample testing filter element under the standard operating conditions with a working load applied, the key stress node set is used to traverse the sample testing filter element to perform material property testing, locating multiple stress defect nodes and multiple stress deviation percentages; the multiple stress defect nodes constitute the material defect identifier, and the multiple stress deviation percentages constitute the material defect quantification coefficient.

[0008] In a possible implementation, the sample testing filter element is subjected to quality control testing according to the standard filter element structure to generate the quality-related defects. The following processing is performed: interactively obtaining the model information of the sample testing filter element, and matching and calling the sealing test equipment according to the model information; interactively obtaining the filter element design pressure of the sample testing filter element, and setting multi-level flow pressure parameters with 1.2 times the filter element design pressure as the pressure variation range; after installing the sample testing filter element into the sealing test equipment, applying a working load to the sample testing filter element using the multi-level flow pressure parameters to locate multiple airtightness defect nodes and multiple airtightness pressure resistance parameters; the multiple airtightness defect nodes constitute the quality defect identifier, and the multiple airtightness pressure resistance parameters constitute the quality defect quantification coefficient.

[0009] In a possible implementation, a pulsed eddy current testing device is used to scan the sample filter element to obtain electromagnetic characteristic data of the filter element. Based on the electromagnetic characteristic data of the filter element, a filter interference analysis is performed to obtain filter element-related defects. The following processes are also performed: the pulsed eddy current testing device is invoked, and the frequency of the Hall sensor in the pulsed eddy current testing device is set according to the characteristics of the standard filter element to obtain a local testing device; the local testing device is run to scan the sample filter element to obtain the electromagnetic characteristic data of the filter element; defects in multiple deep membrane stack filter elements in the sample filter element are identified and located based on the electromagnetic characteristic data of the filter element to obtain a filter element defect array; a filter interference analysis is performed based on the filter element defect array to obtain the filter element-related defects, wherein the filter element-related defects include filter element defect identifiers and filter element defect quantification coefficients.

[0010] In a possible implementation, the integrated analysis of material-related defects, quality-related defects, and filter element-related defects yields batch defect evaluation results. The following processing is also performed: spatial integration of the material defect identifiers, quality defect identifiers, and filter element defect identifiers to obtain a filter element defect space; pre-configuration of material defect weights, quality defect weights, and filter element defect weights; and using the pre-configured material defect weights, quality defect weights, and filter element defect weights to perform defect risk summation processing on the material defect quantification coefficient, quality defect quantification coefficient, and filter element defect quantification coefficient to obtain a defect risk coefficient. The filter element defect space and the defect risk coefficient constitute the batch defect evaluation results.

[0011] In a possible implementation, defects in multiple deep membrane stack filters in the sample test filter are identified and located based on the filter electromagnetic property data to obtain a filter defect array. The following processing is also performed: multi-layer penetration data of the multiple deep membrane stack filters is obtained by splitting the filter electromagnetic property data; three-dimensional image reconstruction of the filter is performed based on the multi-layer penetration data to obtain a multi-layer filter image; a filter layer defect identification model is pre-constructed, and defect location is performed by synchronizing the multi-layer filter image to the filter layer defect identification model to obtain multi-layer filter defect information; wherein, the defect information of each filter layer includes multiple filter layer defect locations, multiple filter layer defect types, and multiple filter layer defect sizes; the coordinate vector of the multi-layer filter defect information is normalized using the standard filter model as a reference to obtain the filter defect array.

[0012] In a possible implementation, filtration interference analysis is performed based on the filter element defect array to obtain the filter element associated defects. These associated defects include filter element defect identifiers and filter element defect quantification coefficients. The following processing is also performed: interactively obtaining historical filtration records of the sample test filter element; performing filter media retention depth analysis based on the historical filtration records to obtain multi-layer retention thickness distributions; performing complement solving based on the multi-layer retention thickness distributions to determine non-filtration interference regions; traversing the filter element defect array using the non-filtration interference regions to locate K filter element defect nodes; mapping and retrieving K filter layer defect types and K filter layer defect sizes from the filter element defect array; retrieving M standard defect sizes for M filter layer defect types; retrieving data from the M standard defect sizes based on the K filter layer defect types, and combining the data retrieval results with the K filter layer defect size mappings to perform Euclidean distance calculations to determine the K defect size deviations; the K filter element defect nodes constitute the filter element defect identifiers, and the K defect size deviations constitute the filter element defect quantification coefficients.

[0013] This application also provides a defect detection device for membrane stack disc filter elements, comprising: a sample filter element acquisition module for sampling membrane stack disc filter elements produced in the same batch to obtain sample filter elements; a standard filter element characteristic acquisition module for interactively obtaining standard design information of the sample filter elements and extracting standard filter element characteristics from the standard design information; a filter element associated defect acquisition module for running a pulsed eddy current detection device to scan the sample filter elements, obtaining filter element electromagnetic characteristic data, and performing filtration interference analysis based on the filter element electromagnetic characteristic data to obtain filter element associated defects; a material quality inspection module for performing material quality inspection on the sample filter elements according to the standard filter element characteristics to obtain material associated defects and quality associated defects; a batch defect evaluation result acquisition module for integrating and analyzing the material associated defects, quality associated defects, and filter element associated defects to obtain batch defect evaluation results; and a membrane stack disc filter element optimization module for optimizing the production process of the membrane stack disc filter elements based on the batch defect evaluation results.

[0014] This application proposes a defect detection method and apparatus for membrane stack disc filter elements. The method involves sampling membrane stack disc filter elements produced in the same batch to obtain sample test filter elements; extracting standard filter element characteristics from standard design information; performing filtration interference analysis based on filter element electromagnetic characteristic data to obtain filter element-related defects; conducting material quality testing on the sample test filter elements to obtain material-related defects and quality-related defects; integrating and analyzing material-related defects, quality-related defects, and filter element-related defects to obtain batch defect evaluation results; and optimizing the production process of the membrane stack disc filter element. This method solves the technical problem of existing filter element defect detection methods, which struggle to efficiently and accurately detect internal defects in complex multi-layer membrane stack disc filter elements, leading to insufficient filter element quality and reliability. It achieves the technical effect of improving the detection accuracy, quality, and reliability of membrane stack disc filter elements. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the apparatus according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0016] Figure 1 A schematic flowchart of a defect detection method for a membrane stack disc filter element provided in an embodiment of this application;

[0017] Figure 2 This is a schematic diagram of a defect detection device for a membrane stack disc filter element provided in an embodiment of this application.

[0018] Figure labeling: Sample test filter element acquisition module 10, standard filter element characteristic acquisition module 20, filter element associated defect acquisition module 30, material quality testing module 40, batch defect evaluation result acquisition module 50, membrane stack disc filter element optimization module 60. Detailed Implementation

[0019] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application.

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or apparatuses. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0022] This application provides a defect detection method for membrane stack disc filter elements, such as... Figure 1 As shown, the method includes:

[0023] Step S100: Sampling is performed on the membrane stack disc filter elements produced in the same batch to obtain sample test filter elements. From the membrane stack disc filter elements produced in the same batch, a certain number of filter elements are randomly or systematically selected according to certain standards or methods. These selected filter elements are called sample test filter elements. A batch refers to a group of membrane stack disc filter elements produced under the same production conditions and within a certain time period. For example, random sampling is used to select several filter elements as samples. These samples should be representative and able to reflect the average quality and potential problems of the entire batch of filter elements.

[0024] Step S200: Interact with the standard design information of the sample test filter element to obtain standard filter element characteristics, and extract standard filter element characteristics from the standard design information. During the testing process, interact with the product database or design documents to obtain the design standards and specifications of the batch of filter elements. Then, extract specific characteristic parameters from these standard design information as standard filter element characteristics. Specifically, perform a search and match in the product database or design documents, such as inputting the batch number, product model, or other identification information of the sample to locate and obtain the corresponding design standards. Retrieve the standard design information related to the current sample test filter element, such as the filter element's design drawings, technical specifications, material requirements, performance indicators, etc., which may include the filter element's geometric dimensions (diameter, thickness, pore size, etc.), number of layers, material type, etc., the filter element's pressure resistance, air permeability, filtration accuracy, electromagnetic properties, etc., and the purity, density, tensile strength, etc., of the material, which are directly related to the quality of the filter element. From the obtained standard design information, extract key characteristic parameters, such as physical dimensions, tolerance range, material properties, electromagnetic response characteristics, etc., as standard values ​​used for comparison with actual measurement data in subsequent testing processes.

[0025] Step S300: The pulsed eddy current testing device is run to scan the sample filter element to obtain electromagnetic characteristic data of the filter element. Based on this data, interference analysis is performed to identify associated defects. During the testing process, the pulsed eddy current testing device scans the sample filter element to collect its electromagnetic characteristic data. Then, through data processing and analysis, interference factors are eliminated, and effective information related to filter element defects is identified, thereby detecting potential defects in the filter element. Specifically, the pulsed eddy current testing device is set and calibrated to suit specific filter element types and testing requirements. The device generates pulsed currents to induce eddy currents within the filter element material. These eddy currents are affected by material properties (such as conductivity and permeability) and geometry (such as cracks, pores, and delamination). The sample filter element is placed in the testing area, and the device captures its electromagnetic response data by scanning the surface and interior of the filter element. This is typically manifested as changes in eddy current signal intensity and phase at different locations. These data reflect the conductivity of the filter element's interior and surface. The electromagnetic characteristics of a filter element are analyzed by identifying changes in its properties, the location and nature of defects, and their characteristics. For example, uniform parts of a material will generate consistent eddy current signals, while defective parts will cause abnormal changes in the signals. Then, signal processing techniques, such as signal filtering, pattern recognition, or data fitting, are used to remove other interference signals from the electromagnetic characteristics data, such as equipment noise, environmental electromagnetic interference, or natural changes in the material. Techniques such as frequency analysis, amplitude correction, and noise suppression are employed to ensure that the final data reflects the true physical defects. The electromagnetic characteristics data after interference filtering can more clearly show abnormal signals related to defects inside or on the surface of the filter element, indicating the specific defect type, location, and severity. The identified defects are classified, such as cracks, holes, material delamination, or inhomogeneity, and their specific locations and properties are recorded, thus obtaining the filter element-related defects.

[0026] Step S400: The sample filter element is subjected to material quality testing based on the standard filter element characteristics to obtain material-related defects and quality-related defects. During the filter element defect detection process, the standard filter element characteristics extracted from the standard design information are used as a reference to conduct detailed testing and analysis of the material and overall quality of the sample filter element. This identifies defects related to the material itself and its processing quality. Specifically, various testing equipment and methods (such as spectral analysis, X-ray detection, ultrasonic testing, etc.) are used to measure the physical properties of the sample filter element material (such as density, hardness, microstructure, etc.). These measured values ​​are compared with the standard filter element characteristics. Chemical analysis techniques (such as spectrometers, mass spectrometers, etc.) are used to detect the chemical composition of the filter element material to ensure it meets standard composition requirements. The mechanical properties of the material, such as tensile strength, compressive strength, and elastic modulus, are evaluated to ensure... The material must be able to withstand the expected stress and environmental conditions in practical applications. If the physical, chemical, or mechanical properties of the material are found to be inconsistent with the standard characteristics during the testing process, it is considered to have a material-related defect, which may include impurities in material composition, insufficient mechanical strength, uneven density, insufficient corrosion resistance, etc. In addition to the material itself, defects that may be introduced during the manufacturing process (such as processing errors, interlayer separation, and substandard surface roughness) will also affect the quality of the filter element. Quality-related defects are identified by testing these parameters. For example, the actual manufacturing quality of the sample filter element is compared and analyzed with the characteristics of the standard filter element. If there is a significant deviation (such as non-compliant dimensions, abnormal porosity, uneven lamination, etc.), it is judged to be a quality-related defect.

[0027] In one possible implementation, step S400 further includes step S410, obtaining standard filter material and standard filter structure from the standard filter characteristics; step S420, performing material characteristic testing on the sample test filter according to the standard filter material and standard filter structure to generate the material-related defect, wherein the material-related defect includes a material defect identifier and a material defect quantification coefficient; step S430, performing quality control testing on the sample test filter according to the standard filter structure to generate the quality-related defect, wherein the quality-related defect includes a quality defect identifier and a quality defect quantification coefficient.

[0028] Preferably, the standard filter element material is detailed information extracted from the characteristics of standard filter elements, including the material's composition, physical properties (such as density, hardness, conductivity, etc.), chemical properties (such as corrosion resistance, oxidation resistance, etc.), and mechanical properties (such as tensile strength, elastic modulus, etc.). The standard filter element structure refers to the geometric and physical structure of the filter element, including its size, shape, number of layers, pore size, arrangement, and sealing method. These structural characteristics determine the overall performance of the filter element, such as filtration efficiency and flow resistance. By analyzing the material characteristics of the filter element sample (e.g., through microscopic analysis, spectral analysis, chemical composition detection, etc.), potential defects in the material can be identified, which may include inconsistent material composition, excessive impurity content, and microstructural defects (such as pores, cracks, etc.). The mechanical properties of the filter element material are tested to evaluate whether its tensile strength, compressive strength, elasticity, etc., meet the standard requirements, thereby generating material-related defects, including material defect identifiers and material defect quantification coefficients. Among them, the material defect identifier is a descriptive mark for the material defects found in the test, used to identify the specific defect type and location. For example, the identifier may indicate the filter element material... The material has cracks in a certain area, or the purity of the material is lower than the standard; the material defect quantification coefficient is used to quantify the severity or impact of material defects, indicating the severity of the defect, which may involve the size, quantity, or range of impact of the defect. A higher quantification coefficient indicates a more serious defect; the actual size of the sample filter element is measured and compared with the structure of the standard filter element to determine whether there are deviations. Size deviations may include the diameter, thickness, pore size, etc. of the filter element not conforming to the design values. The sealing performance of the filter element is evaluated to ensure that it can effectively prevent leakage during use. This may include checking the end cap seal, joint seal, etc. of the filter element to ensure its sealing performance under working pressure, thereby generating quality-related defects, including quality defect identification and quality defect quantification coefficient. Quality defect identification is a descriptive mark of defects found in quality inspection, used to identify specific quality problems, such as size deviations exceeding the tolerance range, or insufficient sealing performance, etc. The quality defect quantification coefficient is used to quantify the severity of quality defects, similar to the material defect quantification coefficient, indicating the potential impact of quality defects on the overall performance of the filter element. A higher coefficient means a more serious quality problem.

[0029] In one possible implementation, step S420 further includes step S421, performing geometric modeling based on the standard filter element structure to reconstruct the test filter element model; step S422, performing material property analysis based on the standard filter element material and assigning the analysis results to the test filter element model to obtain the standard filter element model; step S423, interactively obtaining the standard operating conditions of the sample test filter element, and performing a running simulation after applying a working load to the standard filter element model based on the standard operating conditions to locate the key stress node set, wherein the key stress node set includes a stress node array and a stress parameter array; step S424, during the operation of applying a working load to the sample test filter element according to the standard operating conditions, using the key stress node set to traverse the sample test filter element to perform material characteristic detection, locating multiple stress defect nodes and multiple stress deviation percentages; step S425, the multiple stress defect nodes constitute the material defect identifier, and the multiple stress deviation percentages constitute the material defect quantification coefficient.

[0030] Preferably, based on the structural data of a standard filter element (such as size, shape, pore size arrangement, number of layers, etc.), a three-dimensional filter element model is created in computer-aided design (CAD) software, generating a virtual model with the same structure as the actual sample test filter element. According to the standard material information of the filter element, the physical and mechanical properties of the material, such as elastic modulus, yield strength, density, coefficient of thermal expansion, etc., are analyzed and determined. These material properties are assigned to the geometric model so that it is consistent with the actual filter element not only in shape but also in material properties, forming a complete standard filter element model for simulating the operation under actual working conditions. Standard operating conditions refer to the typical operating conditions of the filter element in actual use, such as working pressure, temperature, flow rate, and characteristics of the filter medium. Then, corresponding working loads are applied to the standard filter element model, including the simulation of physical conditions such as pressure, temperature, and fluid flow. Through simulation, the key stress areas (nodes) of the filter element under these working conditions are identified. These areas are locations of stress concentration, significant deformation, or susceptibility to failure. They include stress node arrays and stress parameter arrays. The stress node array is a set of critical locations representing points or areas where the filter element experiences significant stress during operation. The stress parameter array describes the specific values ​​and characteristics of the stresses experienced at these stress nodes, such as stress magnitude, direction, and frequency. The sample filter element is placed under the same working load conditions as the standard filter element model to simulate its actual operating state. Detailed material property testing is performed on the previously identified critical stress nodes, including microstructure analysis, mechanical property testing, and stress testing, to examine these... The material state of nodes during actual operation is analyzed to identify nodes with defects in stress concentration areas, such as microcracks, local deformation, and material degradation. The deviation between the stress values ​​of these defective nodes and the standard stress values ​​is measured and expressed as a percentage, reflecting the degree of deviation of the material's performance under actual working conditions. Ultimately, the stress-defective nodes constitute the material defect identifier, which is a specific description and location of the defects found during the detection process. The percentage of stress deviation constitutes the quantitative coefficient of the material defect. These coefficients represent the severity of the defect in numerical form. A higher percentage of deviation usually means a more serious material problem or potential failure risk.

[0031] In one possible implementation, step S430 further includes step S431, interactively obtaining the model information of the sample testing filter element, and matching and calling the sealing test equipment according to the model information; step S432, interactively obtaining the filter element design pressure of the sample testing filter element, and setting multi-level flow pressure parameters with 1.2 times the filter element design pressure as the pressure variation range; step S433, after installing the sample testing filter element into the sealing test equipment, applying a working load to the sample testing filter element using the multi-level flow pressure parameters to locate multiple airtightness defect nodes and multiple airtightness pressure resistance parameters; step S434, the multiple airtightness defect nodes constitute the quality defect identifier, and the multiple airtightness pressure resistance parameters constitute the quality defect quantification coefficient.

[0032] Preferably, the specific model information of the sample testing filter element is obtained, including the product specifications, series, size, and material. Based on the obtained model information, the most suitable sealing performance testing equipment is automatically selected or suggested. The design pressure of the sample testing filter element is obtained, which is the pressure the filter element should withstand under normal operating conditions. The test pressure range is set to 1.2 times the design pressure. 1.2 times the design pressure is usually used to test the safety factor of the filter element, ensuring that it can maintain its performance even when the conditions are slightly higher than the design conditions. Within this pressure range, different levels of fluid pressure are set for testing to test the pressure resistance and airtightness of the filter element. The sample testing filter element is correctly installed into the matching sealing performance testing equipment, ensuring that the equipment can simulate actual working conditions and apply pressure to the filter element according to the preset multi-stage flow... The pressure parameter involves gradually applying fluid pressure to the filter element. The sealing test equipment monitors the filter element's airtightness under different pressure conditions, locating multiple airtightness defect nodes and airtightness pressure resistance parameters. These parameters identify areas that show insufficient airtightness or leaks under specific pressures. The airtightness pressure resistance parameters represent the filter element's airtightness performance under different pressures, including the pressure resistance performance of each test node when a specific pressure is applied. Based on the airtightness defect nodes detected during the airtightness test, a quality defect identifier is generated, recording the specific location and nature of the defect. For example, if a leak occurs at a seam of the filter element under 1.2 times the design pressure, a defect quantification coefficient is generated based on the airtightness pressure resistance parameters. This quantifies the severity of each defect, indicating the potential impact of the defect on the filter element's performance under actual operating conditions.

[0033] In one possible implementation, step S430 further includes step S435, invoking a pulsed eddy current detection device and setting the frequency of the Hall sensor in the pulsed eddy current detection device according to the characteristics of the standard filter element to obtain a local detection device; step S436, running the local detection device to scan the sample test filter element to obtain the electromagnetic characteristic data of the filter element; step S437, performing defect identification and location on multiple deep membrane stack filter elements in the sample test filter element according to the electromagnetic characteristic data of the filter element to obtain a filter element defect array; step S438, performing filtration interference analysis based on the filter element defect array to obtain the filter element associated defects, wherein the filter element associated defects include filter element defect identifiers and filter element defect quantification coefficients.

[0034] Preferably, pulsed eddy current testing is a non-destructive testing technique that uses pulsed current to generate eddy currents in conductive materials to detect defects. It typically employs multiple sensors, including a Hall sensor to detect changes in the electromagnetic field. Based on the characteristics of a standard filter element, the operating frequency of the Hall sensor is set to ensure its sensitivity in detecting potential defects within the filter element. The correct frequency setting directly affects the sensitivity and accuracy of the test. By correctly setting the Hall sensor frequency, the pulsed eddy current testing equipment (local testing equipment) can adapt to the current testing requirements, thereby accurately capturing the electromagnetic characteristics data of the filter element. The sample filter element is placed in the local testing equipment, and the entire structure of the filter element, especially its deep internal layers, is scanned. During the scanning process, the Hall sensor records the electromagnetic characteristics of the filter element, reflecting its internal electromagnetic response. Any abnormal electromagnetic signal may indicate a defect in the filter element. Based on the electromagnetic characteristic data, pulsed eddy current detection can identify defects in the deep structure, such as cracks, delamination, and bubbles. By analyzing the electromagnetic characteristic data, the specific location of the defects is determined, and these location data are formed into a filter element defect array, containing the location coordinates and type information of all detected defects. Finally, based on the filter element defect array, filtration interference analysis is performed, that is, irrelevant electromagnetic signals such as equipment noise and environmental interference are removed, leaving signals related to the actual defects, to obtain the filter element associated defects, including filter element defect identification and filter element defect quantification coefficient.

[0035] In one possible implementation, step S437 further includes step S4371, obtaining multi-layer penetration data of the plurality of deep membrane stack filter elements by splitting the electromagnetic characteristic data of the filter element; step S4372, performing three-dimensional image reconstruction of the filter element based on the multi-layer penetration data to obtain a multi-layer filter element image; step S4373, pre-constructing a filter layer defect identification model, and locating defects by synchronizing the multi-layer filter element image to the filter layer defect identification model to obtain multi-layer filter element defect information; step S4374, wherein the defect information of each layer of the multi-layer filter element includes multiple filter layer defect locations, multiple filter layer defect types, and multiple filter layer defect sizes; step S4375, performing coordinate vector normalization on the multi-layer filter element defect information based on the standard filter element model to obtain the filter element defect array.

[0036] Preferably, by analyzing and splitting these electromagnetic property data, multi-layer penetration data of multiple deep membrane stack filter elements are extracted, i.e., the electromagnetic response of different layers of filter elements, which can show the internal condition of each layer and reflect the penetration of each layer of membrane stack filter element. Using the multi-layer penetration data, a three-dimensional model reflecting the internal structure of the filter element is generated through three-dimensional image reconstruction technology, and finally a three-dimensional image of the multi-layer filter element is output, in which each layer reflects the actual structure and potential problems of the filter element. Multi-layer filter element images containing various defects are collected, including label information, i.e., the location, type and size of each defect in the image. The defects in the collected image data are labeled, and a defect recognition model is pre-built based on a convolutional neural network (CNN). The labeled image data is used to improve the defect recognition model. The filter layer defect recognition model is trained and synchronized with the 3D image of the multi-layer filter element. The model automatically identifies and locates defects in each layer, obtaining multi-layer filter element defect information, including the location (the specific location of the defect in each filter element), type (the specific type of defect, such as cracks, holes, or material delamination), and size (the size or range of the defect, such as the diameter, length, and depth). The detected defect information is compared with a standard filter element model, and the defect information is unified into a standardized coordinate system through coordinate vector normalization, unifying the defect representation between different layers. The normalized defect information (including location, type, and size) is organized into a filter element defect array, recording the defect status of each layer in the filter element.

[0037] In one possible implementation, step S438 further includes step S4381, interactively obtaining the historical filtration records of the sample detection filter cartridge; step S4382, performing a filter media retention depth analysis based on the historical filtration records to obtain a multi-layer retention thickness distribution; step S4383, performing a complement solution based on the multi-layer retention thickness distribution to determine the non-filtering interference region; step S4384, using the non-filtering interference region to traverse the filter cartridge defect array and locate K filter cartridge defect nodes; step S4385, mapping and calling the data from the filter cartridge defect array... The process involves: S4386, obtaining M standard defect sizes for M filter layer defect types; S4387, retrieving data from the M standard defect sizes based on the K filter layer defect types, and combining the data retrieval results with the K filter layer defect sizes to calculate Euclidean distance and determine the K defect size deviations; and S4388, where the K filter layer defect nodes constitute the filter layer defect identifier, and the K defect size deviations constitute the filter layer defect quantification coefficient.

[0038] Preferably, the historical filtration records of the filter element are obtained. These records refer to the working data recorded during the use of the filter element, including the type of filtered medium, flow rate, working pressure, usage time, filtration efficiency, etc. The retention of different layers of the medium within the filter element during filtration is analyzed, such as the particle size and concentration distribution of the filtered medium. This allows for the inference of the retention thickness of the medium in different layers of the filter element, resulting in the retention thickness distribution at different depths, i.e., the amount of filtered medium retained at each layer of the filter element. Based on the multi-layer retention thickness distribution, mathematical calculations are used to determine which areas did not participate in effective filtration, identifying non-filtration interference areas—those areas in the filter element that did not participate in effective filtration. These areas may be due to structural defects or other reasons unrelated to the normal filtration process. The filter element defect array is then traversed using these non-filtration interference areas to locate K specific filter element defect nodes. These K points are defects present in the filtration layer areas involved in filtration. Then, the filter element defect array is analyzed... In this process, the relevant information of these K defect nodes is retrieved, including their defect types (such as cracks, holes, delamination, etc.) and specific dimensions (such as length, width, depth, etc.), where K is a positive integer. The filter layer defect types and corresponding standard defect dimensions are retrieved, i.e., M standard defect dimensions for M filter layer defect types are obtained, such as those summarized from historical data, to compare with the actual detected defect dimensions to assess the severity of these defects, where M is a positive integer. Based on the K defect types, the corresponding standard defect dimensions are retrieved and compared with the actual detected K defect dimensions. Euclidean distance calculation is used to quantify the size deviation of each defect, determining the degree of deviation of these defects relative to the standard. Euclidean distance calculation is used to quantify the difference between two vectors (here, size vectors); the larger the calculated distance value, the greater the deviation of the actual defect from the standard size. Finally, the K filter element defect nodes constitute the filter element defect identifier, and the K defect size deviations form the filter element defect quantification coefficient.

[0039] Step S500: Integrate and analyze the material-related defects, quality-related defects, and filter element-related defects to obtain batch defect evaluation results. After completing the testing of the filter elements in terms of materials, manufacturing quality, and electromagnetic properties, the defect data of these different categories are comprehensively analyzed to form an overall defect evaluation of the entire production batch. This evaluation is used to determine the quality status of the batch products. Specifically, various defect information obtained from material testing, quality testing, and filter element testing are integrated to comprehensively understand the quality status of each filter element sample. The correlation between different types of defects is analyzed. For example, impurities in the material may cause abnormal signals in eddy current testing, or temperature control problems in the manufacturing process may lead to a decrease in the mechanical properties of the material. Based on the results of the integrated analysis, the quality of the entire batch of filter elements is evaluated, and the batch defect evaluation results are finally obtained, including the type, quantity, distribution, and severity of filter element defects.

[0040] In one possible implementation, step S500 further includes step S510, spatially integrating the material defect identifier, quality defect identifier, and filter element defect identifier to obtain a filter element defect space; step S520, pre-configuring material defect weights, quality defect weights, and filter element defect weights; step S530, using the pre-configured material defect weights, quality defect weights, and filter element defect weights to perform defect risk summation processing on the material defect quantification coefficient, quality defect quantification coefficient, and filter element defect quantification coefficient to obtain a defect risk coefficient; and step S540, the filter element defect space and the defect risk coefficient constitute the batch defect evaluation result.

[0041] Preferably, material defect identifiers, quality defect identifiers, and filter element defect identifiers are spatially integrated to form a complete filter element defect space. This means that different types of defects are uniformly represented according to their location within the filter element, placing different types of defect identifiers in the same space to comprehensively evaluate their impact on the overall performance of the filter element. Preset weights for material defects, quality defects, and filter element defects are used. The material defect weight represents the importance of material defects in the overall defect evaluation; the quality defect weight represents the importance of quality defects introduced during the manufacturing process; and the filter element defect weight corresponds to the weight of defects inside or on the surface of the filter element, reflecting the impact of these defects on the overall performance of the filter element. The impact on filter cartridge filtration performance or durability is assessed by multiplying different types of defect quantification coefficients by corresponding pre-configured weights and then summing these results. By considering the severity of each defect and its impact on the overall performance of the filter cartridge, a comprehensive risk coefficient, or defect risk coefficient, is calculated. This coefficient represents the overall risk that the filter cartridge may face in actual use. A higher risk coefficient usually means that there may be significant problems with the batch of filter cartridges. The filter cartridge defect space, composed of spatially integrated defect identifiers, and the defect risk coefficient constitute the batch defect evaluation result, which can provide a comprehensive assessment reflecting the quality status and potential usage risks of the entire batch of filter cartridges.

[0042] Step S600: Optimize the production process of the membrane stack disc filter element based on the batch defect evaluation results. By analyzing the batch defect evaluation results, identify the most common or severe defect types during production, which may include insufficient material purity, dimensional errors during manufacturing, uneven filter element structure, and insufficient material strength. Further analyze the sources of these defects, such as improper material selection, equipment aging, or unreasonable process parameter settings. Develop production process optimization plans; for example, if the defect is related to the material, consider using higher quality materials. For quality problems discovered during manufacturing, adjust relevant process parameters. For example, adjust mold temperature, pressure, and time control to improve product consistency and stability, and optimize process parameters such as coating thickness and compaction density to reduce manufacturing defects. If equipment aging or insufficient precision leads to defects, consider upgrading the equipment or strengthening equipment maintenance, such as improving the automation level of the equipment and adding online monitoring and feedback control systems to detect and adjust process parameters in real time to ensure the stability of the production process.

[0043] In the above text, refer to Figure 1 A defect detection method for a membrane stack disc filter element according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 This invention describes a defect detection device for a membrane stack disc filter element according to an embodiment of the present invention.

[0044] According to an embodiment of the present invention, a defect detection device for a membrane stack disc filter element is provided to solve the technical problem that existing filter element defect detection methods struggle to efficiently and accurately detect internal defects in complex multi-layer membrane stack disc filter elements, leading to insufficient filter element quality and reliability. This device achieves the technical effect of improving the detection accuracy, quality, and reliability of membrane stack disc filter elements. The membrane stack disc filter element defect detection device includes: a sample filter element acquisition module 10, a standard filter element characteristic acquisition module 20, a filter element associated defect acquisition module 30, a material quality inspection module 40, a batch defect evaluation result acquisition module 50, and a membrane stack disc filter element optimization module 60.

[0045] The sample filter element acquisition module 10 is used to sample membrane stack disc filter elements produced in the same batch to obtain sample filter elements for testing; the standard filter element characteristic acquisition module 20 is used to interactively obtain the standard design information of the sample filter elements and extract standard filter element characteristics from the standard design information; the filter element associated defect acquisition module 30 is used to run a pulse eddy current testing device to scan the sample filter elements for testing, obtain filter element electromagnetic characteristic data, and perform filtration interference analysis based on the filter element electromagnetic characteristic data to obtain filter element associated defects; the material quality testing module 40 is used to perform material quality testing on the sample filter elements according to the standard filter element characteristics to obtain material associated defects and quality associated defects; the batch defect evaluation result acquisition module 50 is used to integrate and analyze the material associated defects, quality associated defects, and filter element associated defects to obtain batch defect evaluation results; and the membrane stack disc filter element optimization module 60 is used to optimize the production process of the membrane stack disc filter elements according to the batch defect evaluation results.

[0046] The specific configuration of the material quality inspection module 40 will be described in detail below. The material quality inspection module 40 may further include: calculating the mean of the multiple temperature monitoring data sets to determine multiple trend analysis starting points; performing trend analysis based on the multiple trend analysis starting points and the multiple temperature monitoring data sets to obtain multiple data deviation trends; determining whether the multiple data deviation trends are greater than a preset deviation trend degree; if so, adding the corresponding temperature monitoring data set to multiple abnormal temperature monitoring data sets and using the corresponding data deviation trend degree as the abnormal trend degree; if not, adding the corresponding temperature monitoring data set to multiple regular temperature monitoring data sets and using the corresponding data deviation trend degree as the regular trend degree.

[0047] The specific configuration of the material quality inspection module 40 will be described in detail below. The material quality inspection module 40 may further include: obtaining standard filter material and standard filter structure from the standard filter characteristics; performing material characteristic inspection on the sample test filter according to the standard filter material and standard filter structure to generate the material-related defects, wherein the material-related defects include a material defect identifier and a material defect quantification coefficient; and performing quality control inspection on the sample test filter according to the standard filter structure to generate the quality-related defects, wherein the quality-related defects include a quality defect identifier and a quality defect quantification coefficient.

[0048] The specific configuration of the material quality testing module 40 will be described in detail below. The material quality testing module 40 may further include: performing geometric modeling based on the standard filter element structure to reconstruct the test filter element model; performing material property analysis based on the standard filter element material and assigning the analysis results to the test filter element model to obtain the standard filter element model; interactively obtaining the standard operating conditions of the sample test filter element, and performing operational simulation after applying a working load to the standard filter element model based on the standard operating conditions to locate a set of key stress nodes, wherein the set of key stress nodes includes a stress node array and a stress parameter array; during the operation of applying a working load to the sample test filter element according to the standard operating conditions, using the set of key stress nodes to traverse the sample test filter element for material characteristic detection, locating multiple stress defect nodes and multiple stress deviation percentages; the multiple stress defect nodes constitute the material defect identifier, and the multiple stress deviation percentages constitute the material defect quantification coefficient.

[0049] The specific configuration of the material quality inspection module 40 will be described in detail below. The material quality inspection module 40 may further include: interactively obtaining the model information of the sample test filter element, and matching and calling a sealing test device according to the model information; interactively obtaining the filter element design pressure of the sample test filter element, and setting multi-level flow pressure parameters with 1.2 times the filter element design pressure as the pressure variation range; after installing the sample test filter element into the sealing test device, applying a working load to the sample test filter element using the multi-level flow pressure parameters, locating multiple airtightness defect nodes and multiple airtightness pressure resistance parameters; the multiple airtightness defect nodes constitute the quality defect identifier, and the multiple airtightness pressure resistance parameters constitute the quality defect quantification coefficient.

[0050] The specific configuration of the batch defect evaluation result acquisition module 50 will be described in detail below. The batch defect evaluation result acquisition module 50 may further include: spatially integrating the material defect identifier, quality defect identifier, and filter element defect identifier to obtain a filter element defect space; pre-configuring material defect weights, quality defect weights, and filter element defect weights; using the pre-configured material defect weights, quality defect weights, and filter element defect weights to perform defect risk summation processing on the material defect quantification coefficient, quality defect quantification coefficient, and filter element defect quantification coefficient to obtain a defect risk coefficient; the filter element defect space and the defect risk coefficient constitute the batch defect evaluation result.

[0051] The specific configuration of the material quality inspection module 40 will be described in detail below. The material quality inspection module 40 may further include: obtaining multi-layer penetration data of the multiple deep membrane stack filter elements by splitting the electromagnetic property data of the filter element; reconstructing a three-dimensional image of the filter element based on the multi-layer penetration data to obtain a multi-layer filter element image; pre-constructing a filter layer defect identification model, and locating defects by synchronizing the multi-layer filter element image to the filter layer defect identification model to obtain multi-layer filter element defect information; wherein, the defect information of each layer of the multi-layer filter element includes multiple filter layer defect locations, multiple filter layer defect types, and multiple filter layer defect sizes; and normalizing the coordinate vector of the multi-layer filter element defect information based on the standard filter element model to obtain the filter element defect array.

[0052] The specific configuration of the material quality testing module 40 will be described in detail below. The material quality testing module 40 may further include: interactively obtaining historical filtration records of the sample filter element; performing a filter media retention depth analysis based on the historical filtration records to obtain a multi-layer retention thickness distribution; performing a complement solution based on the multi-layer retention thickness distribution to determine non-filter interference regions; traversing the filter element defect array using the non-filter interference regions to locate K filter element defect nodes; mapping and calling the K filter layer defect types and K filter layer defect sizes of the K filter element defect nodes from the filter element defect array; obtaining M standard defect sizes for M filter layer defect types; retrieving data from the M standard defect sizes according to the K filter layer defect types, and combining the data retrieval results with the K filter layer defect size mapping to perform Euclidean distance calculation to determine the K defect size deviations; the K filter element defect nodes constitute the filter element defect identifier, and the K defect size deviations constitute the filter element defect quantification coefficient.

[0053] The defect detection device for a membrane stack disc filter element provided in this embodiment of the invention can execute the defect detection method for a membrane stack disc filter element provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0054] Although this application makes various references to certain modules in the apparatus according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not intended to limit the scope of protection of this invention.

[0055] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for defect detection of a membrane stack disc filter element, characterized in that, The method includes: Samples of membrane stack disc filter elements produced in the same batch were taken to obtain sample filter elements for testing; The standard design information of the sample detection filter element is obtained interactively, and the standard filter element characteristics are extracted from the standard design information. The sample filter element is scanned by a pulsed eddy current testing device to obtain electromagnetic characteristic data of the filter element, and based on the electromagnetic characteristic data of the filter element, a filter interference analysis is performed to obtain the filter element associated defects. Based on the characteristics of the standard filter element, the sample test filter element is subjected to material quality testing to obtain material-related defects and quality-related defects. By integrating and analyzing the material-related defects, quality-related defects, and filter element-related defects, batch defect evaluation results are obtained. Based on the batch defect evaluation results, the production process of the membrane stack disc filter element was optimized. The sample filter element is subjected to material quality testing based on the characteristics of the standard filter element to obtain material-related defects and quality-related defects, including: The standard filter material and standard filter structure are obtained by referring to the characteristics of the standard filter. The sample filter element is subjected to material characteristic testing based on the standard filter element material and standard filter element structure to generate the material-related defects, wherein the material-related defects include material defect identifiers and material defect quantification coefficients; The sample test filter is subjected to quality control testing according to the standard filter structure to generate the quality-related defects, wherein the quality-related defects include quality defect identifiers and quality defect quantification coefficients; The sample filter element is scanned using a pulsed eddy current testing device to obtain electromagnetic characteristic data of the filter element. Based on the electromagnetic characteristic data, a filter interference analysis is performed to obtain filter element-related defects, including: Call the pulse eddy current detection device, and set the frequency of the Hall sensor in the pulse eddy current detection device according to the characteristics of the standard filter element to obtain the local detection device; The local detection device is run to scan the sample detection filter to obtain electromagnetic property data of the filter. Based on the electromagnetic property data of the filter element, defects are identified and located in multiple deep membrane stack filter elements in the sample test filter element to obtain a filter element defect array; Based on the filter element defect array, a filtration interference analysis is performed to obtain the filter element associated defects, wherein the filter element associated defects include filter element defect identifiers and filter element defect quantification coefficients; By integrating and analyzing the material-related defects, quality-related defects, and filter element-related defects, batch defect evaluation results are obtained, including: Spatially integrate the material defect markers, quality defect markers, and filter element defect markers to obtain the filter element defect space; Pre-configured material defect weighting, quality defect weighting, and filter element defect weighting; The pre-configured material defect weight, quality defect weight, and filter element defect weight are used to perform defect risk summation on the material defect quantification coefficient, quality defect quantification coefficient, and filter element defect quantification coefficient to obtain a defect risk coefficient. The defect space and defect risk coefficient of the filter element constitute the batch defect evaluation result.

2. The defect detection method for a membrane stack disc filter element as described in claim 1, characterized in that, The method further includes performing material property testing on the sample filter element based on the standard filter element material and standard filter element structure to generate the material-related defects. Geometric modeling is performed based on the standard filter element structure to reconstruct the test filter element model; Material property analysis is performed on the standard filter material, and the analysis results are assigned to the test filter model to obtain the standard filter model. The standard operating conditions of the sample detection filter element are obtained interactively, and after applying a working load to the standard filter element model based on the standard operating conditions, an operation simulation is performed to locate the set of key stress nodes, wherein the set of key stress nodes includes a stress node array and a stress parameter array. During the operation of the sample test filter under the standard operating conditions, the sample test filter is traversed using the set of key stress nodes to detect material properties, and multiple stress defect nodes and multiple stress deviation percentages are located. The plurality of stress-induced defect nodes constitute the material defect identifier, and the plurality of stress deviation percentages constitute the material defect quantification coefficient.

3. The defect detection method for a membrane stack disc filter element as described in claim 1, characterized in that, The method further includes performing quality control testing on the sample test filter element according to the standard filter element structure to generate the quality-related defects, and the method also includes: The model information of the sample detection filter is obtained interactively, and the sealing test equipment is matched and called according to the model information; The filter design pressure of the sample detection filter is obtained interactively, and 1.2 times the filter design pressure is used as the pressure variation range to set multi-stage flow pressure parameters; After the sample testing filter element is installed into the sealing test equipment, the working load is applied to the sample testing filter element using the multi-stage flow pressure parameters to locate multiple airtight defect nodes and multiple airtight pressure resistance parameters. The plurality of airtight defect nodes constitute the quality defect identifier, and the plurality of airtight pressure resistance parameters constitute the quality defect quantification coefficient.

4. The defect detection method for a membrane stack disc filter element as described in claim 2, characterized in that, Based on the electromagnetic property data of the filter cartridges, defects are identified and located in multiple deep membrane stack filter cartridges in the sample test filter cartridges to obtain a filter cartridge defect array. The method further includes: Multilayer penetration data of the multiple deep membrane stack filter elements are obtained by decomposing the electromagnetic property data of the filter element; Based on the multi-layer penetration data, a three-dimensional image of the filter element is reconstructed to obtain a multi-layer filter element image; A pre-constructed filter layer defect identification model is used, and the defect location is performed by synchronizing the multi-layer filter image to the filter layer defect identification model to obtain multi-layer filter defect information; The defect information of each layer of the multilayer filter element includes multiple filter layer defect locations, multiple filter layer defect types, and multiple filter layer defect sizes. Using the standard filter model as a reference, the coordinate vector of the defect information of the multilayer filter is normalized to obtain the filter defect array.

5. The defect detection method for a membrane stack disc filter element as described in claim 4, characterized in that, Based on the filter element defect array, filtration interference analysis is performed to obtain the filter element associated defects, wherein the filter element associated defects include filter element defect identifiers and filter element defect quantification coefficients. The method further includes: Interactively obtain the historical filtration records of the sample detection filter cartridge; Based on the historical filtration records, the retention depth of the filter media is analyzed to obtain the multi-layer retention thickness distribution. Based on the multi-layer interception thickness distribution, the complement solution is performed to determine the non-filtered interference region; The non-filtering interference region is used to traverse the filter element defect array and locate K filter element defect nodes; The K filter layer defect types and K filter layer defect sizes of the K filter layer defect nodes are retrieved from the filter element defect array mapping. The function retrieves M standard defect sizes for M types of filter layer defects. Data is retrieved from the M standard defect sizes based on the K filter layer defect types, and the data retrieval results are combined with the K filter layer defect sizes to perform Euclidean distance calculation to determine the K defect size deviations. The K filter element defect nodes constitute the filter element defect identifier, and the K defect size deviations constitute the filter element defect quantification coefficient.

6. A defect detection device for a membrane stack disc filter element, characterized in that, The apparatus is used to implement the defect detection method for a membrane stack disc filter element according to any one of claims 1-5, and the apparatus comprises: The sample testing filter element acquisition module is used to sample membrane stack disc filter elements produced in the same batch to obtain sample testing filter elements; A standard filter element characteristic acquisition module is used to interactively obtain the standard design information of the sample detection filter element and extract the standard filter element characteristics from the standard design information. The filter element associated defect acquisition module is used to run a pulsed eddy current detection device to scan the sample test filter element, obtain the filter element electromagnetic characteristic data, and perform filter interference analysis based on the filter element electromagnetic characteristic data to obtain the filter element associated defects. The material quality testing module is used to perform material quality testing on the sample test filter element according to the characteristics of the standard filter element, and to obtain material-related defects and quality-related defects. The batch defect evaluation result acquisition module is used to integrate and analyze the material-related defects, quality-related defects, and filter element-related defects to obtain batch defect evaluation results. The membrane stack disc filter element optimization module is used to optimize the production process of the membrane stack disc filter element based on the batch defect evaluation results.

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