Plate air filter pressure loss dynamic detection system and method thereof

Abstract

The application discloses a kind of plate air filter pressure loss dynamic detection system and method thereof.The system includes air duct module, frequency conversion module, differential pressure detection module and adjustment module.The first test part and the second test part of air duct module are respectively provided with first static pressure cavity and second static pressure cavity, and can be detachably installed on the two sides of the filter to be measured.The variable power airflow is supplied to the first test part by frequency conversion module.The differential pressure detection module samples the static pressure cavity pressure data by pressure sensor.The power adjustment unit, data analysis unit and the like of adjustment module work cooperatively, and the power of frequency conversion module is adjusted according to the difference between pressure difference and reference differential pressure curve.The application realizes dynamic detection and life prediction of plate air filter pressure loss, and improves detection accuracy and efficiency.

Description

Technical Field

[0001] This invention relates to the field of air filtration equipment testing technology, specifically to a dynamic detection system and method for pressure loss of a plate air filter. Background Technology

[0002] In modern industrial production and indoor environmental purification, panel air filters are widely used in air purification, air conditioning systems, and other applications. Over time, filter pressure loss increases due to dust accumulation and clogging, affecting filtration efficiency and system operation. Therefore, accurately detecting filter pressure loss is crucial.

[0003] Currently, most traditional methods for testing pressure loss in panel air filters employ static testing, measuring the pressure difference under fixed operating conditions. This method cannot simulate the complex and variable operating conditions of filters in actual operation, such as airflow fluctuations and changes in contaminant concentration. This leads to significant discrepancies between the test results and the actual pressure loss under real-world conditions, making it difficult to accurately reflect the filter's true performance. Furthermore, existing testing technologies typically focus only on the single parameter of pressure loss, lacking a comprehensive assessment of filter performance. This makes it impossible to promptly detect potential filter failures and performance degradation, and fails to provide a scientific and effective basis for filter maintenance and replacement. Simultaneously, traditional testing methods struggle to accurately acquire and analyze crucial information such as pressure loss differences in different areas of the filter and the deep-layer trapping capacity of the filter material. Summary of the Invention

[0004] To address the above problems, this invention provides a dynamic detection system and method for pressure loss of a plate air filter.

[0005] A first aspect of the present invention provides a dynamic detection system for pressure loss of a plate air filter, comprising:

[0006] The air duct module includes a first test section and a second test section that are detachably installed on two opposite sides of the filter to be tested. The first test section is provided with a first static pressure chamber and the second test section is provided with a second static pressure chamber.

[0007] The frequency converter module is connected to the first test section of the air duct module through the air duct, and is configured to supply air to the first test section with various variable power through the air duct.

[0008] The differential pressure detection module includes a first pressure sensor and a second pressure sensor respectively disposed in the first static pressure chamber and the second static pressure chamber. The first pressure sensor and the second pressure sensor are configured to sample the pressure data in the corresponding static pressure chamber at a preset sampling period.

[0009] The regulation module includes a power regulation unit, a data analysis unit, a storage unit, and a feedback unit;

[0010] The power regulation unit is connected to the frequency converter module and is configured to set the output power of the frequency converter module according to a preset detection mode;

[0011] The data analysis unit is configured to receive pressure data and calculate the pressure difference between the first test unit and the second test unit.

[0012] The storage unit is configured to store log data of sampling performed by the first pressure sensor and the second pressure sensor, as well as a reference differential pressure curve;

[0013] The feedback unit sends a feedback signal to the power regulation unit based on the difference between the pressure difference and the reference pressure difference curve to perform power regulation on the frequency converter module.

[0014] As a preferred embodiment, both the first test section and the second test section are configured to partially cover the two opposite sides of the filter under test, or...

[0015] The filter under test is completely covered on both opposite sides, and movable sealed airbags are provided in both the first static pressure chamber and the second static pressure chamber. The internal pressure of the movable sealed airbags is consistent with the pressure of their corresponding static pressure chambers.

[0016] As a preferred embodiment, when the first test section and the second test section completely cover both sides of the filter under test, displacement sensors are provided in the first static pressure chamber and the second static pressure chamber, and the displacement sensors are configured to detect the position of the sealed airbag in its corresponding static pressure chamber.

[0017] As a preferred embodiment, the first test unit is also connected to a particle injection module, which is configured to inject a preset type of particles into the first static pressure chamber at a preset particle concentration. Both the first and second static pressure chambers are equipped with particle concentration detectors, which are configured to detect the particle concentration in the first and second static pressure chambers.

[0018] A second aspect of the present invention provides a method for dynamic detection of pressure loss in a plate air filter, comprising the following steps:

[0019] S1. Under standard environmental conditions, record the pressure difference-flow characteristic curve of the filter under test;

[0020] S2. Collect the attenuation curves of pressure difference over time under different pollution loads;

[0021] S3. Measure the current environmental baseline value, load the first test unit and the second test unit on the two opposite sides of the filter, drive the frequency conversion module to generate dynamic airflow in a preset simulation mode, sample the pressure difference and particle concentration difference under the current preset simulation mode, and extract the time domain features and frequency domain features within a preset time window.

[0022] S4. Compare with the baseline differential pressure curve. If the difference is within the preset range, the pressure loss is considered normal; otherwise, proceed to the next step.

[0023] S5. After moving the sealed airbag to another detection position, repeat steps S2-S3 to measure the pressure difference and particle concentration difference in the unobstructed area, extract the time and frequency characteristics of the unobstructed area, and repeat this step until at least one area has a difference from the reference pressure difference curve within a preset range.

[0024] An alarm is triggered when no region has a difference between its pressure and the baseline differential pressure curve that falls within the preset range.

[0025] As a preferred approach, the method further includes the following steps: when executing the preset simulation mode, a dynamic airflow is generated, and under the standard differential pressure-flow characteristic curve, an initial airflow is generated until the time domain and frequency domain characteristics are in a steady state, and then a disturbance airflow with random amplitude is added.

[0026] As a preferred approach, the following steps are also included:

[0027] It also includes the following steps:

[0028] Based on S5, local areas with a degree of overlap with the benchmark pressure difference curve exceeding a preset threshold are selected as effective filtration benchmark areas, and pollution diffusion areas with continuous pressure difference deviation from the benchmark and particle concentration difference exceeding a preset threshold are simultaneously identified.

[0029] Within the pollution diffusion zone, sampling points were divided into equidistant grids. The deviation rate between the real-time differential pressure and the reference differential pressure at each grid point was measured, and the upstream and downstream particle concentration ratios at the corresponding sampling points were detected.

[0030] Based on the spatial ratio of the pollution diffusion zone to the effective filtration benchmark zone, the dust holding capacity index under the same spatial ratio in the pre-stored historical data is retrieved, and dynamic weighting factors that are positively correlated with the deviation rate and concentration ratio are assigned to each sampling point in the pollution diffusion zone based on the dust holding capacity index.

[0031] Extract the average dynamic weight factor of the pollution diffusion zone, identify the abrupt change time point of the average dynamic weight factor within the sampling period, and calculate the predicted lifetime of each region from the current state to the weight abrupt change point by combining the weight change difference and time interval between adjacent sampling periods.

[0032] Multiple verification points are randomly selected in the effective filtration benchmark area and tracer particles are injected. The proportion of tracer particles detected in another verification point in the effective filtration benchmark area and the pollution diffusion area is statistically analyzed to determine the penetration probability. When no tracer particles are detected in the area corresponding to the mutation time point, the interval between the mutation time point and the previous sampling period is used as the actual lifetime benchmark value, and the lifetime is estimated based on the difference between the current weight of the remaining areas and the weight of the mutation point.

[0033] Based on the infiltration probability of the effective filtration benchmark area and the historical decay rate of the pre-stored dust holding capacity index, a negative correlation mapping relationship between the infiltration probability and the dust holding capacity index is established. Based on the current dust holding capacity index and infiltration probability, the probability of the effective filtration benchmark area turning into a pollution diffusion area within a unit sampling period is predicted.

[0034] When the transition probability is lower than the preset risk threshold, the predicted lifetime is extended based on the current estimated lifetime value and the rate of change of the penetration probability in adjacent sampling periods. When the transition probability is higher than the preset risk threshold, the sub-region with the fastest weight change rate in the pollution diffusion zone is extracted, and the number of sampling periods from the baseline state to the current pollution state in the sub-region is used as the lifetime decay coefficient to shorten the overall predicted lifetime.

[0035] Add monitoring points at the boundary between the pollution diffusion zone and the effective filtration reference zone. If the penetration probability of the boundary monitoring point increases by more than a preset value for a preset number of consecutive sampling cycles, the correction result of the estimated lifespan is deemed invalid.

[0036] The corrected lifetime value of each sampling point is mapped to the three-dimensional spatial model of the filter to generate a lifetime distribution cloud map. When the area increase of the short lifetime warning region exceeds the preset threshold within a continuous detection cycle, an early warning is initiated.

[0037] Compared with the prior art, the present invention has the following advantages:

[0038] This invention's system, through the coordinated operation of multiple modules such as the duct module and the frequency converter module, enables dynamic detection of pressure loss in plate air filters. It not only detects the overall pressure loss but also adapts to different detection needs by setting different detection modes and parameters, providing a comprehensive understanding of the filter's performance status. The differential pressure detection module collects pressure data at a preset sampling period, and the data analysis unit processes the data to calculate the pressure difference, ensuring data accuracy and timeliness, and providing a reliable basis for subsequent judgment and adjustment. The feedback unit feeds back a signal to the power adjustment unit based on the difference between the pressure difference and the reference pressure difference curve, enabling automatic adjustment of the frequency converter module's power. This allows the system to adapt to changes in the filter's state in real time, ensuring detection accuracy and stability. The first and second testing sections can partially or completely enclose the filter under test. When fully enclosed, a movable sealed airbag and displacement sensor are provided, allowing flexible adaptation to filters of different specifications and states, improving the system's applicability, and making it suitable for detecting different areas of plate air filters. Connecting the particulate jet module and the particle concentration detector allows for simulating pollution loads and detecting particle concentration, contributing to a more comprehensive understanding of the filter's performance in polluted environments.

[0039] The method of this invention records the pressure difference-flow characteristic curve under standard environmental conditions, providing an accurate benchmark for subsequent detection and judgment, and ensuring the reliability of the detection results. Driving the frequency converter module to generate dynamic airflow using a preset simulation method, including generating an initial airflow to a steady state and then adding disturbed airflow, can more realistically simulate the airflow state of the filter in actual operation, improving detection accuracy. By moving the enclosed airbag to detect unobstructed areas, abnormal areas can be accurately located. When no area meeting the benchmark is found, an alarm is triggered, promptly identifying filter problems.

[0040] Based on the selected effective filtration benchmark area and pollution diffusion area, the lifespan is scientifically and rationally predicted by a series of steps, including dividing sampling points, allocating dynamic weight factors, and calculating the predicted lifespan, combined with factors such as penetration probability. This provides a basis for maintenance and replacement. Attached Figure Description

[0041] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0042] Figure 1 This is a structural block diagram of the system provided in the embodiments of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] In a first aspect, this embodiment provides a dynamic detection system for pressure loss of a plate-type air filter, such as... Figure 1 As shown, it includes:

[0045] The air duct module includes a first test section and a second test section that are detachably installed on two opposite sides of the filter to be tested. The first test section is provided with a first static pressure chamber and the second test section is provided with a second static pressure chamber.

[0046] The frequency converter module is connected to the first test section of the air duct module through the air duct, and is configured to supply air to the first test section with various variable power through the air duct.

[0047] The differential pressure detection module includes a first pressure sensor and a second pressure sensor respectively disposed in the first static pressure chamber and the second static pressure chamber. The first pressure sensor and the second pressure sensor are configured to sample the pressure data in the corresponding static pressure chamber at a preset sampling period.

[0048] The regulation module includes a power regulation unit, a data analysis unit, a storage unit, and a feedback unit;

[0049] The power regulation unit is connected to the frequency converter module and is configured to set the output power of the frequency converter module according to a preset detection mode;

[0050] The data analysis unit is configured to receive pressure data and calculate the pressure difference between the first test unit and the second test unit.

[0051] The storage unit is configured to store log data of sampling performed by the first pressure sensor and the second pressure sensor, as well as a reference differential pressure curve;

[0052] The feedback unit sends a feedback signal to the power regulation unit based on the difference between the pressure difference and the reference pressure difference curve to perform power regulation on the frequency converter module.

[0053] As a preferred embodiment, both the first test section and the second test section are configured to partially cover the two opposite sides of the filter under test, or...

[0054] The filter under test is completely covered on both opposite sides, and movable sealed airbags are provided in both the first static pressure chamber and the second static pressure chamber. The internal pressure of the movable sealed airbags is consistent with the pressure of their corresponding static pressure chambers.

[0055] As a preferred embodiment, when the first test section and the second test section completely cover both sides of the filter under test, displacement sensors are provided in the first static pressure chamber and the second static pressure chamber, and the displacement sensors are configured to detect the position of the sealed airbag in its corresponding static pressure chamber.

[0056] As a preferred embodiment, the first test unit is also connected to a particle injection module, which is configured to inject a preset type of particles into the first static pressure chamber at a preset particle concentration. Both the first and second static pressure chambers are equipped with particle concentration detectors, which are configured to detect the particle concentration in the first and second static pressure chambers.

[0057] As a preferred approach, the time-domain and frequency-domain features of the real-time pressure difference are extracted, and the sample entropy and Hurst exponent of the pressure difference within the current time period are calculated. When the sample entropy exceeds a first threshold and the Hurst exponent is lower than a second threshold, it is determined to be an abnormal pressure drop event.

[0058] A second aspect of this embodiment provides a method for dynamic detection of pressure loss in a plate air filter, comprising the following steps:

[0059] S1. Under standard environmental conditions, record the pressure difference-flow characteristic curve of the filter under test;

[0060] S2. Collect the attenuation curves of pressure difference over time under different pollution loads;

[0061] S3. Measure the current environmental baseline value, load the first test unit and the second test unit on the two opposite sides of the filter, drive the frequency conversion module to generate dynamic airflow in a preset simulation mode, sample the pressure difference and particle concentration difference under the current preset simulation mode, and extract the time domain features and frequency domain features within a preset time window.

[0062] S4. Compare with the baseline differential pressure curve. If the difference is within the preset range, the pressure loss is considered normal; otherwise, proceed to the next step.

[0063] S5. After moving the sealed airbag to another detection position, repeat steps S2-S3 to measure the pressure difference and particle concentration difference in the unobstructed area, extract the time and frequency characteristics of the unobstructed area, and repeat this step until at least one area has a difference from the reference pressure difference curve within a preset range.

[0064] An alarm is triggered when no region has a difference between its pressure and the baseline differential pressure curve that falls within the preset range.

[0065] As a preferred approach, the method further includes the following steps: when executing the preset simulation mode, a dynamic airflow is generated, and under the standard differential pressure-flow characteristic curve, an initial airflow is generated until the time domain and frequency domain characteristics are in a steady state, and then a disturbance airflow with random amplitude is added.

[0066] As a preferred approach, the following steps are also included:

[0067] It also includes the following steps:

[0068] Based on S5, local areas with a degree of overlap with the benchmark pressure difference curve exceeding a preset threshold are selected as effective filtration benchmark areas, and pollution diffusion areas with continuous pressure difference deviation from the benchmark and particle concentration difference exceeding a preset threshold are simultaneously identified.

[0069] Within the pollution diffusion zone, sampling points were divided into equidistant grids. The deviation rate between the real-time differential pressure and the reference differential pressure at each grid point was measured, and the upstream and downstream particle concentration ratios at the corresponding sampling points were detected.

[0070] Based on the spatial ratio of the pollution diffusion zone to the effective filtration benchmark zone, the dust holding capacity index under the same spatial ratio in the pre-stored historical data is retrieved, and dynamic weighting factors that are positively correlated with the deviation rate and concentration ratio are assigned to each sampling point in the pollution diffusion zone based on the dust holding capacity index.

[0071] Extract the average dynamic weight factor of the pollution diffusion zone, identify the abrupt change time point of the average dynamic weight factor within the sampling period, and calculate the predicted lifetime of each region from the current state to the weight abrupt change point by combining the weight change difference and time interval between adjacent sampling periods.

[0072] Multiple verification points are randomly selected in the effective filtration benchmark area and tracer particles are injected. The proportion of tracer particles detected in another verification point in the effective filtration benchmark area and the pollution diffusion area is statistically analyzed to determine the penetration probability. When no tracer particles are detected in the area corresponding to the mutation time point, the interval between the mutation time point and the previous sampling period is used as the actual lifetime benchmark value, and the lifetime is estimated based on the difference between the current weight of the remaining areas and the weight of the mutation point.

[0073] Based on the infiltration probability of the effective filtration benchmark area and the historical decay rate of the pre-stored dust holding capacity index, a negative correlation mapping relationship between the infiltration probability and the dust holding capacity index is established. Based on the current dust holding capacity index and infiltration probability, the probability of the effective filtration benchmark area turning into a pollution diffusion area within a unit sampling period is predicted.

[0074] When the transition probability is lower than the preset risk threshold, the predicted lifetime is extended based on the current estimated lifetime value and the rate of change of the penetration probability in adjacent sampling periods. When the transition probability is higher than the preset risk threshold, the sub-region with the fastest weight change rate in the pollution diffusion zone is extracted, and the number of sampling periods from the baseline state to the current pollution state in the sub-region is used as the lifetime decay coefficient to shorten the overall predicted lifetime.

[0075] Add monitoring points at the boundary between the pollution diffusion zone and the effective filtration reference zone. If the penetration probability of the boundary monitoring point increases by more than a preset value for a preset number of consecutive sampling cycles, the correction result of the estimated lifespan is deemed invalid.

[0076] The corrected lifetime value of each sampling point is mapped to the three-dimensional spatial model of the filter to generate a lifetime distribution cloud map. When the area increase of the short lifetime warning region exceeds the preset threshold within a continuous detection cycle, an early warning is initiated.

[0077] In this embodiment, the duct module includes a first testing section and a second testing section, which are detachably installed on opposite sides of the filter under test. Specifically, the first and second testing sections are connected to the filter under test via flanges for easy installation and disassembly. A first static pressure chamber is provided within the first testing section, and a second static pressure chamber is provided within the second testing section. Both the first and second static pressure chambers are rectangular in shape, and their dimensions match the dimensions of the filter under test to ensure accurate measurement of the pressure on both sides of the filter.

[0078] In one embodiment, the first and second test sections partially cover opposite sides of the filter under test. In this case, the coverage area of ​​the first and second test sections is half the surface area of ​​the filter under test, enabling pressure loss detection of a portion of the filter. In another embodiment, the first and second test sections completely cover opposite sides of the filter under test. Movable sealing airbags are provided in both the first and second static pressure chambers. The sealing airbags are connected to their respective static pressure chambers via pressure conduits, and one-way pressure valves are installed within the conduits to ensure that the internal pressure of the sealing airbag remains consistent with the pressure of its corresponding static pressure chamber. The sealing airbags are made of rubber, possessing good elasticity and sealing properties. The structural and connection configurations of the sealed airbag are common in the art and will not be described in detail here. One driving method for the sealed airbag is to use a variable-volume airbag with a partition on one side that slides within a static pressure chamber. By inflating the airbag, the partition is moved within the static pressure chamber, thus changing its coverage area. Another driving method is to use a servo drive mechanism, which is common in the art, to drive the airbag to move within the static pressure chamber, making its volume fixed. Both driving methods in this embodiment are common in the art in terms of structure and connection, and will not be described in detail here.

[0079] The frequency converter module is connected to the first testing section of the air duct module via a duct. The module uses a three-phase asynchronous motor paired with a frequency converter, enabling it to supply air to the first testing section at various variable power levels through the duct. The frequency converter's output power ranges from 0.75kW to 160kW, meeting the testing requirements of different specifications of panel air filters. During testing, the frequency converter module adjusts its output power according to a preset testing mode, thereby controlling the airflow and pressure entering the first testing section.

[0080] The differential pressure detection module includes a first pressure sensor and a second pressure sensor, which are respectively disposed in the first static pressure chamber and the second static pressure chamber. The measurement range of the first pressure sensor and the second pressure sensor is -10 kPa to 10 kPa, and the accuracy is ±0.5%FS. The first pressure sensor and the second pressure sensor are configured to sample the pressure data in the corresponding static pressure chamber at a preset sampling period. In this embodiment, the preset sampling period is 1 second.

[0081] The regulation module includes a power regulation unit, a data analysis unit, a storage unit, and a feedback unit. The power regulation unit is connected to the frequency converter module, specifically via a 4-20mA analog signal to the inverter's control terminal. The power regulation unit is configured to set the output power of the frequency converter module according to a preset detection mode. The data analysis unit uses a DSP chip and is configured to receive pressure data from a first pressure sensor and a second pressure sensor, calculating the pressure difference between the first and second test sections. The storage unit is a solid-state drive (SSD) configured to store log data sampled by the first and second pressure sensors, as well as a reference pressure difference curve. The feedback unit includes a comparator and a signal amplifier, which generates a feedback signal based on the difference between the pressure difference and the reference pressure difference curve, and then uses the power regulation unit to regulate the power of the frequency converter module.

[0082] A particle injection module is also connected to the first testing section. The particle injection module is configured to inject a preset type of particles into the first static pressure chamber at a preset particle concentration. In this embodiment, the preset type of particles is standard test dust, and the preset particle concentration is 10,000 particles / L. Particle concentration detectors are installed in both the first and second static pressure chambers, and are configured to detect the particle concentration within the first and second static pressure chambers.

[0083] In this embodiment, a displacement sensor is also provided. When the first and second test sections completely cover both sides of the filter under test, the displacement sensor is disposed in the first and second static pressure chambers to detect the position of the sealed airbag 1 in its corresponding static pressure chamber. The displacement sensor has a measurement range of 0 mm to 500 mm and an accuracy of ±0.1 mm.

[0084] Specifically, the method in this embodiment of the disclosure is executed as follows:

[0085] S1. Under standard environmental conditions, i.e., temperature 25℃±2℃, humidity 50%±5%, and atmospheric pressure 101.3kPa±1kPa, record the pressure difference-flow characteristic curve of the filter under test. Specifically, adjust the output power through the frequency converter module to ensure the airflow rate entering the first test section is 0.1m. 3 / s to 1.5m 3 Within the range of / s, at 0.1m 3 The pressure difference across the filter is measured at each flow rate increment, and the pressure difference-flow characteristic curve is plotted and stored in the storage unit as a reference pressure difference curve.

[0086] S2. Under different pollution loads, collect the attenuation curves of pressure difference over time. Different concentrations of standard test dust are injected into the first static pressure chamber via a particulate injection module to simulate different pollution loads. Under each pollution load, the airflow rate is maintained at 1.0 m³ / s. 3 / s, record the pressure difference once every 1 minute, and collect the decay curve of the pressure difference over time.

[0087] S3. Measure the current environmental baseline values, including temperature, humidity, and atmospheric pressure. The first and second test units are connected via flanges and loaded onto opposite sides of the filter. A dynamic airflow is generated by driving the frequency converter module using a preset simulation mode. In this embodiment, the preset simulation mode is: wind speed varies sinusoidally within the range of 0.1 m / s to 1.5 m / s, with a period of 30 seconds. During the dynamic airflow generation process, the pressure difference and particle concentration difference under the current preset simulation mode are sampled at a sampling period of 1 second, and the time-domain and frequency-domain features within a preset time window are extracted. The preset time window is 60 seconds. The time-domain features include mean, variance, and peak value, while the frequency-domain features are extracted using a Fast Fourier Transform (FFT).

[0088] S4. Compare the collected pressure difference with the reference pressure difference curve in the storage unit. If the difference is within the preset range (in this embodiment, the preset range is ±5% of the reference pressure difference), it is determined that the pressure loss is normal; otherwise, proceed to the next step.

[0089] S5. When the difference value determined in step S4 exceeds the preset range, the sealed airbag is controlled by the displacement sensor to move towards the edge of the static pressure chamber by a distance of 100mm to another detection position. Steps S2-S3 are repeated to measure the pressure difference and particle concentration difference in the unobstructed area, and to extract the time and frequency characteristics of the unobstructed area. If at least one area has a difference value within the preset range from the reference pressure difference curve, detection continues; if no area has a difference value within the preset range from the reference pressure difference curve, an alarm is triggered.

[0090] The disturbance airflow generation process generates dynamic airflow during the execution of the preset simulation mode. First, an initial airflow is generated under the standard pressure-flow characteristic curve, with a flow rate of 1.0 m³ / s. 3 / s, until the time and frequency domain characteristics are in a steady state (i.e., the change in the time domain characteristics is less than 1% and the change in the frequency domain characteristics is less than 5% within 10 consecutive sampling periods), a disturbance airflow with random amplitude is added. The amplitude range of the disturbance airflow is ±10% of the initial airflow rate, and the frequency is 1Hz.

[0091] Based on S5, local areas whose overlap with the baseline pressure difference curve exceeds a preset threshold (the preset threshold is 90% in this embodiment) are selected as effective filtration baseline areas. At the same time, pollution diffusion areas where the pressure difference continuously deviates from the baseline and the particle concentration difference exceeds a preset threshold (the preset threshold is 150% of the baseline particle concentration difference in this embodiment) are identified.

[0092] Sampling points were divided into equidistant grids within the pollution diffusion zone, with a grid spacing of 50 mm. The deviation rate between the real-time differential pressure and the reference differential pressure at each grid point was measured, and the upstream and downstream particle concentration ratios at the corresponding sampling points were detected.

[0093] Based on the spatial ratio of the pollution diffusion zone to the effective filtration baseline zone, the dust holding capacity index for the same spatial ratio in pre-stored historical data is retrieved. Dynamic weighting factors, positively correlated with deviation rate and concentration ratio, are then assigned to each sampling point in the pollution diffusion zone based on the dust holding capacity index. The formula for calculating the dynamic weighting factor is:

[0094] W=k1×P0ΔP+k2×CdCu

[0095] Where W is the dynamic weighting factor, k1 and

[0096] k2 is the weighting coefficient (k1 = 0.6, k2 = 0.4).

[0097] ΔP is the difference between the real-time differential pressure and the reference differential pressure.

[0098] P0 is the reference pressure difference.

[0099] Cu represents the upstream particle concentration.

[0100] Cd represents the downstream particle concentration.

[0101] The average dynamic weighting factor of the pollution diffusion zone is extracted, and the abrupt change points (abrupt changes exceeding 20%) of the average dynamic weighting factor within the sampling period are identified. Combining the weight change differences and time intervals between adjacent sampling periods, the predicted lifetime of each region from its current state to the weight abrupt change point is calculated. The formula for calculating the predicted lifetime is:

[0102] L=ΔWct×ΔWm

[0103] Where L is the predicted lifetime.

[0104] t is the time interval between adjacent sampling periods (1 minute in this embodiment).

[0105] ΔWm is the difference between the weight mutation point and the current weight.

[0106] ΔWc is the difference in weight changes between adjacent sampling periods.

[0107] Multiple verification points were randomly selected within the effective filtration baseline area and tracer particles (fluorescent particles) were injected. The proportion of tracer particles detected at another verification point within the effective filtration baseline area and in the contamination diffusion area was statistically analyzed to determine the penetration probability. When no tracer particles were detected in the area corresponding to the abrupt change time point, the interval between that abrupt change time point and the previous sampling period was used as the actual lifetime baseline value, and the estimated lifetime was adjusted based on the difference between the current weight of the remaining areas and the weight of the abrupt change point.

[0108] Based on the infiltration probability of the effective filtration benchmark area and the historical decay rate of the pre-stored dust holding capacity index, a negative correlation mapping relationship between the infiltration probability and the dust holding capacity index is established. Based on the current dust holding capacity index and infiltration probability, the probability of the effective filtration benchmark area transforming into a pollution diffusion area within a unit sampling period is predicted.

[0109] When the transition probability is lower than the preset risk threshold (the preset risk threshold is 20% in this embodiment), the predicted lifetime is extended based on the current estimated lifetime value and the rate of change of penetration probability in adjacent sampling periods. When the transition probability is higher than the preset risk threshold, the sub-region with the fastest weight change rate in the pollution diffusion zone is extracted, and the number of sampling periods from the baseline state to the current pollution state in this sub-region is used as the lifetime decay coefficient to shorten the overall predicted lifetime.

[0110] A monitoring point is added at the boundary between the pollution diffusion zone and the effective filtration reference zone. If the permeation probability of the boundary monitoring point increases by more than a preset value (15% in this embodiment) for a preset number of sampling cycles (5 in this embodiment), the correction result of the estimated lifespan is deemed invalid.

[0111] The corrected lifetime value of each sampling point is mapped to the three-dimensional spatial model of the filter to generate a lifetime distribution cloud map. When the area increase of the short lifetime warning area (the area with a lifetime of less than 100h) exceeds the preset threshold (the preset threshold is 20% in this embodiment) within a continuous detection cycle, an early warning is initiated.

[0112] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for descriptive purposes only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or,” as used herein, means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0113] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented using electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to achieve the described functions, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the described devices, apparatuses, and units can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0114] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, function, and operation of possible implementations of apparatus, methods, and computer program products according to embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than those disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based device that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A pressure loss dynamic detection system for panel air filters, characterized by, The application relates to a filter testing device, comprising: a wind channel module, comprising a first test part and a second test part which are detachably installed on two opposite sides of a filter to be tested, a first static pressure cavity is arranged in the first test part, and a second static pressure cavity is arranged in the second test part; a frequency conversion module which is connected to the first test part of the wind channel module through a wind pipe and is arranged to supply air to the first test part through the wind pipe at a plurality of variable powers; a differential pressure detection module, comprising a first pressure sensor and a second pressure sensor which are arranged in the first static pressure cavity and the second static pressure cavity respectively, and the first pressure sensor and the second pressure sensor are configured to sample pressure data in the corresponding static pressure cavity at a preset sampling period; an adjusting module, comprising a power adjusting unit, a data analysis unit, a storage unit and a feedback unit; the power adjusting unit is connected to the frequency conversion module and is configured to set the output power of the frequency conversion module according to a preset detection mode; the data analysis unit is configured to receive the pressure data and calculate the pressure difference between the first test part and the second test part; the storage unit is configured to store log data of sampling performed by the first pressure sensor and the second pressure sensor and a reference differential pressure curve; the feedback unit feeds back a signal to the power adjusting unit according to the difference between the pressure difference and the reference differential pressure curve to perform power adjustment on the frequency conversion module; the first test part and the second test part are arranged to completely wrap the two opposite sides of the filter to be tested, and movable closed air bags are arranged in the first static pressure cavity and the second static pressure cavity, and the internal pressure of the movable closed air bag is consistent with the pressure of the corresponding static pressure cavity; when the first test part and the second test part completely wrap the two sides of the filter to be tested, displacement sensors are arranged in the first static pressure cavity and the second static pressure cavity, and the displacement sensors are configured to detect the position of the closed air bag in the corresponding static pressure cavity; the first test part is further connected to a particle injection module, the particle injection module is configured to inject particles of a preset type into the first static pressure cavity at a preset particle concentration, and particle concentration detectors are arranged in the first static pressure cavity and the second static pressure cavity, and the particle concentration detectors are configured to detect the particle concentration in the first static pressure cavity and the second static pressure cavity.

2. A method of dynamically detecting pressure loss of a panel air filter, implemented based on the pressure loss dynamic detection system of a panel air filter according to claim 1, characterized in that, The application further relates to a filter testing method, comprising the following steps: S1, recording a pressure difference-flow characteristic curve of a filter to be tested under standard environmental conditions; S2, collecting a pressure difference-time attenuation curve under different pollution loads; S3, measuring a current environmental reference value, loading the first test part and the second test part on two opposite sides of the filter to be tested, driving the frequency conversion module to generate a dynamic air flow in a preset simulation mode, sampling a pressure difference and a particle concentration difference under the preset simulation mode, and extracting time domain features and frequency domain features in a preset time window; S4, comparing with a reference differential pressure curve, if the difference is within a preset range, judging that the pressure loss is normal, otherwise, performing the next step. S5, moving the closed air bag to another detection position, measuring the pressure difference and particle concentration difference of the non-occluded area, extracting the time domain feature and frequency domain feature of the non-occluded area, and repeating the step until there is at least one area whose difference with the reference pressure difference curve is within a preset range, initiating an alarm when there is no area whose difference with the reference pressure difference curve is within the preset range.

3. The method of claim 2, wherein, Further comprising the following steps: when the preset simulation mode is executed, generating a dynamic air flow, under a standard pressure difference-flow characteristic curve, generating an initial air flow to the time domain and the frequency domain features are in a steady state, increasing a disturbance air flow with a random amplitude.

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