One-stop offline detection method for an electric air damper

Abstract

The present invention discloses a one-stop offline detection method for an electric air damper, which relates to the technical field of electric air damper detection and includes the following steps: At the beginning of the detection, the ultrasonic sensor comprehensively detects the sealing performance of the electric air damper with the initial sensitivity and real-time collects the leakage signal data of the air damper; preprocesses the obtained leakage signal data, and aggregates the preprocessed data to establish a data set. By implementing the initial sensitivity detection, data preprocessing and feature extraction, intelligent evaluation, and sensitivity dynamic adjustment, the system can accurately identify the leakage signals that actually affect the performance of the air damper and avoid misjudgment of the minor leakage caused by environmental fluctuations. This method reduces false alarms and unnecessary repairs, reduces resource waste and production downtime, and at the same time improves the reliability and flexibility of the detection system. By optimizing the maintenance cycle and improving production efficiency, the economic benefits are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric damper detection, and particularly relates to a one-stop offline detection method for an electric damper. Background Art

[0002] The one-stop offline detection of an electric damper refers to the quality inspection stage before leaving the factory after the electric damper is manufactured. An integrated detection platform is used to perform an integrated detection operation on the damper system in the whole process and with all functions. This method usually integrates multiple detection links such as electrical control, sensor response, communication protocol, linkage mechanism, and action execution. Through a unified test interface and an intelligent control system, the automated detection and recording of key performance parameters such as damper opening and closing control, electric actuator response, sensor status feedback, fault alarm function, and remote communication ability are completed at one detection station, ensuring that the damper system meets the factory operation standard, reducing the labor input and the risks of missed detection and false detection caused by decentralized detection, and improving the product offline efficiency and consistency.

[0003] In the one-stop offline detection process of an electric damper, the ultrasonic leak detection method is one of the important means to detect the sealing performance of the electric damper. This method uses an ultrasonic sensor to perform highly sensitive detection on the damper sealing surface, and can capture ultrasonic signals caused by gas leakage in real time. Its function is to accurately measure the integrity of the damper sealing performance, timely identify possible leakage points, and evaluate the severity of the leakage. The ultrasonic detection method can efficiently detect tiny leaks in the damper sealing part without contact, and is particularly sensitive to the detection of tiny leaks. Therefore, it can ensure that the sealing performance of the damper meets the standard requirements before leaving the factory, thereby preventing energy waste, uneven air flow, or safety hazards caused by leakage.

[0004] The prior art has the following deficiencies: When using the ultrasonic leak detection method to detect the sealing performance of an electric damper, there may be a problem of over-sensitivity, that is, the ultrasonic sensor responds to tiny and almost negligible leakage signals. These tiny leakage signals often originate from natural fluctuations caused by environmental factors (such as temperature changes, humidity fluctuations, etc.), and these leaks do not have a significant impact on the performance of the damper in actual applications, especially there is no substantial harm to the airtightness, ventilation effect, or safety of the working environment of the damper.

[0005] Due to the high sensitivity of the ultrasonic sensor, it can detect these tiny leakage signals and then mark them as potential leakage problems in the detection report. However, the air flow generated by these tiny leaks is extremely limited and is not sufficient to affect the actual use performance of the damper. Therefore, the over-sensitive detection results may lead to unnecessary repairs or adjustments to the damper, thus wasting resources, increasing maintenance costs, and prolonging the downtime of the production line.

[0006] The above information disclosed in the background section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0007] The object of the present invention is to provide a one-stop offline detection method for an electric air damper. By implementing initial sensitivity detection, data preprocessing and feature extraction, intelligent evaluation, and sensitivity dynamic adjustment, the system can accurately identify leakage signals that actually affect the performance of the air damper and avoid misjudgment of minor leaks caused by environmental fluctuations. This method reduces false alarms and unnecessary repairs, reduces resource waste and production downtime, and at the same time improves the reliability and flexibility of the detection system. By optimizing the maintenance cycle and improving production efficiency, the economic benefits are significantly improved to solve the problems in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: A one-stop offline detection method for an electric air damper, comprising the following steps:

[0009] At the beginning of the detection, the ultrasonic sensor comprehensively detects the sealing performance of the electric air damper with the initial sensitivity and real-time collects the leakage signal data of the air damper;

[0010] Preprocess the obtained leakage signal data and aggregate the preprocessed data to establish a data set;

[0011] Extract from the data set the key indicators reflecting that the leakage is caused by natural fluctuations of environmental factors through feature engineering, comprehensively analyze the extracted key indicators, quantify the severity of the leakage, and evaluate the actual impact of the leakage on the performance of the air damper;

[0012] Input the key indicators after comprehensive analysis into a pre-trained machine learning model, and through the machine learning model, conduct intelligent evaluation of the leakage to determine whether the leakage belongs to an actual airtightness problem;

[0013] When it is identified that the leakage signal is a negligible leakage, based on the evaluation result, reduce the sensitivity of the sensor to shield minor and non-essential leakage signals.

[0014] Preferably, the specific steps of preprocessing the data and establishing a data set are as follows:

[0015] First, perform denoising processing on the obtained leakage signal data, and use filtering technology to remove the noise components in the signal;

[0016] Next, convert the data in different time periods into a unified standard through data normalization to eliminate the influence of unit and magnitude differences on the analysis results;

[0017] Then, apply a smoothing algorithm to smooth the signal to reduce interference caused by instantaneous fluctuations.

[0018] For signals with outliers, use an outlier detection method to eliminate or correct them.

[0019] After completing the preprocessing steps, aggregate the leakage signal data for each time period and environmental condition, and integrate the data into a data set according to the dimensions of time, damper working status, and environmental factors to ensure the integrity and consistency of the data, providing accurate and stable basic data for subsequent analysis and modeling.

[0020] Preferably, extract key indicators from the data set through feature engineering that reflect the natural fluctuations of leakage caused by environmental factors. The extracted key indicators include the impact of air pressure changes on the damper seal and the impact of stress changes in the damper seal material under temperature changes on the seal. Comprehensively analyze the impact of air pressure changes on the damper seal and the impact of stress changes in the damper seal material under temperature changes on the seal under the detection window, and generate an air pressure change response reference value and a temperature sensitivity stress reference value respectively. Quantify the severity of leakage through the air pressure change response reference value and the temperature sensitivity stress reference value, and evaluate the actual impact of leakage on the damper performance.

[0021] Preferably, the specific steps for comprehensively analyzing the impact of air pressure changes on the damper seal under the detection window to generate an air pressure change response reference value are as follows:

[0022] First, collect the time series of air pressure change data and the leakage signal of the electric damper, establish the relationship between air pressure changes and the leakage signal, and quantitatively describe the impact of air pressure changes on the damper seal by introducing an air pressure-leakage difference index. The calculation expression of the air pressure-leakage difference index is as follows: , where is the air pressure-leakage difference index, and are the air pressure values at time point and time point respectively, and are the leakage signal values at time point and time point respectively, is the total number of time points;

[0023] After obtaining the air pressure-leakage difference index, generate an air pressure change response reference value through the air pressure-leakage difference index. The generation formula is as follows: , where is time point The air pressure-leakage difference index calculated at a moment is the reference value of the air pressure change response.

[0024] Preferably, the specific steps for comprehensively analyzing the influence of the stress change of the damper sealing material under temperature change on the sealing performance to generate the temperature-sensitive stress reference value are as follows:

[0025] First, analyze the stress change of the electric damper sealing material under temperature change, and then calculate the stress change amount caused by temperature. It is assumed that the volume change of the sealing material caused by temperature change has a direct impact on the sealing performance. The calculation expression of the stress change amount is as follows: , where is the stress change amount, is the elastic modulus of the damper sealing material, is the strain change amount, and its calculation formula is: , where is the length change of the sealing material caused by temperature change, is the initial length of the sealing material;

[0026] Based on the stress change amount , combined with the amplitude of temperature change, generate the temperature-sensitive stress reference value. The calculation expression is as follows: , where is the temperature-sensitive stress reference value, is the amplitude of temperature change, is the reference temperature.

[0027] Preferably, input the air pressure change response reference value and the temperature-sensitive stress reference value after comprehensive analysis into a pre-trained machine learning model. Generate the micro-leakage change coefficient through the machine learning model, and use the micro-leakage change coefficient to intelligently evaluate the leakage and determine whether the leakage belongs to the actual airtightness problem.

[0028] Preferably, compare and analyze the micro-leakage change coefficient with a pre-set micro-leakage change coefficient reference threshold to determine whether the leakage belongs to the actual airtightness problem. The judgment logic is as follows:

[0029] If the micro-leakage change coefficient is greater than the pre-set micro-leakage change coefficient reference threshold, it is determined that the leakage is a natural fluctuation caused by environmental factors; if the micro-leakage change coefficient is less than or equal to the pre-set micro-leakage change coefficient reference threshold, it is determined that the leakage is not a natural fluctuation caused by environmental factors.

[0030] Preferably, when the leakage signal is identified as negligible leakage, based on the evaluation result, the specific steps to reduce the sensitivity of the sensor to shield small and non-essential leakage signals are as follows:

[0031] Once the leakage signal is identified as natural fluctuations caused by environmental factors and judged as negligible leakage, the sensitivity of the sensor will be reduced based on the evaluation result. By reducing the sensitivity of the sensor, tiny and non-essential leakage signals will be automatically blocked, avoiding interference with the detection result. The adjustment formula is as follows: , where is the adjusted sensor sensitivity, is the initial sensor sensitivity, is the sensitivity adjustment coefficient, controlling the rate of sensitivity decline, is the micro-leakage change coefficient, is the reference threshold of the micro-leakage change coefficient, is the non-linear exponential factor, enhancing the non-linear response effect of sensitivity adjustment, is the sensitivity correction factor.

[0032] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0033] By implementing a comprehensive solution of initial sensitivity detection, data preprocessing and feature extraction, intelligent evaluation and dynamic sensitivity adjustment, the system can accurately identify and process leakage signals that only have a substantial impact on the performance of the air damper, thus effectively avoiding misjudgment of tiny natural fluctuation signals caused by environmental factors (such as temperature fluctuations, humidity changes, etc.). This method significantly improves the detection accuracy, reduces unnecessary false alarms and maintenance operations, avoids waste of resources and unnecessary shutdown of the production line. At the same time, the sensitivity adjustment mechanism ensures that the system can adapt to different environmental conditions, improving the flexibility and adaptability of the detection system. Ultimately, this not only improves the overall reliability of the system, reduces the maintenance cost, but also realizes significant economic benefits by optimizing the maintenance cycle and improving the production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0035] Figure 1 is the method flow chart of a one-stop offline detection method for an electric air damper of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0037] The present invention provides Figure 1 The one-stop offline detection method for an electric damper shown in the figure includes the following steps:

[0038] At the beginning of the test, the ultrasonic sensor performs a comprehensive test of the electric damper's sealing performance at its initial sensitivity and collects the damper's leakage signal data in real time;

[0039] By setting the initial sensitivity, the sensor captures all signals, regardless of their intensity, providing the raw data foundation for subsequent analysis. This step ensures comprehensive data collection, providing sufficient information for subsequent data processing and analysis, and minimizing the risk of missing any potential leakage signals.

[0040] The sensor collects real-time leakage signal data from the damper, covering all possible leaks, including small leaks and natural fluctuations caused by environmental factors. The initial sensitivity setting ensures that the sensor responds to a wide range of leak conditions to collect complete leakage data.

[0041] Preprocessing the acquired leakage signal data and aggregating the preprocessed data to establish a data set;

[0042] Data preprocessing, including signal denoising, normalization, smoothing filtering, and outlier correction, removes noise and external interference from the original signal to ensure the accuracy of subsequent analysis and data stability.

[0043] Data preprocessing eliminates interference signals caused by environmental noise, sensor errors, or irrelevant factors, ensuring data quality. This step reduces errors caused by signal fluctuations and provides accurate and stable data for subsequent key indicator extraction and analysis. The goal of this stage is to remove irrelevant data and improve detection accuracy.

[0044] The data set contains leakage signal data from different times and different environmental conditions, which will serve as the basis for further analysis.

[0045] The establishment of the data set can summarize the leakage information in multiple detections and take into account the variations under different working conditions. By correlating the leakage signals with environmental parameters (such as temperature, humidity) and factors such as the damper operation status, a complete historical data set can be established, and potential relationships between the data can be identified in subsequent analysis. This provides multi-dimensional information support for extracting key indicators in the future.

[0046] The specific steps for preprocessing the data and establishing the data set are as follows:

[0047] First, denoise the obtained leakage signal data, and use filtering techniques to remove the noise components in the signal, such as electromagnetic interference, sensor errors, or external environmental noise. Next, convert the data in different time periods into a unified standard through data normalization to eliminate the influence of unit and magnitude differences on the analysis results. Then, apply a smoothing algorithm (such as the moving average method or Gaussian smoothing) to smooth the signal to reduce the interference caused by instantaneous fluctuations. For signals with outliers, use outlier detection methods to eliminate or correct them. After completing the preprocessing steps, aggregate the leakage signal data in each time period and environmental condition, and integrate the data into a data set according to dimensions such as time, damper working status, and environmental factors to ensure the integrity and consistency of the data, providing accurate and stable basic data for subsequent analysis and modeling.

[0048] Extract key indicators from the data set through feature engineering that reflect the natural fluctuations of the leakage caused by environmental factors (such as temperature changes, humidity fluctuations, etc.), comprehensively analyze the extracted key indicators, quantify the severity of the leakage, and evaluate the actual impact of the leakage on the damper performance;

[0049] Extract key indicators from the data set through feature engineering that reflect the natural fluctuations of the leakage caused by environmental factors. The extracted key indicators include the impact of air pressure changes on the damper sealing performance and the impact of stress changes of the damper sealing material under temperature changes on the sealing performance. Comprehensively analyze the impact of air pressure changes on the damper sealing performance and the impact of stress changes of the damper sealing material under temperature changes on the sealing performance under the detection window, and generate the air pressure change response reference value and the temperature sensitivity stress reference value respectively. Quantify the severity of the leakage through the air pressure change response reference value and the temperature sensitivity stress reference value, and evaluate the actual impact of the leakage on the damper performance.

[0050] When the air pressure change has a significant impact on the air damper's sealing performance, it usually indicates that the leakage is a natural fluctuation caused by environmental factors. This is because the sealing performance of the air damper is not only affected by its structural design and the quality of the sealing material, but also highly dependent on the physical parameters of the environment it is in, especially the air pressure. The change in air pressure will cause the pressure difference on both sides of the air damper to fluctuate, thus changing the stress state of the sealing strip or the contact surface, which may lead to short-term and minor leakage phenomena. Especially in high-altitude areas, underground, or places with frequent climate fluctuations, the drastic change in air pressure may cause a temporary imbalance in the tension of the sealing surface, resulting in the generation of reversible leakage signals. Such leakage signals often have the characteristics of suddenness and short duration, and will automatically recover as the air pressure stabilizes. Usually, they will not cause long-term or substantial impacts on the actual operating performance and ventilation efficiency of the air damper. Therefore, if the leakage phenomenon is highly correlated with the air pressure change and the leakage signal has environmental response characteristics (such as being consistent with the trend of the air pressure curve and having no continuous growth trend), it can be determined that it is a natural fluctuation driven by the environment, different from the essential leakage caused by structural damage or component failure.

[0051] The specific steps for comprehensively analyzing the impact of air pressure change on the air damper's sealing performance under the detection window to generate the air pressure change response reference value are as follows:

[0052] First, collect the time series of air pressure change data and the leakage signal of the electric air damper, establish the relationship between the air pressure change and the leakage signal, and quantitatively describe the impact of air pressure change on the air damper's sealing performance by introducing the air pressure-leakage difference index. The calculation expression of the air pressure-leakage difference index is as follows: , where is the air pressure-leakage difference index, which quantifies the relationship between the air pressure change and the leakage signal change, and helps to judge whether the air damper leakage is affected by the air pressure change. and are the air pressure values at time point and time point respectively. and are the leakage signal values at time point [[ID=Z4]]and time point respectively. is the total number of time points;

[0053] The function of this step is to quantify the correlation between the two by combining the change amounts of air pressure and leakage signal. If the air pressure change is significant and consistent with the leakage signal change, it indicates that the leakage may be a natural fluctuation caused by environmental factors.

[0054] After obtaining the air pressure-leakage difference index, generate the air pressure change response reference value through the air pressure-leakage difference index. The air pressure change response reference value can accurately reflect whether the leakage is a natural fluctuation caused by air pressure change. The generation formula is as follows: , where is the time point at which the air pressure - leakage difference index is calculated, and

[0055] is the reference value of the air pressure change response.

[0055] This formula generates by performing a weighted sum of and the change amount of the leakage signal, reflecting the influence degree of the air pressure change on the leakage signal. When has a large value, it indicates that the air pressure change has a significant impact on the air door sealing performance, and most of the leakage signals are natural fluctuations caused by the ambient air pressure fluctuations; if has a small value, it means that the leakage is more likely to come from the structural problems of the air door itself.

[0056] By generating in this step, it can intelligently determine whether the leakage signal is a natural fluctuation caused by the air pressure change. If has a high value, most of the leakage is caused by natural fluctuations; conversely, if is low, the leakage signal may be due to defects in the air door structure or other non - environmental factors.

[0057] The larger the reference value of the air pressure change response generated by comprehensively analyzing the influence of the air pressure change on the air door sealing performance under the detection window, the higher the correlation between the leakage signal and the air pressure change, that is, the leakage intensity changes synchronously with the air pressure fluctuation, indicating that the leakage may be a natural fluctuation caused by the ambient air pressure change, rather than the failure of the air door structure or sealing material; the leakage at this time usually has reversibility and non - persistence. When this reference value is low or the change trend has no obvious corresponding relationship with the air pressure, it means that the leakage signal is less affected by the air pressure change, and the leakage cause is more likely to come from essential problems such as sealing structure defects or mechanical wear.

[0058] When the stress of the damper sealing material changes due to temperature variations, thereby having a fluctuating impact on the sealing performance, it generally indicates that this type of leakage belongs to the natural fluctuations caused by environmental factors. This is because sealing materials (such as rubber, composite elastomers, or metal gaskets) may experience thermal expansion when the temperature rises and may contract or harden in a low-temperature environment. This slight dimensional change or decrease in contact compression force caused by the change in the thermal physical properties of the material will lead to slight gas leakage at the sealing surface in a short period. However, this type of leakage is not caused by structural defects or poor assembly of the damper, but rather the natural response behavior of the sealing material to external thermal environment changes. Usually, as the temperature stabilizes or the environmental fluctuations return to normal, the material will gradually return to its original form, and the sealing performance will also recover automatically. Therefore, this type of leakage has the characteristics of reversibility, periodicity, and non-essentiality. From the perspective of the leakage source and cause, this behavior directly stems from environmental thermal disturbances rather than the stability problem of the damper system itself. Therefore, it is reasonably classified as natural fluctuation-type leakage caused by environmental factors.

[0059] The specific steps for comprehensively analyzing the impact of the stress change of the damper sealing material on the sealing performance under temperature variations in the detection window to generate a temperature-sensitive stress reference value are as follows:

[0060] First, analyze the stress change of the electric damper sealing material under temperature variations, and then calculate the stress change amount caused by temperature. Assume that the volume change of the sealing material caused by temperature change has a direct impact on the sealing performance. The calculation expression for the stress change amount is as follows: , where is the stress change amount, representing the stress change of the damper sealing material caused by temperature change, is the elastic modulus of the damper sealing material, also known as Young's modulus, which is the stiffness coefficient of the material and is defined as the strain degree of the material under unit stress, is the strain change amount, representing the deformation degree of the material caused by external force or temperature change. Its calculation formula is: , where is the length change of the sealing material caused by temperature change (unit: meter), is the initial length of the sealing material (unit: meter);

[0061] By measuring the deformation caused by temperature change ( ) and the elastic characteristics of the sealing material ( ), the stress change amount caused by temperature can be obtained. This stress change amount can be used to characterize the impact of temperature change on the damper sealing performance. A larger stress change value indicates a greater impact of temperature change on the sealing performance, and vice versa.

[0062] Based on the stress change amount , combined with the amplitude of temperature change, generate a temperature-sensitive stress reference value, and the calculation expression is as follows:

[0063] , where is the temperature-sensitive stress reference value, is the amplitude of temperature change, which represents the degree of temperature change in the environment where the air damper is located during the measurement, referring to the change amount of the ambient temperature, usually caused by factors such as temperature fluctuations outside the air damper, seasonal changes, and equipment heat, is the reference temperature, which is usually selected as the standard working temperature of the air damper sealing material.

[0064] By calculating the temperature-sensitive stress reference value , quantify the influence of temperature change on the stress change of the air damper sealing material, so as to evaluate the influence degree of temperature fluctuation on the sealing performance. This step can identify the natural fluctuation type of leakage caused by temperature change and distinguish the actual faulty leakage that has nothing to do with environmental factors.

[0065] The greater the temperature-sensitive stress reference value generated after comprehensively analyzing the influence of the stress change of the air damper sealing material on the sealing performance under the detection window due to temperature change, it indicates that the sealing material is highly sensitive to the current temperature change and significant stress fluctuations have occurred. This kind of fluctuation further leads to the fluctuation of the short-term leakage signal. Therefore, the leakage is more likely to be the natural fluctuation caused by environmental temperature change rather than the structural or functional failure of the air damper. On the contrary, if this reference value is low, that is, the stress response of the sealing material to temperature change is weak, it indicates that the leakage change has a weak correlation with temperature. At this time, it is more likely to be a substantial leakage caused by non-environmental factors (such as seal aging, assembly deviation, structural wear, etc.).

[0066] Input the key indicators after comprehensive analysis into a pre-trained machine learning model, and through the machine learning model, conduct an intelligent evaluation of the leakage to determine whether the leakage belongs to the actual airtightness problem;

[0067] Input the air pressure change response reference value and the temperature-sensitive stress reference value after comprehensive analysis into a pre-trained machine learning model, generate a micro-leakage change coefficient through the machine learning model, and conduct an intelligent evaluation of the leakage through the micro-leakage change coefficient to determine whether the leakage belongs to the actual airtightness problem.

[0068] A pre-trained machine learning model refers to a predictive classification model constructed for the task of detecting electric damper leakage, based on a large amount of historical detection data, environmental monitoring data, and actual leakage results, through supervised learning or semi-supervised learning methods. During the training phase of this model, by inputting multiple characteristic parameters such as the reference value of air pressure change response, temperature sensitivity stress reference value, signal fluctuation frequency, leakage duration, etc., it conducts mapping learning with its corresponding actual leakage categories (such as "negligible leakage" or "structural airtightness problem") to form a model structure with discriminative ability. After training, the model can automatically determine whether the input signal characteristics conform to a certain leakage pattern when facing new detection data, and accordingly output a micro-leakage change coefficient reflecting the risk level, which is used to measure the severity of the current leakage signal and its relationship with the natural fluctuations of the environment.

[0069] The so-called "pre-trained" means that the model has completed the modeling process through an offline data set before system deployment, and has been iteratively updated through methods such as cross-validation, accuracy optimization, and error correction to ensure its sufficient generalization ability and prediction accuracy. In practical applications, when the detection system extracts multi-dimensional indicators such as the reference value of air pressure change response and temperature sensitivity stress reference value, these features are fed into the model as inputs, and the model will make intelligent inferences based on its existing knowledge system and output a quantitative result - the micro-leakage change coefficient. The larger the value of this coefficient, the more highly correlated the current leakage signal is with environmental disturbances, and the more inclined it is to be judged as natural fluctuations; conversely, if the value is low, it indicates that there may be a structural airtightness problem with the damper, and further inspection or early warning is required. Therefore, the core role of this machine learning model is to improve the intelligence level of leakage classification, reduce misjudgments and missed judgments, and enhance the adaptive decision-making ability of the detection system.

[0070] The machine learning model is not limited here, and any machine learning model that can comprehensively analyze the reference value of air pressure change response and the reference value of temperature sensitivity stress to generate a micro-leakage change coefficient can be used. To implement the technical solution of the present invention, the present invention provides a specific implementation method:

[0071] The micro-leakage change coefficient is generated according to the following formula: , where and are respectively the preset proportional coefficients of the reference value of air pressure change response and the reference value of temperature sensitivity stress , and and are both greater than 0.

[0072] The preset proportional coefficient refers to when calculating the micro-leakage change coefficient When adjusting each input variable (i.e., the reference value of the air pressure change response and the reference value of the temperature-sensitive stress ), the coefficients for weighting the influence of these on the final result are denoted as and . These two coefficients are artificially preset parameters in practical applications and are used to control the contribution degree of each physical quantity in the model. For example, if the temperature has a more significant impact on the micro-leakage under a certain working condition, then can be set to strengthen the proportion of the reference value of the temperature-sensitive stress in the calculation.

[0073] These proportional coefficients have two core functions: one is to reflect the sensitivity of different physical factors to the micro-leakage fluctuation according to experience or model training results, and the other is to provide a quantification method of linear combination for simplified processing when deep learning modeling is not carried out. Although they are fixed values, they can be tuned through experimental data to adapt to the response characteristics of different damper materials, environments, and sealing structures to the leakage behavior. Therefore, the "preset proportional coefficient" is essentially a weight control mechanism with physical significance, which is used to ensure that the MLVC is more accurate and representative in actual judgment.

[0074] From the micro-leakage change coefficient, the larger the reference value of the air pressure change response generated by comprehensively analyzing the influence of the air pressure change on the damper sealing performance under the detection window, and the larger the reference value of the temperature-sensitive stress generated by comprehensively analyzing the influence of the stress change of the damper sealing material under temperature change on the sealing performance under the detection window, the larger the micro-leakage change coefficient generated by the intelligent evaluation of the leakage through the pre-trained machine learning model when judging whether the leakage belongs to the actual airtightness problem, indicating that the probability of the leakage being caused by natural fluctuations of environmental factors is greater; on the contrary, it indicates that the probability of the leakage being caused by natural fluctuations of environmental factors is smaller.

[0075] Compare and analyze the micro-leakage change coefficient with the pre-set reference threshold of the micro-leakage change coefficient to judge whether the leakage belongs to the actual airtightness problem. The judgment logic is as follows:

[0076] If the micro-leakage change coefficient is greater than the pre-set reference threshold of the micro-leakage change coefficient, it is judged that the leakage is a natural fluctuation caused by environmental factors; if the micro-leakage change coefficient is less than or equal to the pre-set reference threshold of the micro-leakage change coefficient, it is judged that the leakage is not a natural fluctuation caused by environmental factors.

[0077] When the identified leakage signal is a negligible leakage, based on the evaluation result, reduce the sensitivity of the sensor to shield small and non-essential leakage signals.

[0078] When it is recognized that the leakage signal is a negligible leakage, reducing the sensitivity of the sensor is to shield those tiny and non-essential leakage signals caused by environmental factors and prevent them from affecting the subsequent detection process and judgment. The main function of this step is to reduce false alarms and unnecessary maintenance operations caused by over-sensitivity, thereby improving the efficiency and accuracy of the detection system.

[0079] The high sensitivity of the ultrasonic leakage detection system enables it to capture extremely weak signals, including natural fluctuations caused by environmental factors such as temperature changes, humidity fluctuations, or external air pressure changes. Although these tiny fluctuations can be detected by the sensor, they do not have an actual impact on the airtightness, ventilation effect, or safety of the air damper. Therefore, if not controlled, these tiny signals may be misjudged as leaks that need to be repaired, resulting in unnecessary maintenance, adjustment, or replacement of components in the system.

[0080] By reducing the sensor sensitivity, the system can automatically ignore these tiny leakage signals and reduce their interference with the detection results. In this way, the sensor will only respond to those leakage signals that truly have an impact, ensuring more accurate detection results. This measure not only avoids over-reaction but also significantly reduces the frequency of equipment maintenance and production downtime, thereby improving the overall production efficiency and saving maintenance costs.

[0081] When it is recognized that the leakage signal is a negligible leakage, based on the evaluation results, the specific steps to reduce the sensitivity of the sensor to shield tiny and non-essential leakage signals are as follows:

[0082] Once it is recognized that the leakage signal is a natural fluctuation caused by environmental factors and judged to be a negligible leakage, the sensitivity of the sensor will be reduced based on the evaluation results. By reducing the sensitivity of the sensor, tiny and non-essential leakage signals will be automatically shielded, avoiding interference with the detection results. The adjustment formula is as follows: , where is the adjusted sensor sensitivity, is the initial sensor sensitivity, is the sensitivity adjustment coefficient, which controls the rate of sensitivity decrease ( ), and is usually adjusted according to actual requirements, is the micro-leakage change coefficient, is the reference threshold of the micro-leakage change coefficient, is the non-linear exponential factor, which enhances the non-linear response effect of sensitivity adjustment, making the impact of micro-leakage signals on sensitivity more significant, , The choice of is the sensitivity correction factor, representing the "hysteresis" adjustment of the sensor's response to minute leakage signals, and controlling the final adjustment result of the sensitivity.

[0083] By dynamically adjusting the sensitivity of the ultrasonic sensor, overreaction to minute leakage signals with no substantial impact is avoided, thereby reducing unnecessary false alarms and repairs. This sensitivity adjustment mechanism ensures that only leakage signals that actually affect the airtightness of the air damper and the ventilation effect can trigger a system response, improving the accuracy and efficiency of detection.

[0084] Through the implementation of a comprehensive solution involving initial sensitivity detection, data preprocessing and feature extraction, intelligent evaluation, and dynamic sensitivity adjustment, the system of the present invention can accurately identify and process leakage signals that only have a substantial impact on the performance of the air damper, thus effectively avoiding misjudgment of minute natural fluctuation signals caused by environmental factors (such as temperature fluctuations, humidity changes, etc.). This method significantly improves the detection accuracy, reduces unnecessary false alarms and maintenance operations, avoids waste of resources and unnecessary downtime of the production line. At the same time, the sensitivity adjustment mechanism ensures that the system can adapt to different environmental conditions, improving the flexibility and adaptability of the detection system. Ultimately, this not only enhances the overall reliability of the system, reduces the maintenance cost, but also realizes significant economic benefits by optimizing the maintenance cycle and improving the production efficiency.

[0085] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0086] Only some exemplary embodiments of the present invention have been described above by way of illustration. Undoubtedly, for those of ordinary skill in the art, without departing from the spirit and scope of the present invention, the described embodiments can be modified in various different ways. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

[0087] It should be noted that in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0088] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not imply the order of execution, and the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0089] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0090] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0091] The unit described as a separate component may or may not be physically separated, and the component shown as a unit may or may not be a physical unit, that is, it may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0092] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0093] As described above, this is only the specific implementation manner of the present application. However, the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims described above.

[0094] Only some exemplary embodiments of the present invention have been described above by way of illustration. Without doubt, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

Claims

1. A one-stop offline detection method for an electric air damper, characterized in that, It includes the following steps: At the beginning of the detection, the ultrasonic sensor comprehensively detects the sealing performance of the motorized damper with the initial sensitivity and collects the leakage signal data of the damper in real time; Preprocess the obtained leakage signal data and aggregate the preprocessed data to establish a data set; Extract the key indicators reflecting that the leakage is the natural fluctuation caused by environmental factors from the data set, comprehensively analyze the extracted key indicators, quantify the severity of the leakage, and evaluate the actual impact of the leakage on the damper performance; Input the key indicators after comprehensive analysis into a pre-trained machine learning model, and use the machine learning model to intelligently evaluate the leakage to determine whether the leakage belongs to the actual airtightness problem; When the leakage signal is identified as a negligible leakage, based on the evaluation result, reduce the sensitivity of the sensor to shield tiny and non-essential leakage signals; Extract the key indicators reflecting that the leakage is the natural fluctuation caused by environmental factors from the data set. The extracted key indicators include the influence of air pressure change on the damper sealing performance and the influence of the stress change of the damper sealing material under temperature change on the sealing performance. Comprehensively analyze the influence of air pressure change on the damper sealing performance and the influence of the stress change of the damper sealing material under temperature change on the sealing performance under the detection window, and generate the air pressure change response reference value and the temperature sensitivity stress reference value respectively. Quantify the severity of the leakage through the air pressure change response reference value and the temperature sensitivity stress reference value, and evaluate the actual impact of the leakage on the damper performance; The specific steps for comprehensively analyzing the influence of air pressure change on the damper sealing performance under the detection window to generate the air pressure change response reference value are as follows: First, collect the time series of air pressure change data and the motorized damper leakage signal, establish the relationship between air pressure change and leakage signal, and quantitatively describe the influence of air pressure change on the damper sealing performance by introducing the air pressure-leakage difference index. The calculation expression of the air pressure-leakage difference index is as follows: , where is the pressure-leakage difference index, and are the air pressure values at time points and respectively, and are the leakage signal values at time points and respectively, is the total number of time points; After obtaining the air pressure-leakage difference index, generate the air pressure change response reference value through the air pressure-leakage difference index. The generation formula is as follows: , where is the time point The air pressure-leakage difference index calculated at the moment, is the reference value of the air pressure change response.

2. The one-stop offline detection method for an electric air damper according to claim 1, characterized in that The specific steps for preprocessing data and establishing a data set are as follows: First, perform denoising processing on the obtained leakage signal data, and use filtering technology to remove the noise components in the signal; Next, convert the data in different time periods into a unified standard through data normalization to eliminate the influence of unit and magnitude differences on the analysis results; Then, apply a smoothing algorithm to smooth the signal to reduce the interference caused by instantaneous fluctuations; For signals with outliers, use outlier detection methods to eliminate or correct them; After completing the preprocessing steps, aggregate the leakage signal data in each time period and environmental condition, and integrate the data into a data set according to the dimensions of time, damper working state, and environmental factors to ensure the integrity and consistency of the data, and provide accurate and stable basic data for subsequent analysis and modeling.

3. The one-stop offline detection method for an electric air damper according to claim 1, wherein The specific steps for comprehensively analyzing the influence of the stress change of the damper sealing material under temperature change on the sealing performance under the detection window to generate the temperature sensitivity stress reference value are as follows: First, analyze the stress change of the electric damper sealing material under temperature change, and then calculate the stress change amount caused by temperature. Assume that the volume change of the sealing material caused by temperature change has a direct impact on the sealing performance. The calculation expression of the stress change amount is as follows: , where is the stress change amount, is the elastic modulus of the damper sealing material, is the strain change amount, and its calculation formula is: , where is the length change of the sealing material caused by temperature change, is the initial length of the sealing material; Based on the stress change amount , combined with the amplitude of the temperature change, generate a temperature-sensitive stress reference value, and the calculation expression is as follows: , where is the temperature-sensitive stress reference value, is the temperature change range, is the reference temperature.

4. The one-stop offline detection method for an electric air damper according to claim 1, wherein Input the reference value of the air pressure change response and the reference value of the temperature-sensitive stress after comprehensive analysis into a pre-trained machine learning model. Generate a micro-leakage change coefficient through the machine learning model, and use the micro-leakage change coefficient to intelligently evaluate the leakage to determine whether the leakage belongs to an actual airtightness problem.

5. The one-stop offline detection method for an electric air damper according to claim 4, characterized in that, Compare and analyze the micro-leakage change coefficient with a pre-set reference threshold of the micro-leakage change coefficient to determine whether the leakage belongs to an actual airtightness problem. The judgment logic is as follows: If the micro-leakage change coefficient is greater than the pre-set reference threshold of the micro-leakage change coefficient, it is judged that the leakage is a natural fluctuation caused by environmental factors; if the micro-leakage change coefficient is less than or equal to the pre-set reference threshold of the micro-leakage change coefficient, it is judged that the leakage is not a natural fluctuation caused by environmental factors.

6. A one-stop offline detection method for an electric air damper according to claim 5, characterized in that When it is recognized that the leakage signal is a negligible leakage, based on the evaluation result, the sensitivity of the sensor is reduced. The specific steps to shield tiny and non-essential leakage signals are as follows: Once it is recognized that the leakage signal is a natural fluctuation caused by environmental factors and is judged to be a negligible leakage, the sensitivity of the sensor will be reduced based on the evaluation result. By reducing the sensitivity of the sensor, tiny and non-essential leakage signals will be automatically shielded to avoid interfering with the detection result. The adjustment formula is as follows: , where is the adjusted sensor sensitivity is the initial sensor sensitivity is the sensitivity adjustment coefficient, controlling the rate of sensitivity decrease is the micro-leakage change coefficient is the reference threshold of the micro-leakage change coefficient is the non-linear exponential factor, enhancing the non-linear response effect of sensitivity adjustment is the sensitivity correction factor

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