A method for multi-source feature extraction and structured expression of air conditioner heat exchanger corrosion data

Abstract

The present application belongs to the technical field of material corrosion data processing and industrial equipment state monitoring, and provides a kind of multi-source feature extraction and structured expression method of air conditioner heat exchanger corrosion data, specifically comprising the following steps: obtaining multi-source corrosion original data;Data preprocessing;Corrosion morphology feature extraction;Electrochemical feature extraction;Environmental influence feature extraction;Feature normalization processing;Structured feature vector construction;Form a structured feature vector with fixed dimension, unified expression. Each component in the feature vector corresponds to a corrosion-related parameter with clear physical meaning, completely describing the corrosion state, corrosion kinetics characteristics and service environment conditions of the heat exchanger under specific space-time conditions. Advantage: significantly improve the usability of data, provide an ideal data interface for multi-modal data fusion analysis and large model technology application, significantly improve the objectivity and reliability of the analysis results, have strong engineering practical value.

Description

Technical Field

[0001] This invention belongs to the field of material corrosion data processing and industrial equipment condition monitoring technology. Specifically, it relates to a multi-source heterogeneous data processing method for analyzing the corrosion behavior of heat exchangers in air conditioning equipment, and in particular, a feature extraction method that transforms raw data such as corrosion morphology images, electrochemical test data, and environmental monitoring data into structured feature vectors with physical meaning. Background Technology

[0002] During long-term service, the core heat exchange components of air conditioning equipment (including evaporators and condensers) are inevitably subjected to the coupled effects of multiple environmental factors such as high temperature, high humidity, high salt spray, and atmospheric pollutants, leading to corrosion failure. Accurately acquiring and quantifying the corrosion state of heat exchangers is fundamental to assessing equipment health, predicting remaining lifespan, and developing maintenance strategies. Currently, data characterizing heat exchanger corrosion behavior mainly exists in three forms: corrosion morphology image data, electrochemical test data, and environmental monitoring data.

[0003] Corrosion morphology images, typically obtained from optical microscopes, scanning electron microscopes, or on-site photography, can visually reflect the morphology, distribution, and severity of corrosion. However, they are inherently unstructured image information and cannot be directly used for quantitative analysis. Electrochemical test data, such as polarization curves and electrochemical impedance spectroscopy, contain rich information on corrosion kinetics, but they exist in the form of curves or discrete numerical values. The interpretation process relies on professional interpretation of the curves, which is highly subjective and inefficient. Environmental monitoring data records environmental parameters such as temperature, humidity, and salt spray concentration in the form of continuous time series. Although closely related to the corrosion process, it is difficult to directly establish a quantitative correlation between these parameters and the corrosion state.

[0004] Current technologies for processing multi-source corrosion data typically employ isolated methods: manual interpretation of images, manual fitting of electrochemical curves, and simple statistical analysis of environmental data. This approach suffers from several significant drawbacks: first, the lack of unified feature extraction rules makes it difficult to guarantee consistency and comparability of data processed by different personnel and batches; second, extracted features often remain at the descriptive level, lacking a deep understanding of the corrosion mechanism and exhibiting poor interpretability; and third, features extracted from various data sources are fragmented, making multi-source information fusion difficult and failing to provide a comprehensive characterization of the corrosion state. These problems severely restrict the engineering application of corrosion data and fail to meet the urgent needs of modern intelligent equipment operation and maintenance for data standardization and feature structuring.

[0005] Therefore, there is an urgent need to establish a multi-source feature extraction and structured representation method for corrosion data of air conditioning heat exchangers. Through a unified processing flow and feature definition, the scattered raw data can be transformed into structured feature vectors with clear physical meaning, quantifiability, and fusion. Summary of the Invention

[0006] The purpose of this invention is to overcome the technical shortcomings of existing technologies, such as inconsistent processing methods for multi-source corrosion data, lack of standardization in feature extraction, and difficulty in achieving multi-source information fusion. This invention provides a method for multi-source feature extraction and structured representation of corrosion data from air conditioning heat exchangers. This method aims to establish unified feature extraction rules to extract physically meaningful feature parameters from corrosion morphology images, electrochemical test data, and environmental monitoring data. It then normalizes and vectorizes these features to form standardized structured feature vectors, providing a high-quality data foundation for subsequent corrosion status assessment, lifetime prediction, and multimodal data fusion analysis.

[0007] To achieve the above-mentioned objectives, this invention provides a method for multi-source feature extraction and structured representation of corrosion data from air conditioning heat exchangers. This method follows a technical route of "data acquisition—preprocessing—source-specific feature extraction—normalization—vectorization combination," and specifically includes the following steps: S1: Obtain raw data of multi-source corrosion First, acquire multi-source raw data related to the corrosion behavior of air conditioning heat exchangers. This data includes at least the following three categories: Corrosion morphology image data: Images of the corrosion state of the heat exchanger surface obtained through optical imaging equipment or on-site photography can intuitively reflect the morphology, distribution range and severity of corrosion.

[0008] Electrochemical test data: Data reflecting the electrochemical behavior of corrosion obtained through an electrochemical workstation, including but not limited to polarization curves, electrochemical impedance spectroscopy, and electrochemical noise data.

[0009] Environmental monitoring data: Data reflecting the environmental conditions in which the equipment is in service, obtained through meteorological stations or on-site sensors, including but not limited to temperature, relative humidity, salt spray concentration, and atmospheric pollutant concentration.

[0010] S2: Data Preprocessing To improve data quality and ensure the accuracy of subsequent feature extraction, the above-mentioned multi-source raw data were preprocessed as follows: Image data preprocessing: Denoising the erosion morphology image to eliminate noise interference introduced during acquisition; then grayscale processing is performed to convert the color image into a grayscale image, reducing the data dimensionality and preserving texture and brightness information.

[0011] Electrochemical data preprocessing: Smoothing of electrochemical curves (such as polarization curves) and elimination of high-frequency noise using moving average or filtering algorithms to make curve features clearer and facilitate subsequent feature parameter extraction.

[0012] Environmental data preprocessing: Outlier removal is performed on environmental monitoring time series data. Statistical methods (such as the three-standard-deviation criterion) are used to identify and remove abnormal data points caused by sensor failure or transmission errors.

[0013] S3: Extraction of Corrosion Morphology Features Based on the preprocessed corrosion morphology image, morphological features that can quantitatively characterize the degree and morphology of corrosion are extracted, specifically including: Color characteristics: The grayscale values ​​of the corroded areas differ from those of the uncorroded areas. By calculating the mean and variance of grayscale values ​​in the global or local areas of the image, the brightness variations in the corroded areas are quantified, indirectly reflecting the coverage and thickness of the corrosion products.

[0014] Texture features: Corroded surfaces typically exhibit rough and uneven textures. By calculating the gray-level co-occurrence matrix of the image, texture parameters such as contrast, correlation, and energy are extracted to quantify the roughness and unevenness of the corroded surface.

[0015] Corrosion area ratio: Using image segmentation techniques (such as thresholding, edge detection, or region growing), the image is divided into corroded and uncorroded areas. The ratio of the number of pixels in the corroded area to the total number of pixels in the image is calculated to obtain the corrosion area ratio, which is the most intuitive quantitative indicator of the severity of corrosion.

[0016] S4: Electrochemical Feature Extraction Based on the preprocessed electrochemical test data, electrochemical parameters reflecting corrosion kinetics characteristics are extracted: Corrosion potential: The corrosion potential (natural corrosion potential) is determined by analyzing the polarization curve. This parameter reflects the thermodynamic tendency of the material under specific conditions.

[0017] Corrosion current density: The corrosion current density is obtained from the polarization curve using the Tafel extrapolation method or the linear polarization method. This parameter is a direct kinetic index characterizing the corrosion rate.

[0018] Electrochemical impedance characteristic parameters: Equivalent circuit fitting is performed on the electrochemical impedance spectroscopy data to extract solution resistance, charge transfer resistance and constant phase angle element parameters, which are used to analyze the interface characteristics and reaction mechanism of the corrosion process.

[0019] S5: Environmental Impact Feature Extraction Based on the preprocessed environmental monitoring data, statistical features reflecting the impact of the environment on corrosion behavior are extracted: Temperature characteristics: Calculate the average, maximum, minimum and fluctuation range of temperature within a certain time window to characterize the thermal stress level of the environment.

[0020] Humidity characteristics: Calculate the average relative humidity and the duration of high humidity (the proportion of time when the relative humidity exceeds a certain threshold) to characterize the humidity level of the environment.

[0021] Corrosive media characteristics: The average and peak values ​​of salt spray concentration or chloride ion deposition are statistically analyzed to characterize the intensity of corrosive media in the environment.

[0022] S6: Feature Normalization Processing Because the extracted features differ significantly in physical meaning, dimensions, and numerical range (e.g., corrosion area ratio is a decimal between 0 and 1, corrosion current density may be in the microampere range, and temperature is in degrees Celsius), to eliminate the influence of dimensions and facilitate subsequent multi-feature fusion and unified expression, all extracted features are normalized to a uniform numerical range. The normalization method can be min-max normalization or Z-score standardization.

[0023] S7: Constructing Structured Feature Vectors The normalized corrosion morphology, electrochemical characteristics, and environmental characteristics are combined to form a structured feature vector with fixed dimensions and a unified expression. Each component in this feature vector corresponds to a corrosion-related parameter with a clear physical meaning, comprehensively characterizing the corrosion state, corrosion kinetics, and service environment conditions of the heat exchanger under specific spatiotemporal conditions.

[0024] Compared with existing technologies, the multi-source feature extraction and structured representation method for corrosion data of air conditioning heat exchangers provided by this invention has the following significant advantages: This invention achieves unified feature representation of multi-source heterogeneous data: For the first time, this invention establishes a unified and standardized feature extraction process for three completely different types of raw data: corrosion morphology images, electrochemical data, and environmental data. This process transforms unstructured data into structured features, significantly improving the usability of the data.

[0025] Feature extraction has a clear physical meaning: Each feature parameter extracted in this invention (such as corrosion area ratio, corrosion current density, temperature fluctuation amplitude, etc.) is directly related to the physical or chemical nature of the corrosion process, avoiding the uninterpretability problem caused by "black box" feature extraction, and providing interpretable input for subsequent mechanism analysis and model construction.

[0026] It provides a standardized feature output format: Through normalization and vectorization, this invention unifies all features into a standardized feature vector, solving the technical problem of inconsistent multi-source data formats and difficulty in fusion, and providing an ideal data interface for multimodal data fusion analysis and large model technology applications.

[0027] The invention improves the automation and reliability of corrosion analysis: It automatically extracts features through algorithms, replacing the traditional method of relying on manual interpretation and fitting, eliminating the subjectivity and inconsistency introduced by human factors, and significantly improving the objectivity and reliability of the analysis results.

[0028] It has good engineering applicability and scalability: The method of this invention has a clear process and can be easily integrated into corrosion monitoring and analysis software systems. The specific algorithm and parameters of feature extraction can be flexibly adjusted according to actual application scenarios, which has strong engineering practical value. Attached Figure Description

[0029] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of the overall process of the multi-source corrosion data feature extraction and structured representation method described in this invention; Figure 2 This is a schematic diagram of the corrosion morphology feature extraction process described in this invention, illustrating the transformation process from the original image to parameters such as corrosion area ratio and texture features; Figure 3 This is a schematic diagram of the electrochemical feature extraction process described in this invention, illustrating the analytical method for obtaining corrosion potential and corrosion current density from polarization curves. Detailed Implementation

[0030] The present invention will be further explained below with reference to specific implementation schemes, but it is not limited to the present invention. The structures, proportions, sizes, etc. shown in the accompanying drawings are only used to complement the content disclosed in the specification, so as to enable those skilled in the art to understand and read, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modification of the structure, change of the proportion relationship or adjustment of the size, without affecting the effect and purpose that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0031] This embodiment takes the outdoor unit heat exchanger (condenser) of a certain type of split air conditioner as the object. The heat exchanger adopts an aluminum fin structure. After operating in a coastal high salt spray environment for one year, relevant corrosion data was collected for feature extraction.

[0032] Step S1: Obtain raw data of multi-source corrosion Obtain three types of raw data related to the corrosion of this heat exchanger: Corrosion morphology image data: The surface of the heat exchanger fins was photographed using a digital microscope to obtain RGB color images with a resolution of 1024×768 pixels. White corrosion products and local pitting corrosion can be seen on the surface of the fins in the images.

[0033] Electrochemical test data: Samples were taken from the heat exchanger fins and electrochemical tests were conducted in an electrolyte solution in a simulated coastal environment to obtain polarization curves. The scanning range was from -0.3V to +0.5V relative to the open circuit potential, and the scanning rate was 0.5mV / s.

[0034] Environmental monitoring data: Hourly environmental data for the past year were obtained from a nearby meteorological station, including temperature (°C), relative humidity (%), and chloride ion deposition (mg / m³). 2 ·d).

[0035] Step S2: Data Preprocessing Image preprocessing: The original RGB image is denoised by median filtering to eliminate random noise during image acquisition; then, the color image is converted into a grayscale image using a weighted average method to obtain a grayscale image with 256 levels of grayscale.

[0036] Electrochemical data preprocessing: The raw polarization curve data were smoothed using the Savitzky-Golay filtering algorithm to eliminate high-frequency noise and make the subtle changes in the curve near the corrosion potential clearer.

[0037] Environmental data preprocessing: Statistical analysis of hourly temperature and humidity data is performed, and outliers (such as instantaneous extreme values ​​caused by sensor failure) are removed using the three-standard-deviation criterion.

[0038] Step S3: Extraction of corrosion morphology features Erosion area ratio calculation: Otsu's method was used to adaptively threshold the grayscale image, binarizing it into eroded areas (white) and uneroded areas (black). The proportion of pixels in the eroded area to the total number of pixels was calculated to be 35.2%.

[0039] Texture feature extraction: The gray-level co-occurrence matrix of the gray-level image is calculated, and the contrast parameter is extracted to be 0.48 and the energy parameter is 0.21, reflecting that the corroded surface has a moderate degree of roughness.

[0040] Color feature extraction: The calculated global grayscale mean of the image is 142 (range 0-255), which is lower than the theoretical grayscale value of uncorroded aluminum alloy, indicating that the surface color is darkened due to the coverage of corrosion products.

[0041] Step S4: Electrochemical Feature Extraction Corrosion potential and corrosion current density extraction: The pretreated polarization curves were analyzed to determine the corrosion potential as -0.72 V (relative to a saturated calomel electrode). Using the Tafel extrapolation method, linear fitting was performed on the Tafel regions of the cathode and anode branches, yielding an extrapolated corrosion current density of 8.5 μA / cm². 2 This value reflects the corrosion rate of the heat exchanger under the current environment.

[0042] Electrochemical impedance characteristics (optional): If impedance spectrum data is obtained, parameters such as charge transfer resistance can be further fitted.

[0043] Step S5: Environmental Impact Feature Extraction Statistical analysis of environmental data from the past year: Temperature characteristics: The average annual temperature is 23.5℃, the highest temperature is 34.2℃, the lowest temperature is 8.5℃, and the annual temperature fluctuation range is 25.7℃.

[0044] Humidity characteristics: The annual average relative humidity is 82%, and the time with relative humidity exceeding 80% accounts for 67% of the total time.

[0045] Corrosive media characteristics: Annual average chloride ion deposition is 15.2 mg / m³. 2 ·d, peak value can reach 42.3 mg / m 2 The value ·d indicates that the area is a high-salt-spray corrosion environment.

[0046] Step S6: Feature normalization processing All extracted feature parameters were normalized. Taking the corrosion area ratio as an example, its maximum possible value was set to 80% based on experience, which is 0.44 after normalization; the corrosion current density was set to a maximum value of 50 μA / cm. 2 After normalization, the value is 0.17; the annual average temperature is set to a range of -10℃ to 40℃, and after normalization, the value is 0.67. Other features are converted to the same range, all features are converted to the [0,1] interval.

[0047] Step S7: Construct structured feature vectors The normalized features are combined in a preset order to form the corrosion status feature vector of the heat exchanger after one year of operation: F= [0.44, 0.48, 0.21, 0.17, 0.67, 0.82, 0.38, ...] This feature vector contains key information such as corrosion area ratio, texture contrast, corrosion current density, annual average temperature, annual average humidity, and chloride ion level, which fully characterizes the corrosion state, corrosion kinetics, and service environment conditions of the heat exchanger.

[0048] Using the above method, the original multi-source corrosion data was successfully transformed into standardized structured feature vectors, providing high-quality data input for subsequent corrosion status assessment, lifetime prediction, and multimodal data fusion analysis. Matters not covered in this invention are prior art.

[0049] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for multi-source feature extraction and structured representation of corrosion data from air conditioning heat exchangers, characterized in that, Includes the following steps: S1: Obtain raw data of multi-source corrosion, including at least corrosion morphology image data, electrochemical test data, and environmental monitoring data; S2: Preprocess the original multi-source corrosion data, including denoising and grayscale processing of image data, smoothing processing of electrochemical data, and outlier removal processing of environmental data. S3: Based on the preprocessed image data, extract corrosion morphology features, which include at least corrosion area ratio, color features, and texture features; S4: Based on the preprocessed electrochemical data, extract electrochemical features, which include at least corrosion potential and corrosion current density; S5: Based on the preprocessed environmental data, extract environmental impact features, which include at least temperature features, humidity features, and corrosive medium features; S6: Normalize all features extracted in steps S3 to S5 to convert them to a uniform numerical range. S7: Combine the normalized corrosion morphology features, electrochemical features, and environmental impact features to construct a structured feature vector.

2. The method for multi-source feature extraction and structured representation of corrosion data of air conditioning heat exchangers according to claim 1, characterized in that, The corrosion area ratio mentioned in step S3 is obtained by dividing the image into corrosion areas and uncorroded areas using an image segmentation method, and calculating the ratio of the number of pixels in the corrosion area to the total number of pixels in the image.

3. The method for multi-source feature extraction and structured representation of corrosion data of air conditioning heat exchangers according to claim 2, characterized in that, The image segmentation methods mentioned include threshold segmentation, edge detection, or region growing methods.

4. The method for multi-source feature extraction and structured representation of corrosion data of air conditioning heat exchangers according to claim 1, characterized in that, The texture features mentioned in step S3 are contrast, correlation, or energy parameters extracted by calculating the gray-level co-occurrence matrix of the image.

5. The method for multi-source feature extraction and structured representation of corrosion data of air conditioning heat exchangers according to claim 1, characterized in that, The corrosion current density mentioned in step S4 is obtained by analyzing the polarization curve using the Tafel extrapolation method or the linear polarization method.

6. The method for multi-source feature extraction and structured representation of corrosion data of air conditioning heat exchangers according to claim 1, characterized in that, The electrochemical characteristics described in step S4 also include solution resistance, charge transfer resistance, or constant phase angle element parameters obtained by equivalent circuit fitting of the electrochemical impedance spectrum.

7. The method for multi-source feature extraction and structured representation of corrosion data of air conditioning heat exchangers according to claim 1, characterized in that, The temperature characteristics mentioned in step S5 include the average, maximum, minimum, or fluctuation range of temperature; the humidity characteristics include the average relative humidity or the proportion of high humidity time; and the corrosive medium characteristics include the average or peak value of salt spray concentration or chloride ion deposition.

8. The method for multi-source feature extraction and structured representation of corrosion data of air conditioning heat exchangers according to claim 1, characterized in that, The normalization process described in step S6 uses the minimum-maximum normalization or Z-score normalization method.

9. The method for multi-source feature extraction and structured representation of corrosion data of air conditioning heat exchangers according to claim 1, characterized in that, The structured feature vector mentioned in step S7 is a fixed-dimensional numerical vector with components having clear physical meanings, used to characterize the corrosion state, corrosion kinetics, and service environment conditions of the heat exchanger.

10. A system for multi-source feature extraction and structured representation of corrosion data from air conditioning heat exchangers, characterized in that, include: The data processing module is used to perform the operation of step S2 in claim 1, and to preprocess the original data of multi-source corrosion. The feature extraction module is used to perform the operations of steps S3 to S5 in claim 1, and extract corrosion morphology features, electrochemical features and environmental impact features from the preprocessed data respectively; The feature output module is used to perform the operations of steps S6 and S7 in claim 1, normalize the extracted features and construct a structured feature vector.

Images