Testing device and method for salt spray corrosion test of composite material layer of wind power blade

Abstract

The invention relates to the technical field of wind power blade material testing, and discloses a testing device and method for a salt spray corrosion test of a wind power blade composite material layer. According to the method, wind power blade composite material coating preparation parameters are obtained, and coating basic information is provided for subsequent performance evaluation; based on preset salt spray test environment control parameters, dynamic salt spray environment simulation conditions are generated so as to better fit the actual outdoor environment; then, a multi-sensor coupling monitoring network is constructed, real-time data acquisition is conducted on the corrosion process of the composite material coating, and comprehensive tracking of the corrosion process is achieved; processing real-time data by utilizing a corrosion characteristic analysis algorithm, generating a multi-dimensional corrosion characteristic index, and quantitatively representing the corrosion condition of the coating; and finally, evaluating the anti-corrosion stability of the new material coating in combination with the coating characteristic parameters and the multi-dimensional corrosion characteristic indexes. According to the method, the corrosion resistance of the coating in the actual environment can be more accurately reflected, and support is provided for research, development and application of the composite coating of the wind power blade.

Description

Technical Field

[0001] This invention relates to the field of wind turbine blade material testing technology, specifically to a testing device and method for salt spray corrosion testing of composite material layers in wind turbine blades. Background Technology

[0002] In the rapid development of the new energy industry, wind power, as a clean and sustainable energy form, has seen its installed capacity and application scope continuously expand. Wind turbine blades, as the core component of wind turbine generators, directly determine the power generation efficiency and operational lifespan of the unit. Composite materials, with their excellent properties such as lightweight, high strength, and fatigue resistance, have become the mainstream material for wind turbine blade manufacturing. However, wind turbine blades are exposed to complex outdoor environments for extended periods, enduring the combined effects of salt spray from marine climates, corrosive gases from industrial environments, and variations in diurnal temperature and humidity. Among these, salt spray corrosion is one of the key factors leading to the degradation of composite material coating performance and damage to structural integrity.

[0003] Chloride ions in salt spray environments have extremely high permeability, capable of penetrating the tiny pores and defects of composite material coatings. They react chemically with components within the matrix material or coating, causing phenomena such as blistering, peeling, and discoloration. This leads to a decline in the mechanical properties of the composite material, and in severe cases, can even cause safety accidents such as blade structural cracking and breakage. This not only increases the maintenance costs of wind turbine equipment but also affects the normal operation of wind farms. Therefore, conducting salt spray corrosion tests on the composite coatings of wind turbine blades to accurately assess their corrosion resistance is a crucial prerequisite for ensuring the long-term stable operation of wind turbine blades.

[0004] There are existing testing methods for salt spray corrosion of materials in the industry. These methods typically set fixed environmental parameters such as salt spray concentration, temperature, and humidity according to relevant standards, simulate the corrosive environment through static salt spraying, and evaluate the corrosion resistance of materials by periodic visual observation or single performance tests (such as coating adhesion testing and weight change measurement). However, existing testing methods have many limitations. First, parameters such as salt spray concentration, temperature, and humidity in actual outdoor environments are not constant but dynamically change with factors such as season, weather, and geographical location. Static environmental simulation methods cannot accurately reflect the actual corrosive environment of wind turbine blades, resulting in a significant deviation between test results and material performance in actual application scenarios, making it difficult to accurately guide the research and development and application of composite material coatings.

[0005] Current monitoring methods are relatively simplistic, with most relying solely on periodic manual inspections. This makes it impossible to track the corrosion process in real time and capture subtle changes in the early stages of corrosion. Failure to detect and analyze early-stage damage hinders in-depth research into coating corrosion mechanisms and fails to provide accurate data support for coating performance optimization. Furthermore, single performance indicators only reflect a specific aspect of the material's corrosion status and cannot comprehensively characterize the performance degradation patterns of coatings under salt spray conditions. For example, visual observation alone cannot quantify internal chemical changes and microstructural damage within the coating, and measuring weight changes alone cannot correlate the degree of degradation in the coating's mechanical properties.

[0006] Existing testing methods for evaluating the corrosion resistance of materials often focus solely on post-corrosion performance changes, neglecting the impact of composite coating preparation parameters (such as coating thickness, curing temperature, and component ratios) on their corrosion resistance. Different preparation parameters directly affect the coating's fundamental properties, including density, adhesion, and chemical stability, thus determining its corrosion resistance in salt spray environments. Without comprehensive analysis incorporating coating preparation parameters during the evaluation process, it is difficult to accurately determine the root causes of differences in coating corrosion resistance, and it is impossible to provide targeted guidance for optimizing the formulation and improving the preparation process of new composite coatings. This leads to prolonged development cycles and low efficiency in the research and development of new coatings. These problems make existing salt spray corrosion testing methods insufficient to meet the demands of high-performance and long-life composite coatings for wind turbine blades. There is an urgent need for a testing method that can more realistically simulate actual environments, more comprehensively monitor the corrosion process, and more scientifically evaluate corrosion resistance. Summary of the Invention

[0007] The purpose of this invention is to provide a test method for salt spray corrosion of composite material layers in wind turbine blades, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides a test method for salt spray corrosion testing of composite material layers in wind turbine blades, the method comprising:

[0009] Obtain the preparation parameters of composite material coating for wind turbine blades;

[0010] Based on preset salt spray test environment control parameters, dynamic salt spray environment simulation conditions are generated;

[0011] A multi-sensor coupled monitoring network is constructed to collect real-time data on the corrosion process of the composite material coating;

[0012] The real-time data is processed using a corrosion characteristic analysis algorithm to generate multi-dimensional corrosion characteristic indicators.

[0013] The corrosion resistance stability of the new material coating is evaluated by combining the coating characteristic parameters and the multi-dimensional corrosion characteristic indicators.

[0014] Preferably, the method for obtaining the preparation parameters of the composite material coating for wind turbine blades is as follows: extracting the thickness distribution data, chemical composition ratio data, and microstructure characteristic data of the new material coating; generating a dynamic environmental correction factor based on the temperature and humidity benchmark values ​​in the salt spray test environment control parameters; and applying the dynamic environmental correction factor to correct the thickness distribution data and microstructure characteristic data in real time to obtain the environmentally adaptable coating parameters.

[0015] Preferably, the method for constructing the multi-sensor coupled monitoring network is as follows: design a monitoring node layout based on a directed topology, wherein the monitoring nodes are divided into coating corrosion layer monitoring nodes, interface bonding layer monitoring nodes, and substrate material layer monitoring nodes; according to a preset node information flow rule, the output data of the coating corrosion layer monitoring nodes is transmitted to the interface bonding layer monitoring nodes, and the output data of the interface bonding layer monitoring nodes is transmitted to the substrate material layer monitoring nodes; after completing one full node data interaction, the state estimate of each monitoring node is updated.

[0016] Preferably, the method for generating the multi-dimensional corrosion characteristic index is as follows: extracting coating surface morphology change characteristic data, interface peeling rate data, and substrate stress distribution data from the real-time data; processing the coating surface morphology change characteristic data and interface peeling rate data using a weighted fusion algorithm to generate a comprehensive corrosion deformation index; and calculating the interlayer failure risk coefficient of the material by combining the substrate stress distribution data and the comprehensive corrosion deformation index.

[0017] Preferably, the method for generating the dynamic environmental correction factor includes: collecting real-time temperature fluctuation data, humidity offset data, and salt spray deposition data in the salt spray test chamber; calculating the temperature and humidity coupling influence weight based on the real-time temperature fluctuation data and humidity offset data; and introducing salt spray deposition data to compensate and correct the temperature and humidity coupling influence weight to generate a comprehensive salt spray environmental influence index.

[0018] Preferably, the method for evaluating the corrosion resistance stability is as follows: calculate the stability fluctuation rate of each monitoring node based on the interlayer failure risk coefficient of the material; mark high-risk corrosion areas based on the stability fluctuation rate and the preset coating failure threshold; and output the dynamic stability rating of the new material coating by combining the comprehensive impact index of the salt spray environment and the marking results.

[0019] Preferably, the method further includes a method for handling random salt spray concentration interference: monitoring instantaneous deviation data of salt spray deposition concentration and constructing a Bernoulli distribution model to describe concentration mutation events; performing probability compensation correction on the salt spray deposition data based on the probability parameters of the Bernoulli distribution model; and feeding back the compensated salt spray deposition data to the generation process of the dynamic environmental correction factor.

[0020] Preferably, the method further includes a coating performance degradation modeling method: acquiring time series data of the comprehensive corrosion deformation index; extracting the mean change rate and standard deviation of the time series data; constructing a coating thickness degradation rate model based on the mean change rate and standard deviation; and applying the thickness degradation rate model to predict the remaining lifespan of the new material coating.

[0021] Preferably, the method further includes a corrosion process visualization method: mapping the surface morphology change feature data of the coating to a three-dimensional spatial coordinate system to generate a corrosion morphology evolution map; superimposing the interlayer failure risk coefficient of the material onto the corrosion morphology evolution map to form a risk thermal distribution map; and dynamically coloring and rendering the risk thermal distribution map based on the dynamic stability rating.

[0022] The method further includes a test parameter feedback optimization method: generating a salt spray test intensity adjustment command based on the remaining lifespan and dynamic stability rating; updating the salt spray test environment control parameters based on the probability-compensated salt spray deposition data and the salt spray test intensity adjustment command; and inputting the updated salt spray test environment control parameters into the generation process of the dynamic salt spray environment simulation conditions.

[0023] Preferably, the present invention also includes a testing device for salt spray corrosion testing of a wind turbine blade composite material layer, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described testing method for salt spray corrosion testing of a wind turbine blade composite material layer.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] This salt spray corrosion test method for the composite material layer of wind turbine blades overcomes the limitations of existing static salt spray environments in environmental simulation. By generating dynamic salt spray environment simulation conditions based on preset salt spray test environment control parameters, it can simulate corrosion environments under different time periods and weather conditions according to the variation patterns of parameters such as salt spray concentration, temperature, and humidity in the actual outdoor environment. This makes the test environment closer to the salt spray corrosion scenarios that wind turbine blades actually experience during operation, thus effectively avoiding the problem of large deviations between test results and actual performance caused by static environment simulation. This makes the evaluation of the corrosion resistance performance of composite material coatings more realistic and valuable.

[0026] At the corrosion monitoring level, a multi-sensor coupled monitoring network is constructed to achieve real-time data acquisition of the corrosion process. Compared with traditional manual periodic inspections or single-sensor detection methods, it can simultaneously acquire real-time data on multiple aspects such as coating surface morphology, chemical composition, electrochemical signals, and mechanical properties. It can not only capture minute changes on the coating surface in the early stage of corrosion and hidden damage to the internal structure, but also achieve continuous tracking of the corrosion process, completely recording the entire process of corrosion from occurrence, development to intensification. This avoids the situation where key corrosion information is missed due to excessively long detection intervals or single detection dimensions. It provides a rich and comprehensive data foundation for in-depth analysis of the corrosion mechanism of composite material coatings, and helps to more clearly understand the characteristics and laws of corrosion at different stages.

[0027] At the data processing and analysis level, corrosion characteristic analysis algorithms are used to process real-time collected data and generate multi-dimensional corrosion characteristic indicators. This can transform scattered and complex raw data into quantitative indicators with clear physical meaning, such as coating corrosion rate, porosity change, and electrochemical impedance change. These multi-dimensional indicators can comprehensively characterize the corrosion status of the coating from different perspectives. They can not only reflect the macroscopic corrosion phenomena on the coating surface, but also reveal the microscopic structural changes and chemical changes inside the coating. Compared with the single performance indicators in existing methods, this method can more systematically and accurately assess the degree of performance degradation of the coating in a salt spray environment, providing a more scientific basis for judging the corrosion resistance of the coating.

[0028] At the performance evaluation level, combining the preparation parameters of composite material coatings with multi-dimensional corrosion characteristic indicators to assess corrosion resistance stability can establish the correlation between coating preparation process and corrosion resistance. By analyzing the differences in corrosion characteristic indicators corresponding to different preparation parameters (such as coating thickness, curing process, component ratio, etc.), the influence of preparation parameters on coating corrosion resistance can be clearly understood. This provides direction for formula optimization and preparation process improvement of novel composite material coatings, helping researchers to adjust preparation parameters in a targeted manner to improve the corrosion resistance of coatings. Simultaneously, this evaluation method combining preparation parameters can more accurately determine the root cause of differences in coating corrosion resistance, avoiding the problem of failing to identify influencing factors based solely on corrosion results. This helps shorten the development cycle of novel coatings and improve development efficiency.

[0029] From a practical application perspective, this method provides reliable technical support for the selection, quality control, and maintenance strategy formulation of composite coatings for wind turbine blades. In the coating selection stage, this method allows for accurate comparison of the corrosion resistance of different types of coatings, enabling the selection of coating materials more suitable for specific wind power scenarios (such as coastal wind farms and high-humidity wind farms). In the quality control stage, monitoring the relationship between coating preparation parameters and corrosion performance ensures the stability of coating quality during production. In the maintenance strategy formulation stage, based on real-time monitoring and performance evaluation of the coating corrosion process, the service life of the coating can be predicted in advance, allowing for the development of more reasonable maintenance cycles and plans. This reduces wind turbine blade failures caused by coating failure, lowers the operating costs and maintenance risks of wind farms, ensures the long-term stable operation of wind turbine generators, and promotes the sustainable and healthy development of the wind power industry. Attached Figure Description

[0030] Figure 1 This is a schematic diagram illustrating the working principle of the salt spray corrosion test method for the composite material layer of wind turbine blades described in this invention.

[0031] Figure 2 A flowchart illustrating the method for obtaining parameters in the preparation of composite coatings for wind turbine blades;

[0032] Figure 3 A flowchart illustrating a method for generating multi-dimensional corrosion feature indicators;

[0033] Figure 4 This is a flowchart of a method for evaluating the corrosion resistance stability of new material coatings. Detailed Implementation

[0034] 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.

[0035] Please see Figure 1 This invention provides a testing device and method for salt spray corrosion testing of composite material layers in wind turbine blades, the method comprising:

[0036] The preparation parameters for the composite coating of wind turbine blades were obtained, including coating thickness, chemical composition, and microstructure. Subsequently, dynamic salt spray environment simulation conditions were generated based on preset salt spray test environment control parameters. These conditions simulate the combined factors of salt spray deposition, temperature and humidity changes, etc., in the actual marine atmospheric environment. A multi-sensor coupled monitoring network was constructed and deployed on and inside the test samples to collect real-time data on the corrosion process of the composite coating under salt spray conditions, including changes in physical morphology, chemical properties, and mechanical properties. The collected real-time data was processed using a corrosion characteristic analysis algorithm, which extracted multi-dimensional corrosion characteristic indicators from the raw data, such as coating surface corrosion depth, interfacial bonding strength attenuation, and stress concentration in the matrix material. Finally, combining the coating characteristic parameters and multi-dimensional corrosion characteristic indicators, a stability assessment model was used to calculate the performance degradation rate and failure risk of the material under corrosive conditions, thereby evaluating the corrosion resistance stability of the new material coating.

[0037] Example 1: See Figure 2 This study involves acquiring the preparation parameters of composite coatings for wind turbine blades and constructing a multi-sensor coupled monitoring network. When acquiring coating preparation parameters, high-precision measuring instruments are used to systematically detect the new material coating. An automatic thickness gauge is used to select measurement points on the coating surface in a grid pattern, recording the thickness value at each location to form a thickness distribution data set. This data set is statistically analyzed to generate a thickness distribution cloud map, reflecting the uniformity of the coating thickness and potential weak areas. Chemical composition ratio data are obtained using spectral analysis technology. The coating sample is placed in the spectrometer detection chamber, and the proportions of resin matrix, curing agent, inorganic filler, and reinforcing fibers are calculated based on characteristic spectral peaks. Microstructural feature data are acquired using a scanning electron microscope. The coating cross-section is observed at a specific magnification, recording structural information such as pore distribution morphology, fiber orientation, and interfacial bonding state.

[0038] Based on the temperature and humidity baseline values ​​set in the salt spray test environment control parameters, the environmental simulation system generates a dynamic environmental correction factor. This factor is calculated using a temperature and humidity coupling algorithm, and its value is adjusted in real time according to fluctuations in environmental parameters. When the temperature inside the test chamber deviates from the baseline value, the correction factor compensates for the thermal expansion effect in the thickness distribution data; when the humidity deviates, the correction factor corrects for the moisture absorption deformation in the microstructure characteristic data. The final output of the environmentally adaptable coating parameters is the real-time corrected thickness distribution value and microstructure stability index, which serve as the basic input for subsequent corrosion assessment.

[0039] The monitoring nodes are divided into three levels: the coating corrosion layer monitoring node uses a combination of an electrochemical workstation and an optical microscope to continuously collect data on surface potential changes, corrosion product accumulation morphology, and surface roughness evolution; the interface bonding layer monitoring node integrates acoustic emission sensors and microstrain gauges to monitor changes in interfacial shear stress, bond strength attenuation rate, and microcrack propagation trajectory; and the substrate material layer monitoring node is equipped with fiber optic grating sensors and temperature probes to collect data on fiber strain distribution, resin degradation degree, and internal temperature gradient changes. All monitoring nodes transmit data according to preset information flow rules. The output data from the coating corrosion layer monitoring node is transmitted to the interface bonding layer monitoring node via a data acquisition card, and the processed data from the interface bonding layer monitoring node is transmitted to the substrate material layer monitoring node via a signal conversion module.

[0040] In a complete data interaction process, each monitoring node sequentially completes data acquisition, preprocessing, and transmission. The coating corrosion layer monitoring node first converts the surface morphology data into a digital signal, adds a timestamp and node identifier, and then sends it to the data bus. After receiving the data, the interface bonding layer monitoring node performs data fusion processing based on its own acquired interface stress data to generate an interface state assessment result. After receiving the interface data, the substrate material layer monitoring node performs correlation analysis with the internal strain data and outputs a substrate integrity assessment report. After completing the data interaction across all nodes, a state estimation algorithm is used to update the reliability index and data confidence level of each monitoring node. These state values ​​are used for adaptive adjustment in subsequent data acquisition cycles.

[0041] Thickness measurement employs a non-contact laser thickness gauge to avoid damage to the coating surface. The measurement point array covers the entire sample surface, with a constant spacing between adjacent measurement points to ensure comprehensive characterization of the thickness distribution. During spectral analysis, multiple scans of the same sample are performed and the average value is taken to reduce the impact of random errors. Before electron microscopy observation, the sample undergoes vacuum gold plating to improve image clarity. The dynamic environmental correction factor is calculated using a recursive algorithm, updating the correction coefficient at specific time intervals. The placement of monitoring nodes is optimized through finite element simulation to ensure coverage of critical corrosion-risk areas. Data transmission utilizes the industrial Ethernet protocol to guarantee real-time performance and reliability. The update of the state estimate is based on the Kalman filter principle, comprehensively considering measurement noise and environmental interference factors.

[0042] The sensor data acquisition frequency is dynamically adjusted according to the corrosion development stage. A lower sampling frequency is used in the early stages of corrosion, and the sampling frequency is automatically increased when signs of accelerated corrosion are detected. All monitoring data are time-synchronized, facilitating subsequent multi-source data correlation analysis. The update of node state estimates considers not only the current measurement data but also historical data trends, forming an evaluation mechanism with memory capabilities. The entire system achieves closed-loop control during operation, and monitoring results are fed back to the environmental simulation system in real time.

[0043] Example 2: See Figure 3 This involves extracting key features from real-time monitoring data and generating multi-dimensional corrosion characteristic indicators. This process begins with in-depth analysis of data collected by a multi-sensor coupled monitoring network. The acquisition of coating surface morphology change characteristic data is achieved through a laser displacement scanning system. This system projects a laser beam onto the coating surface at a specific angle and constructs three-dimensional point cloud data of the surface by receiving the reflected light signals. After filtering and denoising, this point cloud data is transformed into quantitative indicators characterizing the degree of surface corrosion, including the average depth, maximum depth, and distribution density of corrosion pits, as well as the evolution trend of surface roughness. After each measurement cycle, the system compares the current point cloud data with the initial state, calculating the expansion rate of corrosion pits and the area of ​​newly corroded regions.

[0044] The acquisition of interfacial peeling rate data was accomplished using a high-frequency acoustic emission sensor array. These sensors were carefully positioned at the interface between the coating and the substrate to capture the elastic wave signals released during the initiation and propagation of microcracks at the interface. The sensor array recorded parameters such as energy, amplitude, and duration of acoustic emission events with a microsecond-level time resolution. By analyzing the cumulative number of acoustic emission events, their energy distribution, and their frequency of occurrence over time, the system calculated the instantaneous rate and acceleration of interfacial peeling. These data reflect the degradation of interfacial adhesion properties, including the initiation point of adhesive failure and the dynamic process of crack propagation.

[0045] The acquisition of matrix stress distribution data employs distributed fiber Bragg grating sensing technology. A series of fiber Bragg grating sensors are embedded within the composite matrix, arranged in a matrix to simultaneously monitor strain and temperature changes at multiple locations. Each sensor measures the minute deformation of the matrix material under salt spray conditions, and the data acquisition system records the fiber wavelength shift and converts it into stress values. By analyzing the correlation between data from sensors at different locations, the system generates a stress distribution map within the matrix, displaying the location and intensity changes of stress concentration areas.

[0046] After obtaining the three types of raw data, the system initiates a weighted fusion algorithm to process the coating surface morphology change characteristic data and interface peeling rate data. The algorithm first normalizes the two types of data to eliminate dimensional differences. The weighting coefficients are determined based on sensor measurement accuracy and data reliability indicators. The weight of the surface morphology data is dynamically adjusted according to the measurement point density and signal-to-noise ratio, while the weight of the interface peeling data is allocated based on the correlation and consistency of acoustic emission events. During the fusion process, the system considers the synchronicity of the two types of data in the time series and precisely matches the timestamps. The fused output is a comprehensive corrosion deformation index, which comprehensively characterizes the coupling effect of surface corrosion and interface failure in scalar form. Its numerical change reflects the development trend of the overall corrosion state of the material.

[0047] Calculating the interlayer failure risk coefficient requires integrating substrate stress distribution data and corrosion deformation indices. The system establishes an interlayer stress transfer model, which takes substrate stress distribution data as input and calculates the path and intensity of stress transfer from the substrate to the coating. Simultaneously, corrosion deformation indices are used to correct the model parameters, reflecting the impact of corrosion damage on stress distribution. Through iterative calculations, the system outputs an interlayer failure risk coefficient, which quantifies the probability of a chain reaction from coating surface corrosion to substrate material failure. The calculation process for the risk coefficient considers the characteristic parameters of different material layers, including elastic modulus, Poisson's ratio, and coefficient of thermal expansion, ensuring the accuracy of the assessment results.

[0048] The data acquisition system operates continuously, with each measurement cycle adhering to a strict time synchronization protocol. The measurement accuracy of the laser displacement scanning system is maintained through periodic calibration, verified using standard height blocks. The sensitivity settings of the acoustic emission sensor are adaptively adjusted based on the ambient noise level to ensure a high capture rate of the effective signal. The temperature compensation algorithm for the fiber Bragg grating sensor operates in real time, eliminating interference from temperature changes in strain measurements. The weighting coefficients of the weighted fusion algorithm are recalculated at specific time intervals to reflect real-time changes in sensor performance. The parameters of the interlayer stress transfer model are dynamically updated based on material aging patterns, enabling risk assessment results to track the actual degradation state of the material.

[0049] All processing is executed on a dedicated computing platform with multi-core parallel processing capabilities, enabling simultaneous processing of multiple sensor data streams. Data storage employs a time-series database structure, facilitating retrospective analysis of historical data. The system incorporates a data quality check mechanism to automatically identify and remove outlier measurements, ensuring the reliability of input data. After each calculation, the system generates a detailed process log, recording all intermediate parameters and the final result, providing a complete data traceability path for subsequent analysis.

[0050] Example 3: See Figure 4This involves the generation of dynamic environmental correction factors and the assessment of corrosion resistance stability. This process begins with the refined acquisition of environmental parameters within the salt spray test chamber. Multiple temperature sensors arranged inside the chamber operate in a high-frequency sampling mode, recording the instantaneous temperature values ​​at various locations and their time-varying gradients. These temperature fluctuation data not only reflect the control accuracy of the heating system but also characterize the uniformity of the thermal field distribution within the chamber. Humidity monitoring employs a combination of dew point and relative humidity sensors. Humidity offset data, including absolute humidity deviation and relative humidity fluctuation range, is calculated by comparing the measurements from both sensors. Salt spray deposition data is obtained using a precision collection device that collects deposited droplets at specified time intervals. The deposition per unit area is determined by weighing, and image analysis technology is used to statistically analyze the droplet size distribution.

[0051] The calculation of the weighting effect of temperature and humidity coupling is based on the collected real-time temperature fluctuation data and humidity offset data. The system establishes an environmental parameter correlation model, which analyzes the synergistic effect of temperature and humidity changes. When temperature increases accompanied by humidity increases, the accelerated corrosion effect is not simply additive, but exhibits a nonlinear enhancement characteristic. In the weighting calculation process, the system first standardizes the temperature fluctuation data and humidity offset data to eliminate dimensional differences. Then, it analyzes the correlation coefficient of the two types of parameters through an environmental simulation algorithm, and determines the basic value of the coupling weight based on the correlation strength. This weight value is dynamically adjusted over time to reflect the change in the intensity of the synergistic effect of temperature and humidity under different environmental conditions.

[0052] The generation of the comprehensive impact index of salt spray environment incorporates deposition data to compensate for the weighting of the coupled effects of temperature and humidity. The compensation algorithm considers the interaction between salt spray deposition and temperature and humidity parameters; as deposition increases, the corrosion acceleration effect of temperature and humidity significantly strengthens. The system establishes a three-dimensional parameter space, treating temperature, humidity, and deposition as independent variables, and calculates the environmental corrosion intensity at any point using an interpolation algorithm. The final generated comprehensive impact index of salt spray environment is a dimensionless parameter, and its calculation formula is as follows:

[0053] Γ=α·ΔT·Φ+β·ΔH·Ψ+γ·S·Ω

[0054] Wherein: Γ represents the comprehensive impact index of salt spray environment, ΔT represents the temperature fluctuation range, Φ represents the temperature sensitivity coefficient, ΔH represents the humidity offset, Ψ represents the humidity sensitivity coefficient, S represents the salt spray deposition amount, Ω represents the deposition amount influence coefficient, and α, β, and γ are the normalized weighting coefficients of each parameter. This index comprehensively characterizes the degree of synergistic influence of the three environmental factors of temperature, humidity, and salt spray deposition on material corrosion.

[0055] The assessment of corrosion resistance stability begins with a comprehensive analysis of data from each monitoring node. Based on the real-time calculated interlayer failure risk coefficient, the system calculates the stability volatility of each node using a time series analysis algorithm. This volatility characterizes the rate of change of the risk coefficient over time. The change in the risk coefficient at adjacent time points is obtained through differential operations, and then divided by the time interval to obtain the instantaneous volatility. A moving average filtering technique is used in the volatility calculation process to eliminate abnormal fluctuations caused by random interference.

[0056] The marking of high-risk corrosion areas is based on stability volatility and a preset coating failure threshold. The system establishes a threshold comparison algorithm that compares the real-time volatility of each monitoring node with the threshold. When the volatility of a node consistently exceeds the threshold and shows an upward trend, the system marks the area where that node is located as a high-risk area. Spatial correlation is considered during the marking process; when adjacent nodes exhibit high-risk characteristics simultaneously, they are classified into the same risk area. Each marked area records its geographical coordinates, risk level, volatility trend, and expected rate of development.

[0057] The dynamic stability rating is generated by combining the comprehensive impact index of the salt spray environment and the results of risk area marking. The rating model employs a multi-dimensional decision-making algorithm, using the environmental index as input parameters and risk area data as state parameters. The model first determines the basic rating level based on the magnitude of the environmental index, and then adjusts the level according to the number, level, and distribution of risk areas. The rating output adopts a graded system, with each level corresponding to a different level of corrosion resistance. The time factor is considered during the rating process, and trend analysis is performed on the rating results over multiple consecutive periods to ensure the continuity and stability of the rating.

[0058] Throughout the implementation process, environmental parameter acquisition employed a redundant design, with critical parameters monitored simultaneously by multiple sensors. Data fusion technology was used to improve measurement reliability. The temperature and humidity coupling weights were updated at specific time intervals to ensure timely reflection of environmental changes. The comprehensive impact index of salt spray environment was calculated using an iterative algorithm, referencing historical data trends for each calculation. The stability volatility calculation employed an adaptive window width, automatically adjusting the calculation window size based on data fluctuation characteristics. The risk area marking algorithm used a lag threshold to avoid mislabeling due to short-term fluctuations. The dynamic stability rating model incorporated a self-learning function, capable of optimizing rating rules based on historical rating results.

[0059] All computations run on a distributed computing architecture, with environmental parameter processing and stability assessment occurring on different computing nodes, exchanging data via message queues. The system includes an anomaly handling mechanism; when sensor data becomes abnormal, a backup data source is automatically activated or an estimation algorithm is employed. Data storage utilizes a multi-replica mechanism to ensure data integrity and traceability. After each assessment, the system generates an assessment report containing all intermediate parameters and the final rating result, while also recording the algorithm version and parameter settings used during the assessment process.

[0060] Example 4: This involves the process of handling random salt spray concentration interference and establishing a coating performance degradation model. The salt spray concentration monitoring system uses a high-frequency electrolytic salt spray sensor to collect salt spray deposition concentration data in the test chamber at millisecond intervals. These sensors are placed in key locations in the test space, including the area near the spray nozzle, directly above the sample, and in the corners of the chamber, to obtain comprehensive concentration distribution information. The collected data includes parameters such as instantaneous concentration values, fluctuation amplitude, and duration. When a sudden deviation of the concentration value from the normal range is detected, the system records the start time, peak concentration, and duration of the event, forming a dataset of concentration mutation events.

[0061] Based on these mutation event data, a Bernoulli distribution model is constructed to describe the occurrence patterns of concentration mutations. This model treats each sampling interval as an independent experiment, defining two possible outcomes: normal concentration and mutated concentration. The probability of a mutated state is calculated by statistically analyzing the frequency of mutated events in historical data. This probability parameter reflects the instability of the salt spray environment; a higher value indicates more frequent concentration mutations. The model also records the intensity distribution of mutated events, including the relative frequency of mutations of different magnitudes.

[0062] The probabilistic compensation correction process adjusts the original salt spray deposition data using the probability parameters of the Bernoulli distribution model. The system first identifies the time periods of abrupt concentration changes, and then weights the deposition data for these time periods based on the probability and intensity characteristics of the abrupt events. The compensation algorithm considers the duration and scope of the abrupt events, smoothing short-term abrupt changes and assessing the cumulative effect of long-term, continuous abrupt changes. The corrected salt spray deposition data includes the expected deposition value and its possible range of variation, more accurately reflecting actual environmental conditions.

[0063] The coating performance degradation modeling is based on time-series data of comprehensive corrosion deformation indices. This data is collected at fixed time intervals, recording the cumulative deformation process of the coating under salt spray conditions. The data preprocessing stage includes outlier removal, missing data imputation, and smoothing to ensure the continuity and consistency of the time series. The system employs a sliding window technique to analyze data trends; each window contains a certain number of continuous data points, and the window moves forward as new data arrives.

[0064] From the preprocessed time-series data, the system extracts two key statistical features: the mean change rate and the standard deviation. The mean change rate reflects the average rate of change of corrosion deformation indicators and is obtained by calculating the change in the mean between adjacent time windows. The standard deviation characterizes the dispersion of the data, reflecting the instability and fluctuation intensity of the corrosion process. These two indicators together describe the overall trend and fluctuation characteristics of coating degradation. Based on the mean change rate and standard deviation, the system constructs a coating thickness degradation rate model. This model treats the degradation process as a function of time, considering both linear degradation components and random fluctuation components. The model parameters are dynamically updated based on real-time data, adapting to the degradation characteristics at different stages. The model output includes the current degradation rate, acceleration trend, and possible range of change.

[0065] When using a thickness degradation rate model to predict remaining lifetime, the system first determines the coating's failure threshold, which is set based on the coating material's properties and engineering requirements. The prediction process employs time extrapolation, combining the current degradation rate and its accelerating trend to calculate the time required to reach the failure threshold. The prediction results include the expected value of the remaining lifetime and its possible range of variation, reflecting the uncertainty of the prediction.

[0066] Throughout the implementation process, data acquisition and processing adhered to stringent quality control standards. Salt spray concentration sensors were calibrated regularly to ensure measurement accuracy. The parameters of the Bernoulli distribution model were updated every 24 hours to reflect the latest environmental characteristics. Time series analysis employed a multi-scale approach, considering both short-term fluctuations and long-term trends. The degradation model's adaptive adjustment mechanism automatically optimized model parameters based on new data. Prediction results were updated every 6 hours, providing the most up-to-date lifetime assessment.

[0067] The system establishes a data verification mechanism to ensure data reliability by cross-referencing measurement results from different sensors. All processing steps are logged in detail, including data source, processing time, and usage parameters. A visual interface displays real-time concentration mutation events, degradation trends, and lifetime prediction results, facilitating operator monitoring of the entire process.

[0068] Table 1: Coating degradation monitoring data record table.

[0069] Time point Corrosion Deformation Index Rate of change of mean Standard deviation Degradation rate Remaining life prediction T1 0.15 0.002 0.012 0.005 2850 hours T2 0.18 0.003 0.014 0.006 2650 hours T3 0.22 0.004 0.016 0.007 2400 hours T4 0.27 0.005 0.018 0.008 2150 hours T5 0.33 0.006 0.021 0.009 1900 hours

[0070] Referring to Table 1, this table displays coating degradation-related data collected at continuous time points. The corrosion deformation index shows the cumulative amount of coating deformation, with its value gradually increasing over time. The mean change rate reflects the average rate of change of the index, showing an increasing trend. The standard deviation data indicates an increase in the dispersion of the measured values. The degradation rate data shows that the rate of material performance decline is accelerating. The remaining service life prediction, calculated based on the current degradation trend, shows that the expected remaining service life is gradually decreasing. These data provide a quantitative basis for coating performance evaluation and service life prediction.

[0071] Example 5: This example involves the visualization of the corrosion process and the feedback optimization of experimental parameters. The generation of the corrosion morphology evolution map begins with the spatial mapping of the surface morphology change characteristics of the coating. The 3D point cloud data of the surface acquired by the laser scanning system is transformed into coordinates and then imported into a 3D visualization engine. This engine uses a triangulation algorithm to convert discrete point cloud data into a continuous surface model, preserving the spatial characteristics of the original data. Each grid vertex contains position coordinates and corrosion depth information. Color coding technology is used to convert depth differences into visual differences. Areas with shallower depths are represented using cool colors, while areas with greater depths are rendered using warm colors, forming an intuitive corrosion distribution map. The map update mechanism dynamically adjusts the grid vertex positions and color attributes based on newly acquired data, reflecting the evolution of the corrosion morphology in real time.

[0072] The generation of the risk thermal distribution map requires overlaying the interlayer failure risk coefficient onto the corrosion morphology evolution map. The system establishes a spatial mapping relationship, associating the risk coefficient calculated for each monitoring node with its corresponding three-dimensional coordinates. The risk coefficient is presented on the base map using a transparency overlay algorithm; high-risk areas are displayed as a semi-transparent red overlay, while low-risk areas are displayed as a light blue overlay. The transparency of the overlay is proportional to the risk coefficient value, making high-risk areas more visually prominent. Simultaneously, the system adds risk level legends to the map edges, marking the risk coefficient range corresponding to different colors, facilitating accurate interpretation of the map information by observers.

[0073] The dynamic shading rendering process is based on real-time updated dynamic stability ratings. The rendering engine has a built-in color mapping rule library that associates different stability rating levels with color schemes. When the rating changes, the system automatically switches the color scheme for the entire graph. A high stability rating corresponds to a cool color scheme dominated by green, while a low stability rating triggers a warm color scheme dominated by red. Gradient transition technology is used during rendering; when the rating changes, the color scheme transitions smoothly within a set time interval, avoiding abrupt visual changes. Simultaneously, the system records historical shading data, supporting playback of color changes over specific time periods.

[0074] The test parameter feedback optimization process begins with a comprehensive analysis of remaining lifetime and dynamic stability rating. The optimization algorithm establishes a multi-objective decision model, simultaneously considering test acceleration requirements and sample protection needs. When a shortening of the remaining lifetime or a decline in the stability rating is detected, the system generates a salt spray test intensity adjustment command. This command includes specific parameter adjustment suggestions, such as reducing the salt spray concentration, adjusting the temperature setpoint, or changing the spray cycle. Historical data trends are referenced during command generation to avoid unnecessary adjustments due to short-term fluctuations.

[0075] The environmental control parameters for the salt spray test are updated based on probabilistically compensated salt spray deposition data and adjustment commands. The parameter optimization module employs a gradual adjustment strategy, decomposing the target parameter values ​​into multiple adjustment steps and implementing small adjustments within each control cycle. This strategy avoids sudden parameter changes that could impact the test system and maintain the stability of environmental conditions. The updated parameters are immediately sent to the environmental control system, changing the operating status of the salt spray test chamber in real time. Simultaneously, the system records parameter adjustment logs, including adjustment time, adjustment magnitude, and adjustment reason, providing a basis for subsequent analysis.

[0076] The closed-loop control system is implemented by re-inputting the updated salt spray test environment control parameters into the generation process of dynamic salt spray environment simulation conditions. This process forms a complete feedback loop, in which the environmental simulation conditions are continuously optimized based on actual monitoring results. The control system adopts a feedforward-feedback composite control mode. The feedforward link adjusts parameters in advance based on a predictive model, while the feedback link corrects adjustment errors based on real-time monitoring data. The system sets parameter adjustment thresholds, and automatically triggers an emergency adjustment procedure when the monitored indicators exceed the predetermined range, ensuring the safety and reliability of the test process.

[0077] Throughout the implementation, the visualization system employs a distributed rendering architecture, allocating the computational load across multiple graphics processing units. Real-time data streams are transmitted to rendering nodes via high-speed networks, ensuring timely updates to the visualization. The generation of the risk heatmap utilizes hierarchical detail technology, automatically adjusting rendering precision based on observation distance to balance visual appeal and computational efficiency. The color mapping rule library supports online updates, allowing users to customize color schemes according to specific needs. The parameter optimization module incorporates multiple adjustment strategies, allowing selection of appropriate strategies based on specific experimental requirements. The closed-loop control system incorporates multiple safety verification mechanisms to prevent parameter adjustments from exceeding the equipment's permissible range. Detailed event logs are maintained for all operations, supporting subsequent auditing and analysis. The system provides an interactive control interface, allowing operators to manually intervene in the automatic adjustment process, ensuring the overall system's flexibility and controllability.

[0078] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0079] 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 test method for salt spray corrosion testing of composite material layers in wind turbine blades, characterized in that, include: Obtain the preparation parameters of composite material coating for wind turbine blades; Based on preset salt spray test environment control parameters, dynamic salt spray environment simulation conditions are generated; A multi-sensor coupled monitoring network is constructed to collect real-time data on the corrosion process of the composite material coating; The real-time data is processed using a corrosion characteristic analysis algorithm to generate multi-dimensional corrosion characteristic indicators. The corrosion resistance stability of the new material coating is evaluated by combining the coating characteristic parameters and the multi-dimensional corrosion characteristic indicators.

2. The test method for salt spray corrosion testing of a wind turbine blade composite material layer according to claim 1, characterized in that, The method for obtaining the preparation parameters of the composite material coating for wind turbine blades is as follows: extract the thickness distribution data, chemical composition ratio data, and microstructure characteristic data of the new material coating; and generate a dynamic environmental correction factor based on the temperature and humidity reference values ​​in the salt spray test environment control parameters. The thickness distribution data and microstructure feature data are corrected in real time by applying the dynamic environment correction factor to obtain the environmentally adaptable coating parameters.

3. The test method for salt spray corrosion testing of a wind turbine blade composite material layer according to claim 2, characterized in that, The method for constructing the multi-sensor coupled monitoring network is as follows: design a monitoring node layout based on a directed topology, wherein the monitoring nodes are divided into coating corrosion layer monitoring nodes, interface bonding layer monitoring nodes, and substrate material layer monitoring nodes; according to the preset node information flow rules, the output data of the coating corrosion layer monitoring nodes is transmitted to the interface bonding layer monitoring nodes, and the output data of the interface bonding layer monitoring nodes is transmitted to the substrate material layer monitoring nodes; after completing one full node data interaction, the state estimate of each monitoring node is updated.

4. The test method for salt spray corrosion testing of a composite material layer for wind turbine blades according to claim 3, characterized in that, The method for generating the multi-dimensional corrosion characteristic index is as follows: extract coating surface morphology change characteristic data, interface peeling rate data, and substrate stress distribution data from the real-time data; process the coating surface morphology change characteristic data and interface peeling rate data using a weighted fusion algorithm to generate a comprehensive corrosion deformation index; and calculate the interlayer failure risk coefficient of the material by combining the substrate stress distribution data and the comprehensive corrosion deformation index.

5. The test method for salt spray corrosion testing of a wind turbine blade composite material layer according to claim 4, characterized in that, The method for generating the dynamic environmental correction factor includes: collecting real-time temperature fluctuation data, humidity offset data, and salt spray deposition data in the salt spray test chamber; calculating the temperature and humidity coupling influence weight based on the real-time temperature fluctuation data and humidity offset data; and introducing salt spray deposition data to compensate and correct the temperature and humidity coupling influence weight to generate a comprehensive salt spray environmental influence index.

6. The test method for salt spray corrosion testing of a composite material layer for wind turbine blades according to claim 5, characterized in that, The method for assessing the corrosion resistance stability is as follows: calculate the stability fluctuation rate of each monitoring node based on the interlayer failure risk coefficient of the material; mark high-risk corrosion areas based on the stability fluctuation rate and the preset coating failure threshold; Based on the comprehensive impact index of the salt spray environment and the labeling results, the dynamic stability rating of the new material coating is output.

7. The test method for salt spray corrosion testing of a composite material layer for wind turbine blades according to claim 6, characterized in that, It also includes a method for handling random salt spray concentration interference: monitoring instantaneous deviation data of salt spray deposition concentration and constructing a Bernoulli distribution model to describe concentration mutation events; performing probability compensation correction on the salt spray deposition data based on the probability parameters of the Bernoulli distribution model; and feeding back the compensated salt spray deposition data to the generation process of the dynamic environmental correction factor.

8. The test method for salt spray corrosion testing of a composite material layer for wind turbine blades according to claim 7, characterized in that, It also includes a coating performance degradation modeling method: obtaining time series data of the comprehensive corrosion deformation index; extracting the mean change rate and standard deviation of the time series data; constructing a coating thickness degradation rate model based on the mean change rate and standard deviation; and applying the thickness degradation rate model to predict the remaining life cycle of the new material coating.

9. The test method for salt spray corrosion testing of a composite material layer for wind turbine blades according to claim 8, characterized in that, It also includes a method for visualizing the corrosion process: mapping the surface morphology change feature data of the coating to a three-dimensional spatial coordinate system to generate a corrosion morphology evolution map; The interlayer failure risk coefficient of the material is superimposed onto the corrosion morphology evolution map to form a risk thermal distribution map; The risk heat map is dynamically colored and rendered based on the dynamic stability rating. It also includes a test parameter feedback optimization method: generating a salt spray test intensity adjustment command based on the remaining lifespan and dynamic stability rating; updating the salt spray test environment control parameters based on the probability-compensated salt spray deposition data and the salt spray test intensity adjustment command; and inputting the updated salt spray test environment control parameters into the generation process of the dynamic salt spray environment simulation conditions.

10. A testing device for salt spray corrosion testing of a composite material layer on a wind turbine blade, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the test method for salt spray corrosion testing of the composite material layer of a wind turbine blade as described in any one of claims 1 to 9.

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