Electrochemical impedance-dissolved hydrogen boiler four tube monitoring system

Abstract

The present application relates to the technical field of boiler pipeline safety monitoring, in particular to an electrochemical impedance-dissolved hydrogen boiler four-pipe monitoring system. Through multi-modal collaborative monitoring technology, electrochemical impedance spectroscopy, dissolved hydrogen photoacoustic spectroscopy detection and ultrasonic thickness monitoring are integrated. An adaptive modal weight dynamic adjustment mechanism, a corrosion product layer thickness inversion module, a dissolved hydrogen photoacoustic signal enhancement algorithm, a multi-scale time series feature extraction module and a warning threshold dynamic updating mechanism are innovatively introduced. A CNN and attention mechanism Transformer hybrid model is used to fuse three modal data, a corrosion-leakage dynamic correlation model is established, and early corrosion and leakage of the boiler four-pipe are accurately warned. The present application solves the problems of one-sidedness of traditional single modal monitoring, inadaptability of data fusion to working condition changes, weak signal anti-interference ability, false alarm and missed alarm caused by fixed warning threshold, significantly improves the monitoring accuracy, warning accuracy and working condition adaptability, and provides reliable protection for safe operation of the boiler four-pipe.

Description

Technical Field

[0001] This invention relates to the field of boiler pipeline safety monitoring technology, specifically to an electrochemical impedance-dissolved hydrogen boiler four-tube monitoring system. Background Technology

[0002] The boiler tubes are core pressure-bearing components of thermal power generating units. They operate under complex conditions of high temperature, high pressure, high humidity, and corrosive media (such as flue gas and steam containing sulfur and chloride ions), making them prone to corrosion, wear, fatigue cracking, and ultimately leakage accidents. Statistics show that boiler tube leaks account for over 60% of unplanned shutdowns in thermal power generating units, causing not only huge economic losses but also potential safety incidents. Therefore, achieving early and accurate warnings of boiler tube corrosion leaks is crucial for ensuring the safe and stable operation of the unit.

[0003] Existing boiler four-tube monitoring technologies are mainly divided into two categories: single-modal monitoring and preliminary multi-modal fusion monitoring, but both have significant shortcomings. In single-modal monitoring, electrochemical impedance spectroscopy (EIS) can reflect the impedance changes of the electrochemical corrosion process on the pipe surface, but it cannot capture non-electrochemical failure mechanisms such as hydrogen-induced cracking and corrosion product layer detachment, and it is easily affected by environmental factors such as temperature and humidity; dissolved hydrogen photoacoustic spectroscopy can predict the risk of hydrogen-induced corrosion through changes in dissolved hydrogen concentration, but it is not sensitive enough to failure modes such as uniform corrosion and localized wear; ultrasonic thickness monitoring technology can directly measure the remaining wall thickness of the pipe, but it is affected by the coverage of corrosion product layer and the surface roughness of the pipe, resulting in large measurement errors, and it cannot reflect the corrosion kinetic process.

[0004] Multimodal preliminary fusion monitoring technology attempts to integrate data from two or more modalities, but it suffers from three major problems: First, the data fusion method is simple, often employing static fusion methods such as weighted averaging and feature splicing, without considering the differences in the effectiveness of each modal data under different operating conditions, resulting in low fusion accuracy. Second, the signal processing capability is weak; vibration, electromagnetic interference, and temperature fluctuations during boiler operation can reduce the signal-to-noise ratio of modal signals, and existing technologies lack targeted signal enhancement and noise suppression algorithms, affecting the accuracy of feature extraction. Third, the correlation model and early warning mechanism are rigid; corrosion-leakage correlation models are mostly static models, failing to consider the influence of dynamic factors such as corrosion product layers and operating time, and the early warning thresholds are mostly fixed values, unable to adapt to changes in operating conditions such as unit load fluctuations and changes in medium composition, resulting in persistently high false alarm and false alarm rates.

[0005] Furthermore, existing technologies do not consider the impact of corrosion product layers on monitoring results. The formation of corrosion product layers alters the electrochemical properties of the pipe surface and the ultrasonic propagation path, leading to distortion of EIS signals and ultrasonic thickness measurements, thus affecting the accuracy of corrosion assessment. Simultaneously, traditional feature extraction methods often focus on single-scale features, failing to comprehensively capture short-term fluctuations in the corrosion process (such as sudden changes in corrosion rate caused by instantaneous changes in medium concentration) and long-term trends (such as continuous reduction in wall thickness due to cumulative corrosion), resulting in incomplete input features for the correlation model. These problems collectively make it difficult for existing monitoring systems to achieve early and accurate warnings of corrosion leaks in boiler four-tube systems, failing to meet actual industrial needs. Therefore, developing a multimodal collaborative monitoring system with dynamic fusion, strong anti-interference capabilities, accurate models, and adaptive thresholds has become an urgent technical challenge to be addressed. Summary of the Invention

[0006] To address the problems in existing technologies, this invention provides an electrochemical impedance-dissolved hydrogen boiler four-tube monitoring system. Through the organic integration of multi-dimensional innovative technologies, it solves problems such as the one-sidedness of single-mode monitoring, the inability of data fusion to adapt to operating conditions, weak signal anti-interference, and inaccurate early warning, thereby achieving early and accurate early warning of corrosion leakage in the four boiler tubes.

[0007] The technical solution adopted by this invention to solve its technical problem is: an electrochemical impedance-dissolved hydrogen boiler four-tube monitoring system, comprising: The sensor module includes an electrochemical impedance spectroscopy (EIS) sensor, a dissolved hydrogen photoacoustic spectroscopy sensor, and an ultrasonic thickness sensor, which respectively collect electrochemical corrosion signals, dissolved hydrogen concentration signals, and pipe thickness signals of the four boiler tubes. The data acquisition module is connected to the sensor module to synchronously acquire the three-modal raw signals and convert them into digital signals; The preprocessing module filters and reduces noise in the digital signal. The dissolved hydrogen photoacoustic signal is processed using an enhancement algorithm that combines wavelet threshold denoising with adaptive gain adjustment. The feature extraction module extracts local features of the three-modal signal through CNN, and extracts short-term fluctuation and long-term trend features by combining multi-scale time series decomposition to obtain multi-dimensional feature vectors; The data fusion module, based on the attention mechanism Transformer model, introduces an adaptive modal weight dynamic adjustment mechanism to dynamically allocate and fuse the three modal feature weights according to real-time operating parameters. The correlation model module, combined with the correction parameters output by the corrosion product layer thickness inversion module, establishes a corrosion-leakage dynamic correlation model. The corrosion product layer thickness is obtained by coupling and inverting the EIS signal and the ultrasonic thickness signal. The early warning module uses a reinforcement learning algorithm to build a dynamic update mechanism for early warning thresholds. It updates the thresholds based on equipment operation data and historical records, and outputs early warning information. The storage module stores data from each module and model parameters.

[0008] Specifically, the adaptive modal weight dynamic adjustment mechanism takes real-time boiler temperature, pressure, and medium composition data as input, calculates the effectiveness coefficient of each mode through a radial basis function neural network, and dynamically adjusts the fusion weight.

[0009] Specifically, the corrosion product layer thickness inversion module calculates the corrosion product layer thickness based on the charge transfer resistance and double-layer capacitance parameters of EIS and the ultrasonic thickness measurement values, and corrects the actual corrosion depth of the pipeline using an inversion model optimized by a genetic algorithm.

[0010] Specifically, the multi-scale time series decomposition adopts variational mode decomposition (VMD), with 3-5 decomposition layers, corresponding to the micro-instantaneous fluctuations, meso-periodic changes and macro-trend evolution of the corrosion process, respectively.

[0011] Specifically, in the hybrid model of CNN and attention mechanism Transformer, the CNN uses 3 convolutional layers with a kernel size of 3×3, and the Transformer encoder contains 6 encoding layers, each with 8 attention heads, and the output dimension is 256 dimensions.

[0012] Specifically, the reinforcement learning algorithm is a deep Q-network (DQN), which uses the warning accuracy, false alarm rate, and false negative rate as reward functions, and updates the warning threshold every 72 hours.

[0013] Specifically, the electrochemical impedance spectroscopy sensor has a test frequency range of 10mHz-100kHz, and the excitation signal is a sinusoidal AC signal with an amplitude of 5mV. It is installed in areas of high corrosion incidence on the inner wall of the pipeline.

[0014] Specifically, the dissolved hydrogen photoacoustic spectroscopy sensor uses a near-infrared laser light source with a wavelength of 1.57 μm, maintains the photoacoustic cell pressure at 0.1-0.3 MPa, and has a signal sampling frequency of 1 kHz.

[0015] Specifically, the ultrasonic thickness sensor has a probe frequency of 5MHz, a measurement range of 3-50mm, a resolution of ±0.01mm, and is installed at an angle of 0° to the pipe surface.

[0016] Specifically, the corrosion-leakage dynamic correlation model is optimized by combining multiple linear regression with a BP neural network. The input is a fused feature vector and the thickness of the corrosion product layer, and the output is the corrosion rate, leakage risk probability, and remaining lifetime prediction.

[0017] The beneficial effects of this invention are: Multimodal collaboration and dynamic fusion: By complementing three modes—EIS, dissolved hydrogen photoacoustic spectroscopy, and ultrasonic thickness—and combining them with an adaptive modal weight dynamic adjustment mechanism, the problem of traditional static fusion being unable to adapt to changes in operating conditions is solved. Under complex operating conditions such as high temperature and high pressure, and fluctuations in medium composition, the weights of each mode are dynamically adjusted according to the effectiveness coefficient, improving the fusion accuracy by more than 30% compared to traditional weighted averaging, ensuring the relevance and accuracy of data fusion.

[0018] Signal anti-interference and feature comprehensiveness: In response to the strong interference environment of boiler operation, the dissolved hydrogen photoacoustic signal is enhanced by wavelet threshold denoising and adaptive gain adjustment, improving the signal-to-noise ratio to over 30dB; feature extraction adopts multi-scale time series decomposition combined with CNN to comprehensively capture the micro-instantaneous fluctuations, meso-scale periodic changes and macro-scale trend evolution of the corrosion process. The feature dimension is increased by 2 times compared with traditional methods, providing a more comprehensive input basis for the correlation model.

[0019] Improved Corrosion Assessment Precision: By introducing a corrosion product layer thickness inversion module, the interference of the corrosion product layer on the monitoring results is corrected through the coupling inversion of EIS signal and ultrasonic thickness signal. The actual corrosion depth measurement error of the pipeline is ≤ ±0.02mm, which is 60% lower than the existing technology, significantly improving the accuracy of corrosion assessment.

[0020] Adaptive early warning and high reliability: A dynamic update mechanism for early warning thresholds is constructed using reinforcement learning. The thresholds are adaptively adjusted every 72 hours based on operational data and historical records. The false alarm rate is ≤3%, the false alarm rate is ≤2%, and the early warning accuracy is ≥95%. Compared with traditional fixed threshold early warning systems, the false alarm rate is reduced, providing a reliable guarantee for early corrosion and leakage warning.

[0021] The system is highly practical: the sensors are embedded and can be adapted to the complex structure of areas with high corrosion rates in the four tubes of boilers; the data acquisition cycle can be flexibly adjusted, and data storage and historical traceability are supported, meeting the actual industrial needs of different fields such as thermal power, nuclear power, and chemical industry, and has a wide range of applications. Attached Figure Description

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] Figure 1 The diagram shows the architecture of the electrochemical impedance-dissolved hydrogen boiler four-tube monitoring system provided by this invention. Detailed Implementation

[0024] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0025] like Figure 1As shown, the electrochemical impedance-dissolved hydrogen boiler four-tube monitoring system of the present invention includes a sensor module, a data acquisition module, a preprocessing module, a feature extraction module, a data fusion module, a correlation model module, an early warning module, and a storage module. These modules are electrically connected in sequence to form a closed-loop monitoring system. The specific technical solution is as follows: Sensor Module: This module integrates an electrochemical impedance spectroscopy (EIS) sensor, a dissolved hydrogen photoacoustic spectroscopy sensor, and an ultrasonic thickness sensor. All are embedded and fixed in high-corrosion areas of the boiler's four tubes (such as the lower header of the water-cooled wall and superheater elbows). The EIS sensor uses a three-electrode system (working electrode, reference electrode, and auxiliary electrode). The working electrode is made of the same material as the pipe base material (e.g., 20G steel), the reference electrode is an Ag / AgCl electrode, and the auxiliary electrode is a platinum electrode. It is used to collect electrochemical impedance parameters of the pipe surface (charge transfer resistance Rct, double-layer capacitance Cdl, polarization resistance Rp). The dissolved hydrogen photoacoustic spectroscopy sensor includes a near-infrared laser source, a photoacoustic cell, and a microphone detector. The laser wavelength is 1.57 μm (characteristic absorption peak of dissolved hydrogen). The photoacoustic cell is made of corrosion-resistant stainless steel and has a built-in temperature compensation module for collecting dissolved hydrogen concentration signals. The ultrasonic thickness sensor uses a piezoelectric ceramic probe at a frequency of 5 MHz and is equipped with an automatic coupling agent replenishment device for collecting pipe wall thickness data.

[0026] Data acquisition module: It adopts a multi-channel synchronous data acquisition card (sampling rate 1MHz, resolution 16-bit), which is connected to the sensor module through a shielded cable. It synchronously acquires three-mode raw signals, converts analog signals into digital signals, and transmits the data to the preprocessing module via Ethernet. The acquisition cycle can be set to 1-10 minutes (adjusted according to the complexity of the working conditions).

[0027] Preprocessing module: Preprocesses digital signals, including: EIS signals using adaptive Kalman filtering to remove electromagnetic interference; ultrasonic thickness signals using median filtering to remove random noise caused by surface roughness; dissolved hydrogen photoacoustic signals using wavelet threshold denoising plus adaptive gain adjustment enhancement algorithm, with the wavelet basis selected as db4, the number of decomposition layers as 5, the threshold calculated using an improved Birge-Massart criterion, and the adaptive gain adjustment dynamically adjusting the amplification factor based on the signal-to-noise ratio to improve the signal-to-noise ratio to above 30dB.

[0028] Feature extraction module: It adopts a multi-scale decomposition plus CNN structure. First, the three-modal preprocessed signal is decomposed into 3-5 layers (corresponding to 10ms-10min time scale) through variational mode decomposition (VMD). The temporal features (peak, mean, variance, kurtosis) and frequency features (center frequency, spectral energy, harmonic components) of each scale are extracted respectively. Then, the multi-scale features are input into a 3-layer CNN network with a convolution kernel size of 3×3, a stride of 1, and a ReLU activation function to extract local correlation features. Finally, the output is a 128-dimensional multi-scale fused feature vector.

[0029] Data fusion module: A dynamic fusion framework is constructed based on the Transformer model with an attention mechanism. The Transformer encoder contains 6 encoding layers, each with 8 attention heads, and an output dimension of 256. An adaptive modal weight dynamic adjustment mechanism is introduced. Taking the boiler's real-time temperature (0-600℃), pressure (0-25MPa), and medium composition (H2O, H2, O2, SO2 concentration) as input, the effectiveness coefficients (range 0-1) of each modal feature are calculated through a radial basis function neural network (RBFNN). The attention weights of each modal feature in the Transformer model are dynamically adjusted to achieve adaptive matching of operating conditions, modalities, and weights.

[0030] The correlation model module constructs a dynamic correlation model between corrosion and leakage, including a corrosion product layer thickness inversion submodule and a correlation prediction submodule. The corrosion product layer thickness inversion submodule calculates the corrosion product layer thickness based on EIS charge transfer resistance Rct, double-layer capacitance Cdl, and ultrasonic thickness measurements. It uses a genetic algorithm-optimized inversion model (with the objective function being the minimum mean square error between measured and theoretical values) to correct for the actual corrosion depth of the pipeline. The correlation prediction submodule employs a hybrid model of multiple linear regression and BP neural network. Its inputs are a 256-dimensional feature vector output from the data fusion module and the corrosion product layer thickness. The outputs are corrosion rate (mm / a), leakage risk probability (0-100%), and remaining lifetime (h).

[0031] Early warning module: A dynamic update mechanism for early warning thresholds is constructed using reinforcement learning (DQN). The reward functions are early warning accuracy (target ≥95%), false alarm rate (target ≤3%), and false negative rate (target ≤2%). The status inputs are equipment runtime, historical corrosion data, and maintenance records. The early warning thresholds (including corrosion rate threshold, leakage risk probability threshold, and remaining lifespan threshold) are updated every 72 hours. When a monitored value exceeds the corresponding threshold, early warning information is output through three methods: audible and visual alarms, SMS notifications, and pop-up windows on the host computer, along with the risk level (Level I-IV).

[0032] Storage module: Uses industrial-grade solid-state drives (capacity ≥1TB) to store raw signals, preprocessed data, feature vectors, fusion results, early warning records, etc., with a storage period of ≥3 years, and supports data export and historical traceability.

[0033] Example 1: Monitoring of water-cooled walls in thermal power boilers (rated operating conditions) 1. Implementation conditions Monitoring object: Water-cooled wall of a 300MW thermal power boiler (material: 20G steel, design wall thickness: 12mm, service life: 5 years). The high-risk corrosion area is the lower header and pipe interface (temperature: 350-400℃, pressure: 18-20MPa, medium: saturated steam plus a small amount of condensate containing chloride and sulfur ions). - Concentration 0.1-0.3 mg / L, SO4 2- Concentration 0.2-0.5 mg / L.

[0034] System Configuration: Sensor Module: The EIS sensor employs a three-electrode system. The working electrode is made of the same material as the pipe base (20G steel), the reference electrode is an Ag / AgCl electrode, and the auxiliary electrode is a platinum electrode. The test frequency range is 10mHz-100kHz, and the excitation signal is a sinusoidal AC signal with an amplitude of 5mV. The dissolved hydrogen photoacoustic spectroscopy sensor uses a near-infrared laser light source (wavelength 1.57μm), and the photoacoustic cell is made of corrosion-resistant stainless steel. It has a built-in temperature compensation module, maintains the photoacoustic cell pressure at 0.2MPa, and has a signal sampling frequency of 1kHz. The ultrasonic thickness sensor probe has a frequency of 5MHz, a measurement range of 3-50mm, a resolution of ±0.01mm, and is equipped with an automatic coupling agent replenishment device. All three sensors are embedded and fixed in high-corrosion areas, with a 50mm spacing between adjacent sensors to ensure coordinated signal acquisition.

[0035] Data acquisition module: It adopts a multi-channel synchronous data acquisition card (model NI-6363), with a sampling rate of 1MHz and a resolution of 16 bits. It is connected to the sensor module through a shielded cable, and the acquisition cycle is set to 5 minutes. It synchronously acquires three-mode raw signals and converts them into digital signals, which are then transmitted to the preprocessing module via Ethernet.

[0036] Preprocessing module: The EIS signal is filtered using an adaptive Kalman filter (process noise covariance Q=0.005, observation noise covariance R=0.002) to remove electromagnetic interference; the ultrasonic thickness signal is filtered using a median filter (window size 5) to remove random noise caused by pipe surface roughness; the dissolved hydrogen photoacoustic signal is enhanced using a 5-level db4 wavelet decomposition plus an improved Birge-Massart criterion threshold denoising plus adaptive gain adjustment algorithm. The threshold is calculated using the improved Birge-Massart criterion (introducing a signal amplitude weighting coefficient), and the adaptive gain adjustment dynamically adjusts the amplification factor based on the real-time signal-to-noise ratio of the signal to ensure that the signal-to-noise ratio of the processed signal is improved to above 32dB.

[0037] Feature extraction module: It adopts a multi-scale decomposition plus CNN structure. The multi-scale time series decomposition adopts variational mode decomposition (VMD) with 3 decomposition layers, corresponding to time scales of 10ms (micro-instantaneous fluctuations), 1min (meso-level periodic changes), and 5min (macro-level trend evolution). It extracts the time domain features (peak value, mean, variance, kurtosis) and frequency domain features (center frequency, spectral energy, harmonic components) of the signal at each scale, for a total of 24 basic features. The CNN network contains 3 convolutional layers with a kernel size of 3×3, a stride of 1, and the activation function is ReLU. The pooling layer adopts max pooling (pooling kernel 2×2), and finally outputs a 128-dimensional multi-dimensional feature vector.

[0038] Data fusion module: A dynamic fusion framework is constructed based on the Transformer model with an attention mechanism. The Transformer encoder contains 6 encoding layers, each with 8 attention heads, and an output dimension of 256. An adaptive modal weight dynamic adjustment mechanism is introduced, taking into account the real-time temperature, pressure, and Cl content of the boiler medium. - Concentration, SO4 2- Concentration is used as the input parameter. The effectiveness coefficients (ranging from 0 to 1) of each mode are calculated using a radial basis function neural network (RBFNN). The attention weights of the three-modal features in the Transformer model are dynamically adjusted to achieve adaptive matching of working conditions, modes, and weights.

[0039] The correlation model module includes a corrosion product layer thickness inversion submodule and a correlation prediction submodule. The corrosion product layer thickness inversion submodule calculates the corrosion product layer thickness based on the charge transfer resistance (Rct) and double-layer capacitance (Cdl) parameters of the EIS signal and ultrasonic thickness measurements. It uses a genetic algorithm-optimized inversion model (population size 50, iterations 100, objective function is the minimum mean square error between measured and theoretical values) to calculate the corrosion product layer thickness. The correlation prediction submodule employs a hybrid model of multiple linear regression and a backpropagation (BP) neural network. The inputs are a 256-dimensional feature vector output from the data fusion module and the corrosion product layer thickness (a total of 257 dimensions). The BP neural network has three hidden layers with 128, 64, and 32 neurons respectively, using ReLU as the activation function. The output layer outputs the corrosion rate (mm / a), leakage risk probability (0-100%), and remaining lifetime (h).

[0040] Early warning module: A dynamic update mechanism for early warning thresholds is constructed using a Deep Q-Network (DQN) reinforcement learning algorithm. The reward functions are early warning accuracy (weight 0.6), false alarm rate (weight 0.2), and false negative rate (weight 0.2). The status inputs are equipment runtime, historical corrosion data, and maintenance records. The early warning thresholds (including corrosion rate threshold, leakage risk probability threshold, and remaining lifespan threshold) are updated every 72 hours. The early warning levels are divided into Level I (leakage risk probability < 30%, safe state), Level II (30%-60%, alert state), Level III (60%-80%, early warning state), and Level IV (> 80%, emergency state). When the monitored value exceeds the corresponding threshold, the early warning information is output through three methods: audible and visual alarm, SMS notification, and upper computer pop-up window.

[0041] Storage module: It adopts an industrial-grade solid-state drive (capacity 2TB) to store raw signals, preprocessed data, feature vectors, fusion results, early warning records and other data. The storage period is set to 5 years, and it supports data export (in Excel and CSV formats) and historical traceability.

[0042] 2. Implementation process (1) System deployment and calibration: The sensor module is embedded in the lower header and pipe interface of the water-cooled wall to ensure that the sensor is in close contact with the pipe surface and that the working electrode is electrically connected to the pipe base material; the data acquisition module is calibrated to ensure the synchronization of the three-mode signal acquisition (synchronization error ≤ 1ms); the associated model module is initialized and the boiler's 5-year operating history data (corrosion rate, leakage record, operating parameters) is input to complete the model pre-training.

[0043] (2) Data acquisition: After the system is started, the data acquisition module synchronously acquires EIS signals (charge transfer resistance Rct, double layer capacitance Cdl, polarization resistance Rp), dissolved hydrogen concentration signal (0.05-0.15mL / L), and ultrasonic thickness signal (initial measurement value 11.85mm) at a 5-minute cycle. After converting the analog signals into digital signals, they are transmitted to the preprocessing module via Ethernet.

[0044] (3) Signal preprocessing: The preprocessing module performs targeted processing on the three-mode digital signals: the EIS signal is filtered by an adaptive Kalman filter to remove electromagnetic interference generated by boiler operation and obtain a stable impedance parameter time series; the ultrasonic thickness signal is filtered by a median filter to remove random noise caused by pipe surface roughness and correct measurement deviation; the dissolved hydrogen photoacoustic signal is decomposed by a 5-layer db4 wavelet, and the background noise is removed by calculating the threshold through the improved Birge-Massart criterion, and then the signal-to-noise ratio is increased to 32dB by adaptive gain adjustment to obtain a clear dissolved hydrogen concentration signal.

[0045] (4) Feature extraction: The feature extraction module first decomposes the preprocessed trimodal signal into three multi-scale signals (10ms, 1min, 5min) through VMD, and extracts the time domain features (peak value, mean, variance, kurtosis) and frequency domain features (center frequency, spectral energy, harmonic components) of each scale signal, for a total of 24 basic features; then the multi-scale basic features are input into a three-layer CNN network, and local correlation features are extracted through convolution operation. After dimensionality reduction by pooling layer, a 128-dimensional multi-dimensional feature vector is output.

[0046] (5) Data Fusion: In the data fusion module, RBFNN uses real-time operating parameters (temperature 380℃, pressure 19MPa, Cl) to perform data fusion. - Concentration 0.2 mg / L, SO4 2- The effectiveness coefficients for each mode were calculated at a concentration of 0.3 mg / L: EIS mode 0.85 (reflecting electrochemical corrosion sensitivity), dissolved hydrogen mode 0.78 (reflecting hydrogen-induced corrosion sensitivity), and ultrasonic mode 0.92 (reflecting the accuracy of wall thickness variation). The Transformer model dynamically adjusted the attention weights based on these effectiveness coefficients, fused the 128-dimensional feature vectors, and output a 256-dimensional fused feature vector.

[0047] (6) Correlation model calculation: The input EIS signal Rct of the corrosion product layer thickness inversion submodule is 5000Ω·cm 2 Cdl=20μF / cm 2The ultrasonic thickness measurement value is 11.85 mm. The corrosion product layer thickness is calculated to be 0.12 mm using the inversion model optimized by the genetic algorithm. After correction, the actual corrosion depth of the pipeline = initial wall thickness - current measured wall thickness - corrosion product layer thickness = 12 - 11.85 - 0.12 = 0.03 mm. The correlation prediction submodule takes 257-dimensional data (256-dimensional fused features + 0.12 mm corrosion product layer thickness) as input. After preliminary fitting by multiple linear regression and optimization by BP neural network, it outputs a corrosion rate of 0.08 mm / a, a leakage risk probability of 25%, and a remaining life of 87,600 h (10 years).

[0048] (7) Warning output and data storage: The warning module calls the warning threshold dynamically updated by DQN (the warning threshold for Level II is a leakage risk probability of 30%). The current leakage risk probability is 25% < 30%, so it outputs a Level I warning (safe status) and stores the original signal, preprocessed data, feature vector, fusion result, warning record and other data to an industrial-grade solid-state drive.

[0049] 3. Implementation Results After 30 days of continuous operation, the system experienced no fault-free shutdowns, demonstrating excellent operational stability. The corrosion rate measurement error was ±0.005 mm / a, the leakage risk probability prediction error was ±2%, and the remaining life prediction error was ±3600 h, indicating significantly superior monitoring accuracy compared to existing technologies. During this period, the system experienced two load fluctuations (250MW-300MW-280MW), and the adaptive modal weight dynamic adjustment mechanism responded quickly. The EIS modal effectiveness coefficient fluctuated between 0.75 and 0.90, the dissolved hydrogen mode fluctuated between 0.70 and 0.85, and the ultrasonic mode fluctuated between 0.85 and 0.95, maintaining stable fusion accuracy (correlation coefficient between the fused feature vector and the actual corrosion state ≥0.98). Throughout the entire operating cycle, the false alarm rate was 0.8%, the missed alarm rate was 0%, and the early warning accuracy rate was 99.2%, fully validating the system's monitoring accuracy and reliability under rated operating conditions.

[0050] Example 2: Monitoring of superheaters in nuclear power boilers (variable load condition) 1. Implementation conditions Monitoring object: Superheater of a 1000MW nuclear power boiler (material TP304 stainless steel, design wall thickness 15mm, service life 3 years). The high corrosion rate area is the elbow (temperature 500-580℃, pressure 22-25MPa, medium is superheated steam with trace amount of hydrogen, hydrogen concentration 0.1-0.3mL / L). The unit load fluctuates frequently (800MW-1000MW-900MW cycle, once every 6 hours).

[0051] System Configuration: Sensor module: The working electrode of the EIS sensor is made of TP304 stainless steel, and the other parameters are the same as in Example 1; the pressure of the photoacoustic cell of the dissolved hydrogen photoacoustic spectroscopy sensor is maintained at 0.3MPa, and a pressure compensation module is added to adapt to pressure fluctuations under variable load; the ultrasonic thickness sensor is installed on the outside of the elbow and is equipped with an automatic replenishment device for high-temperature coupling agent (temperature resistance 600℃). The three sensors are spaced 80mm apart and are embedded.

[0052] Data acquisition module: adopts a multi-channel synchronous data acquisition card (model NI-6366), with a sampling rate of 1MHz, a resolution of 16 bits, and an acquisition cycle of 3 minutes, adapting to rapid signal changes under variable load conditions, with a synchronization error ≤0.5ms.

[0053] Preprocessing module: Dynamic adjustment of adaptive Kalman filter parameters for EIS signal (process noise covariance Q=0.01, observation noise covariance R=0.005) to adapt to electromagnetic interference changes under varying loads; signal-to-noise ratio of dissolved hydrogen photoacoustic signal is increased to 35dB; median filter window size of ultrasonic signal is adjusted to 7 to enhance noise suppression capability.

[0054] Feature extraction module: The VMD decomposition has 4 layers, corresponding to time scales of 10ms (micro instantaneous fluctuation), 30s (short-term load fluctuation response), 3min (collection period features), and 10min (long-term trend), respectively, extracting 32 basic features (8 features per scale). The CNN network structure is consistent with that in Example 1, and outputs a 128-dimensional feature vector.

[0055] Data fusion module: The RBFNN input parameters are increased with unit load (800-1000MW) to dynamically calculate the effectiveness coefficients of each mode; the Transformer model maintains 6 encoding layers, 8 attention heads, and 256 output dimensions.

[0056] The correlation model module has increased the number of iterations of the genetic algorithm in the corrosion product layer thickness inversion submodule to 150, improving the inversion accuracy; the number of neurons in the hidden layer of the BP neural network in the correlation prediction submodule has been adjusted to 256, 128, and 64, enhancing the fitting ability to complex features under variable load conditions.

[0057] Early warning module: The load fluctuation coefficient weight (0.1) is added to the DQN reward function, and the reward function is adjusted to early warning accuracy (0.5), false alarm rate (0.2), false alarm rate (0.2), and load fluctuation adaptability (0.1). The threshold update cycle remains 72 hours.

[0058] Storage module: 2TB industrial-grade solid-state drive with a 5-year storage lifespan, supporting real-time data and historical data comparison and analysis.

[0059] 2. Implementation process (1) System deployment and calibration: The sensor is installed on the outside of the superheater elbow to ensure that the angle between the probe and the pipe surface is 0° and the automatic coupling agent replenishment device is in close contact with the pipe surface; the data acquisition module is synchronously calibrated to ensure the synchronization of the three-mode signal acquisition under variable load conditions; input the 3-year operating history data of the nuclear power boiler (including load fluctuation records, corrosion data, and leakage records) to complete the pre-training of the correlation model.

[0060] (2) Data acquisition: After the system starts, EIS signals (Rct=8000Ω·cm) are acquired synchronously at a 3-minute cycle. 2 Cdl=15μF / cm 2 The data acquisition module tracks the unit load changes in real time (800MW-1000MW-900MW), converts the analog signals into digital signals, and then transmits them to the preprocessing module.

[0061] (3) Signal preprocessing: In response to the increased vibration interference caused by load fluctuations, the EIS signal suppresses electromagnetic interference by dynamically adjusting the Kalman filter parameters; the dissolved hydrogen photoacoustic signal is denoised by wavelet and adaptive gain adjustment, and the signal-to-noise ratio is stabilized at 35dB; the ultrasonic signal is filtered by median (window size 7) to remove noise caused by surface roughness and vibration, ensuring signal quality.

[0062] (4) Feature extraction: The signal changes at different time scales are captured by decomposing the VMD4 layer, especially the short- and medium-term features (30s, 3min) caused by load fluctuations. 32 basic features are extracted (4 time-domain features and 4 frequency-domain features at each scale). After extracting local correlation features through the CNN network, a 128-dimensional feature vector is output to fully reflect the corrosion characteristics under variable load.

[0063] (5) Data fusion: When the unit load drops from 1000MW to 800MW, the temperature drops to 520℃ and the pressure drops to 23MPa. RBFNN calculates the EIS mode effectiveness coefficient as 0.70 (the temperature decrease leads to a decrease in electrochemical corrosion sensitivity), the dissolved hydrogen mode as 0.85 (the hydrogen concentration fluctuation sensitivity increases), and the ultrasonic mode as 0.80 (the wall thickness measurement stability is moderate). The Transformer model dynamically adjusts the attention weight to highlight the dissolved hydrogen mode characteristics. When the load rises back to 1000MW, the temperature rises to 570℃ and the pressure rises to 25MPa. The effectiveness coefficients are adjusted to EIS=0.88 (the electrochemical corrosion sensitivity increases), dissolved hydrogen=0.75 (the hydrogen concentration is stable), and ultrasonic=0.90 (the wall thickness measurement stability increases). The Transformer model focuses on fusing the EIS and ultrasonic mode characteristics and outputs a 256-dimensional fused feature vector.

[0064] (6) Correlation model calculation: Rct = 8000 Ω·cm of the input EIS signal to the corrosion product layer thickness inversion submodule. 2 Cdl=15μF / cm 2 With an ultrasonic thickness measurement of 14.92 mm, the corrosion product layer thickness was calculated to be 0.08 mm using an inversion model optimized by a genetic algorithm (population size 50, iteration count 150). After correction, the actual corrosion depth of the pipeline = initial wall thickness - current measured wall thickness - corrosion product layer thickness = 15 - 14.92 - 0.08 = 0 mm (in the initial corrosion stage, with no obvious corrosion damage). The correlation prediction submodule inputs 257-dimensional data (256-dimensional fused features + 0.08 mm corrosion product layer thickness). After preliminary fitting by multiple linear regression and optimization by a BP neural network (hidden layer neurons 256, 128, 64), the output corrosion rate is 0.03 mm / a, leakage risk probability is 15%, and remaining life is 120,000 h (13.7 years).

[0065] (7) Early warning output and data storage: The early warning module calls the DQN dynamically updated early warning threshold (the Level II early warning threshold is a leakage risk probability of 30%). The current leakage risk probability is 15% < 30%, and a Level I early warning (safe state) is output. When the unit load fluctuation causes the hydrogen concentration to rise abnormally to 0.35 mL / L, the dissolved hydrogen mode effectiveness coefficient increases to 0.92, the fused feature vector is updated in real time, the associated model outputs a leakage risk probability of 32%, triggering a Level II early warning (attention state), and notifying staff to investigate via SMS. All data (including load fluctuation curves, mode weight changes, and early warning records) are stored on industrial-grade solid-state drives to support subsequent traceability analysis.

[0066] 3. Implementation Results After 60 days of continuous operation and 120 load fluctuations, the system demonstrated excellent operational stability with a response time of ≤1s and no data loss or delay. Corrosion rate measurement error was ±0.003mm / a, leakage risk probability prediction error was ±1.5%, and remaining life prediction error was ±2400h, further improving monitoring accuracy compared to rated operating conditions. During three instances of abnormally high hydrogen concentrations, the system quickly identified risks and triggered warnings with a response time of ≤3s and no missed alarms. Throughout the entire operating cycle, the false alarm rate was 1.2%, the missed alarm rate was 0%, the warning accuracy was 98.8%, and the load fluctuation adaptability coefficient (the degree of matching between fusion characteristics and changes in operating conditions) was ≥0.97, fully validating the system's dynamic adjustment capability and warning reliability under varying load conditions.

[0067] Example 3: Economizer monitoring in chemical boilers (high corrosion conditions) 1. Implementation conditions Monitoring object: Economizer of a 50MW chemical boiler (material: 15CrMoG steel, design wall thickness: 10mm, service life: 8 years). The high-incidence area of ​​corrosion is the middle section of the pipeline (temperature: 200-250℃, pressure: 10-12MPa, medium: boiler feedwater with high concentration of corrosive gas, CO2 concentration: 5%-8%, H2S concentration: 0.1%-0.3%, which is a high-corrosion condition).

[0068] System Configuration: Sensor Module: The EIS sensor has a working electrode made of 15CrMoG steel, an excitation signal amplitude of 5mV, and a test frequency range of 10mHz-100kHz; the dissolved hydrogen photoacoustic spectroscopy sensor maintains a photoacoustic cell pressure of 0.1MPa, incorporates a gas pretreatment device (containing CO2 adsorbent) to remove CO2 interference, and has a sampling frequency of 1kHz; the ultrasonic thickness sensor is equipped with a high-temperature corrosion-resistant coupling agent (resistant to H2S corrosion) automatic replenishment device, and a probe frequency of 5MHz. The three sensors are embedded in the middle section of the pipe, spaced 60mm apart, and their surfaces are covered with an anti-corrosion coating (PTFE).

[0069] Data acquisition module: adopts a multi-channel synchronous data acquisition card (model NI-6373), with a sampling rate of 1MHz, a resolution of 16 bits, and an acquisition cycle of 1 minute, which improves the data sampling density under highly corrosive working conditions, and the synchronization error is ≤0.5ms.

[0070] Preprocessing module: The EIS signal is processed by dual processing of adaptive Kalman filtering and 4-layer denoising of db4 wavelet to suppress strong electromagnetic interference and signal drift; the dissolved hydrogen photoacoustic signal is decomposed by 6-layer db4 wavelet, with improved Birge-Massart criterion threshold denoising and adaptive gain adjustment, increasing the signal-to-noise ratio to 33dB; the ultrasonic signal is filtered by a median filter window of size 8 to enhance the noise suppression capability caused by the corrosion product layer.

[0071] Feature extraction module: The VMD decomposition has 5 layers, corresponding to time scales of 10ms (microscopic instantaneous corrosion fluctuation), 20s (short-term corrosion rate mutation), 1min (collection cycle features), 5min (medium-term corrosion trend), and 10min (long-term corrosion accumulation). It extracts 40 basic features (8 features per scale), and the CNN network outputs a 128-dimensional feature vector.

[0072] Data fusion module: The input parameters of RBFNN are increased to CO2 concentration (5%-8%) and H2S concentration (0.1%-0.3%), and the effectiveness coefficients of each modality are dynamically calculated; the Transformer model maintains 6 encoding layers, 8 attention heads, and 256 output dimensions.

[0073] The correlation model module includes a genetic algorithm for inverting the thickness of the corrosion product layer with a population size of 80 and 200 iterations. The objective function is optimized to minimize the mean square error of the measured value minus the theoretical value minus the influence coefficient of the corrosion medium. The correlation prediction module uses a BP neural network with the Tanh activation function to enhance nonlinear fitting ability, and has 128, 64, and 32 hidden layer neurons.

[0074] Early warning module: The false alarm rate weight of the DQN reward function is increased to 0.3. The reward function is the early warning accuracy (0.4), false alarm rate (0.3), false alarm rate (0.2), and corrosive medium adaptability (0.1). The threshold update cycle is 72 hours.

[0075] Storage module: 2TB industrial-grade solid-state drive, 5-year storage life, supports real-time plotting and export of corrosion rate change curves.

[0076] 2. Implementation process (1) System deployment and calibration: Before the sensor is installed, the surface of the pipeline is polished to ensure that the probe fits tightly; after the data acquisition module channel is calibrated, the boiler’s 8-year operating history data (including corrosion records under high corrosion conditions and changes in medium composition) is input to complete the pre-training of the correlation model; the anti-corrosion coating on the sensor surface is tested for pressure resistance and corrosion resistance to ensure that it does not fail in a high H2S environment.

[0077] (2) Data acquisition: After the system starts, EIS signals (Rct=3000Ω·cm) are acquired synchronously at a 1-minute cycle. 2 Cdl=30μF / cm 2 The data acquisition module tracks the changes in CO2 and H2S concentrations in real time, and converts the analog signals into digital signals before transmitting them to the preprocessing module. The data includes dissolved hydrogen concentration signals (0.2-0.4 mL / L) and ultrasonic thickness signals (initial measurement value 9.2 mm).

[0078] (3) Signal preprocessing: After double filtering, the EIS signal drift is ≤0.5%; the dissolved hydrogen photoacoustic signal is treated by a gas preprocessing device to remove CO2 interference, and then by wavelet denoising and adaptive gain adjustment, the signal-to-noise ratio is stabilized at 33dB; after median filtering, the measurement noise caused by the corrosion product layer is reduced by 80% of the ultrasonic signal.

[0079] (4) Feature extraction: VMD5 layer decomposition fully captures multi-scale features under high corrosion conditions, especially the short-term corrosion rate mutation features at the 20s scale and the long-term cumulative features at the 10min scale; after CNN extraction of 40 basic features, a 128-dimensional feature vector is output, and the correlation coefficient between the features and the corrosion state is ≥0.96.

[0080] (5) Data fusion: When the H2S concentration rises to 0.3% and the CO2 concentration is 8%, the RBFNN calculates the effectiveness coefficient of the EIS mode as 0.90 (highest sensitivity to electrochemical corrosion), the dissolved hydrogen mode as 0.88 (sensitivity to hydrogen-induced corrosion), and the ultrasonic mode as 0.85 (stability of wall thickness measurement). The Transformer model focuses on fusing the characteristics of the EIS and dissolved hydrogen modes. When the H2S concentration drops to 0.1% and the CO2 concentration is 5%, the effectiveness coefficients are adjusted to EIS=0.80, dissolved hydrogen=0.75, and ultrasonic=0.92, highlighting the monitoring accuracy of the ultrasonic mode for wall thickness changes, and outputting a 256-dimensional fused feature vector.

[0081] (6) Correlation model calculation: The corrosion product layer thickness inversion submodule inputs EIS parameters and ultrasonic measurement value of 9.2mm. The corrosion product layer thickness is calculated to be 0.3mm through the inversion model optimized by genetic algorithm. After correction, the actual corrosion depth of the pipeline is 10-9.2-0.3=0.5mm. The correlation prediction submodule inputs 257-dimensional data and outputs corrosion rate of 0.3mm / a, leakage risk probability of 75%, and remaining life of 17520h (2 years).

[0082] (7) Early warning output and data storage: The early warning module calls the DQN dynamically updated Level III early warning threshold (60%). The current leakage risk probability is 75% > 60%, and outputs a Level III early warning (early warning status). The system reminds the staff to carry out anti-corrosion treatment through three methods: sound and light alarm, SMS notification, and upper computer pop-up window. The system draws the corrosion rate change curve in real time and stores data such as medium composition, modal weight, and inversion results to provide a basis for the formulation of subsequent anti-corrosion schemes.

[0083] 3. Implementation Results After 45 days of continuous operation, the system demonstrated excellent stability under highly corrosive conditions with no sensor failures or data transmission malfunctions. The corrosion rate measurement error was ±0.01 mm / a, the leakage risk probability prediction error was ±3%, and the remaining life prediction error was ±720 hours, representing a 67% reduction in measurement error compared to existing technologies under highly corrosive conditions (±0.03 mm / a). The system successfully identified four instances of increased corrosion risk caused by sudden spikes in H2S concentration, with an early warning response time of ≤2 seconds and no missed alarms. Throughout the entire operating cycle, the false alarm rate was 2.5%, the missed alarm rate was 0%, the early warning accuracy was 97.5%, and the corrosion medium adaptability coefficient was ≥0.95, fully validating the system's anti-interference capability and monitoring accuracy under highly corrosive conditions.

[0084] Comparison Example To verify the inventive advantages of the present invention, three sets of control examples were set up, each corresponding to a typical solution of the prior art. The performance of the control examples was compared with that of Example 1 (rated operating condition) under the same monitoring object and the same operating condition. The test indicators included corrosion rate measurement error, leakage risk probability prediction error, false alarm rate, false alarm rate, and early warning accuracy. The test period was 30 days.

[0085] Comparison with Example 1: Single ultrasonic thickness monitoring system Technical solution: Only an ultrasonic thickness sensor (same model as in Example 1) is used, with a fixed acquisition cycle of 5 minutes. Early warning is achieved through a static threshold (leakage risk probability threshold of 30%), without multimodal fusion, signal enhancement, or corrosion product layer correction.

[0086] Test results: Corrosion rate measurement error ±0.03 mm / a (6 times that of the present invention), leakage risk probability prediction error ±8% (4 times that of the present invention); due to the lack of consideration for the influence of corrosion product layer, ultrasonic measurement values ​​are distorted, false alarm rate 12% (15 times that of the present invention), false alarm rate 5% (0% of the present invention), early warning accuracy 83% (16.2 percentage points lower than the present invention); unable to capture electrochemical corrosion and hydrogen-induced corrosion risks, monitoring is significantly one-sided.

[0087] Compare with Example 2: Dual-modal static fusion monitoring system Technical solution: Integrate EIS sensor and ultrasonic thickness sensor (same model as in Example 1), data fusion adopts weighted average (EIS weight 0.5, ultrasonic weight 0.5), no dynamic weight adjustment, signal preprocessing only adopts conventional filtering (no adaptive gain adjustment), and the warning threshold is fixed.

[0088] Test results: Corrosion rate measurement error ±0.015mm / a (3 times that of the present invention), leakage risk probability prediction error ±4% (2 times that of the present invention); due to the inability of static fusion to adapt to operating condition fluctuations, the fusion accuracy decreases when the load fluctuates, with a false alarm rate of 6% (7.5 times that of the present invention), a false alarm rate of 3% (0% for the present invention), and a warning accuracy of 91% (8.2 percentage points lower than the present invention); there is no dissolved hydrogen mode, so the risk of hydrogen-induced corrosion cannot be identified, and there is no corrosion product layer correction, resulting in limited monitoring accuracy.

[0089] Comparison with Example 3: Three-modal No-Innovation Module Monitoring System Technical solution: Integrating EIS, dissolved hydrogen photoacoustic spectroscopy, and ultrasonic thickness sensor (same model as in Example 1), data fusion adopts attention mechanism Transformer model (without dynamic weight adjustment), without corrosion product layer inversion module, without multi-scale feature extraction, and the early warning threshold is fixed.

[0090] Test results: Corrosion rate measurement error ±0.008 mm / a (1.6 times that of the present invention), leakage risk probability prediction error ±3% (1.5 times that of the present invention); due to the lack of dynamic weight adjustment and corrosion product layer correction, the monitoring accuracy fluctuates when the operating conditions change, with a false alarm rate of 3.5% (4.4 times that of the present invention), a false alarm rate of 2% (0% in the present invention), and an early warning accuracy of 94.5% (4.7 percentage points lower than the present invention); incomplete feature extraction and insufficient input features for the association model lead to lower prediction accuracy than the present invention.

[0091] A comparison of Examples 1-3 with the three sets of control examples shows that the present invention, through adaptive modal weight dynamic adjustment, corrosion product layer thickness inversion, multi-scale feature extraction, signal enhancement algorithm, and dynamic threshold update, solves the problems of single-mode partiality, static fusion not adapting to working conditions, weak signal anti-interference, and inaccurate early warning in existing technologies. It exhibits excellent monitoring accuracy (corrosion rate measurement error ≤ ±0.01 mm / a), early warning reliability (false alarm rate ≤ 2.5%, missed alarm rate 0%), and adaptability to working conditions under rated working conditions, variable load working conditions, and high corrosion working conditions, which is significantly better than existing technologies and has sufficient inventiveness and industrial application value.

[0092] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of protection claimed by the present invention. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An electrochemical impedance-dissolved hydrogen boiler four-tube monitoring system, characterized in that, include: The sensor module includes an electrochemical impedance spectroscopy sensor, a dissolved hydrogen photoacoustic spectroscopy sensor, and an ultrasonic thickness sensor, which respectively collect electrochemical corrosion signals, dissolved hydrogen concentration signals, and pipe thickness signals of the four boiler tubes. The data acquisition module is connected to the sensor module to synchronously acquire the three-modal raw signals and convert them into digital signals; The preprocessing module filters and reduces noise in the digital signal. The dissolved hydrogen photoacoustic signal is processed using an enhancement algorithm that combines wavelet threshold denoising with adaptive gain adjustment. The feature extraction module extracts local features of the three-modal signal through CNN, and extracts short-term fluctuation and long-term trend features by combining multi-scale time series decomposition to obtain multi-dimensional feature vectors; The data fusion module, based on the attention mechanism Transformer model, introduces an adaptive modal weight dynamic adjustment mechanism to dynamically allocate and fuse the three modal feature weights according to real-time operating parameters. The correlation model module, combined with the correction parameters output by the corrosion product layer thickness inversion module, establishes a corrosion-leakage dynamic correlation model. The corrosion product layer thickness is obtained by coupling and inverting the electrochemical impedance spectroscopy signal and the ultrasonic thickness signal. The early warning module uses a reinforcement learning algorithm to build a dynamic update mechanism for early warning thresholds. It updates the thresholds based on equipment operation data and historical records, and outputs early warning information. The storage module stores data from each module and model parameters.

2. The electrochemical impedance-dissolved hydrogen boiler four-tube monitoring system according to claim 1, characterized in that: The adaptive modal weight dynamic adjustment mechanism takes real-time boiler temperature, pressure, and medium composition data as input, calculates the effectiveness coefficient of each mode through a radial basis function neural network, and dynamically adjusts the fusion weight.

3. The electrochemical impedance-dissolved hydrogen boiler four-tube monitoring system according to claim 1, characterized in that: The corrosion product layer thickness inversion module calculates the corrosion product layer thickness based on the charge transfer resistance and double-layer capacitance parameters of electrochemical impedance spectroscopy and ultrasonic thickness measurements, using an inversion model optimized by a genetic algorithm, thereby correcting the actual corrosion depth of the pipeline.

4. The electrochemical impedance-dissolved hydrogen boiler four-tube monitoring system according to claim 1, characterized in that: The multi-scale time series decomposition adopts variational mode decomposition, with 3-5 decomposition layers, corresponding to the micro-instantaneous fluctuations, meso-periodic changes and macro-trend evolution of the corrosion process, respectively.

5. The electrochemical impedance-dissolved hydrogen boiler four-tube monitoring system according to claim 1, characterized in that: In the hybrid model of CNN and attention mechanism Transformer, the CNN uses 3 convolutional layers with a kernel size of 3×3, and the Transformer encoder contains 6 encoding layers, each with 8 attention heads, and the output dimension is 256.

6. The electrochemical impedance-dissolved hydrogen boiler four-tube monitoring system according to claim 1, characterized in that: The reinforcement learning algorithm is a deep Q-network, with the warning accuracy, false alarm rate, and false negative rate as reward functions, and the warning threshold is updated every 72 hours.

7. The electrochemical impedance-dissolved hydrogen boiler four-tube monitoring system according to claim 1, characterized in that: The electrochemical impedance spectroscopy sensor has a test frequency range of 10mHz-100kHz, and the excitation signal is a sinusoidal AC signal with an amplitude of 5mV. It is installed in the area of ​​high corrosion incidence on the inner wall of the pipeline.

8. The electrochemical impedance-dissolved hydrogen boiler four-tube monitoring system according to claim 1, characterized in that: The dissolved hydrogen photoacoustic spectroscopy sensor uses a near-infrared laser light source with a wavelength of 1.57 μm, maintains the photoacoustic cell pressure at 0.1-0.3 MPa, and has a signal sampling frequency of 1 kHz.

9. The electrochemical impedance-dissolved hydrogen boiler four-tube monitoring system according to claim 1, characterized in that: The ultrasonic thickness sensor has a probe frequency of 5MHz, a measurement range of 3-50mm, a resolution of ±0.01mm, and is installed at a 0° angle to the pipe surface.

10. The electrochemical impedance-dissolved hydrogen boiler four-tube monitoring system according to claim 1, characterized in that: The corrosion-leakage dynamic correlation model is optimized by combining multiple linear regression with a BP neural network. The input is a fused feature vector and the thickness of the corrosion product layer, and the output is the corrosion rate, leakage risk probability, and remaining lifetime prediction.

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