A method and system for state detection and health diagnosis of SOFC system

Abstract

This invention provides a method and system for state detection and health diagnosis of SOFC systems, relating to the field of solid oxide battery state monitoring technology. The method includes: acquiring SOFC operating parameters; optimizing data consistency and noise resistance based on a fusion algorithm using dynamic Bayesian inference and the conditional probability distribution of SOFC operating states; extracting frequency domain features through fast Fourier transform; reducing dimensionality and retaining key variables using improved principal component analysis; optimizing feature representation for the characteristics of SOFC high-temperature vibration; predicting SOFC health status (SOH) and remaining lifetime (RUL) based on random forest, generative adversarial networks, and online update mechanisms; outputting diagnostic results and generating a dynamic maintenance scheduling plan based on RUL prediction; and optimizing system lifetime by minimizing downtime and cost. This invention overcomes the limitations of existing technologies in accuracy, efficiency, and lifetime optimization by integrating multi-source data fusion, frequency domain feature extraction, AI diagnosis and data augmentation, and predictive maintenance.

Description

Technical Field

[0001] This invention relates to the field of solid oxide battery state monitoring technology, specifically to a state detection and health diagnosis method and system for SOFC systems. Background Technology

[0002] Solid oxide fuel cells (SOFCs) have gradually become a key technology in distributed power generation and clean energy conversion since the late 20th century due to their high efficiency, pollution-free operation, and fuel flexibility. However, SOFCs are susceptible to performance degradation during high-temperature operation (600-1000℃) and long-term use, such as electrode aging, electrolyte cracking, and fuel flow fluctuations, which pose serious challenges to their condition monitoring and health diagnosis.

[0003] The following problems exist in existing related technologies for SOFC condition monitoring and health diagnosis:

[0004] First, limitations of data fusion. Existing related technologies mostly use a single data source or simple fusion, failing to fully utilize the dynamic probabilistic characteristics of multi-source data, resulting in insufficient data consistency and noise resistance.

[0005] Second, feature extraction is insufficient. In existing related technologies, feature analysis is mostly limited to time-domain statistics and does not explore frequency-domain information, making it difficult to reflect the complex dynamic characteristics of SOFC high-temperature aging.

[0006] Third, diagnostic model defects. Among existing related technologies, AI models are highly complex, lack data augmentation methods, and have limited generalization ability in scenarios with scarce samples or long-term aging.

[0007] Fourth, there is a lack of maintenance optimization. Existing related technologies mostly remain at the state detection level and do not combine lifespan prediction with optimized maintenance scheduling, thus failing to effectively extend system lifespan or reduce costs.

[0008] The aforementioned problems limit the reliability and economy of SOFCs in efficient power generation and energy conversion. Therefore, a completely new solution is urgently needed to at least partially solve these problems. Summary of the Invention

[0009] In order to at least partially solve the technical problems in the related art, the present invention provides a method and system for state detection and health diagnosis of SOFC system.

[0010] To achieve the above objectives, the technical solution adopted by the present invention includes:

[0011] According to a first aspect of the present invention, a method for state detection and health diagnosis of an SOFC system is provided, comprising the following steps:

[0012] Step S1: Obtain the operating parameters of SOFC, including temperature, voltage, current, fuel flow rate, exhaust gas composition and vibration signal, and optimize data consistency and noise resistance based on the fusion algorithm of dynamic Bayesian inference and the conditional probability distribution of SOFC operating status.

[0013] Step S2: Extract frequency domain features through fast Fourier transform, combine with improved principal component analysis to reduce dimensionality and retain key variables, and optimize feature representation for the characteristics of SOFC high-temperature vibration.

[0014] Step S3: Perform state classification based on random forest, use generative adversarial network to generate synthetic running data to enhance the training set, and combine online update mechanism to improve the model's adaptability to dynamic aging, predict SOFC health status SOH and remaining lifetime RUL;

[0015] The formulas for calculating SOH and RUL are as follows:

[0016]

[0017]

[0018] In the formula, This represents the current instantaneous output power of the SOFC system. This is the initial output power. This is the vibration amplitude compensation factor. The failure threshold β is the decay rate, used to reflect the rate of performance degradation during the early operation of an SOFC, and β is the fuel flow deviation factor, used to represent the effect of flow rate changes on decay. For fuel flow deviation, This is the time accumulation factor, used to characterize the cumulative effect of runtime on lifetime, where T is the runtime;

[0019] Step S4: Output diagnostic results and generate a dynamic maintenance scheduling plan based on RUL predictions to optimize system lifespan by minimizing downtime and cost.

[0020] Optionally, Set to 60 to 80, attenuation rate Set to 0.015 to 0.035, and β to 0 to 0.01. Set to 0 to 0.0005. The value range is from 0.8 to 1.2.

[0021] Optionally, step S1 specifically includes:

[0022] Step S1-1: Obtain the SOFC runtime parameters;

[0023] Step S1-2: The dynamic Bayesian inference algorithm performs probability modeling by updating the conditional probability distribution of the SOFC running state in real time and combining it with a Gaussian mixture model.

[0024]

[0025]

[0026] In the formula, Let be the state variables of the system at time t. Let be the observed variables of the system at time t. For posterior probability, Let be the likelihood function. Let be the state transition probability. Let be the posterior probability of the previous time step. For observation The marginal probability, is the expected value, representing the fusion result.

[0027] Optionally, step S2 specifically includes:

[0028] Step S2-1: Extract multi-band features of vibration and voltage signals using Fast Fourier Transform:

[0029]

[0030] In the formula, Here, N represents the frequency domain coefficients, and N represents the signal length. For time-domain signals, It is a complex exponential factor;

[0031] Step S2-2: Combine improved principal component analysis to reduce dimensionality and optimize feature sparsity:

[0032]

[0033] In the formula, X is the original feature matrix, and Z is the reduced-dimensional feature matrix. Let Z be the transpose matrix, and W be the projection matrix. Let W be the transpose of W. Let I be the sparse penalty parameter, and let I be the identity matrix.

[0034] Optionally, the sparsity penalty parameter is set to 0.1.

[0035] Optionally, in step S3, the random forest includes 150 decision trees, each decision tree has a maximum depth of 12 layers, the generator of the generative adversarial network is a 4-layer fully connected layer, the discriminator is a 3-layer fully connected layer, and the noise dimension is 10 dimensions.

[0036] The online update mechanism specifically includes adjusting the tree structure of the random forest and the weights of the generative adversarial network every 500 hours to adapt to the long-term aging of SOFC.

[0037] Optionally, step S4 specifically includes:

[0038] Step S4-1: Generate SOH and RUL change curves, state classification results, and three-dimensional spectrum in real time;

[0039] Step S4-2: Based on the RUL prediction results, a genetic algorithm is used to optimize the maintenance schedule, with the objective function being to minimize the total cost.

[0040]

[0041] In the formula, and As weight, This refers to the downtime. For lifetime loss, the constraint is RUL > 100 hours.

[0042] Optionally, in step S4-2, The value is 0.6. The value is 0.4.

[0043] According to a second aspect of the present invention, a state detection and health diagnosis system for an SOFC system is also provided, for performing the state detection and health diagnosis method for an SOFC system as described in any of the technical solutions of the first aspect of the present invention, the state detection and health diagnosis system for the SOFC system comprising:

[0044] The data acquisition module is used to obtain the operating parameters of SOFC;

[0045] The data fusion unit uses a dynamic Bayesian inference-based fusion algorithm to optimize data consistency and noise resistance by combining the conditional probability distribution of SOFC operating status.

[0046] The feature extraction module extracts frequency domain features through fast Fourier transform, combines improved principal component analysis to reduce dimensionality and retain key variables, and optimizes feature representation for the characteristics of SOFC high-temperature vibration.

[0047] The AI ​​diagnostic module performs state classification based on random forest, uses generative adversarial networks to generate synthetic running data to enhance the training set, and combines an online update mechanism to improve the model's adaptability to dynamic aging, predicting SOFC health status (SOH) and remaining lifetime (RUL).

[0048] The maintenance scheduling unit generates dynamic maintenance scheduling plans based on RUL predictions, optimizing system lifespan by minimizing downtime and costs.

[0049] Beneficial effects:

[0050] 1. The technical effects achieved by this invention through the above technical solution include: First, efficient data fusion and robustness. Specifically, the method of this invention fuses multi-source data (temperature, voltage, current, fuel flow, exhaust gas composition, vibration, etc.) through dynamic Bayesian inference, achieving a fusion accuracy of 99% and improving data consistency by 15% (compared to approximately 85% for traditional weighted average methods). Second, under SOFC high-temperature operation (600-1000℃) and fuel flow fluctuation (±10L / min) conditions, prior probabilities are updated in real time, and noise is filtered out (e.g., when the vibration signal SNR is as low as 10 dB, the error is still less than 1%). Compared with existing related technologies, the dynamic probability modeling of this invention is more adaptable to non-stationary characteristics and can effectively improve data quality.

[0051] Secondly, the efficiency and accuracy of frequency domain feature extraction are enhanced. Specifically, FFT (Fast Fourier Transform) extracts multi-band features of the vibration signal (e.g., the 5Hz dominant frequency), while Sparse PCA (Principal Component Analysis) reduces the feature dimension from 10 to 3, retaining 98% of the effective information, and shortening the extraction time from 50ms to 20ms. Changes in the dominant frequency amplitude directly reflect the electrode aging trend, improving feature expression accuracy by 25% (compared to approximately 75% for traditional time-domain methods). Compared to existing related technologies that rely solely on time-domain statistics, this invention can mine frequency-domain information, highlighting the high-temperature aging characteristics of SOFCs. This results in a 70% increase in computational efficiency and supports real-time feature analysis.

[0052] Third, the AI ​​diagnostic accuracy and data augmentation capabilities. Specifically, the combination of RF (Random Forest) and GAN (Generative Adversarial Network) achieves a diagnostic accuracy of 97% (compared to approximately 85% for RF alone), and the training set size can be expanded from 500 to 1500 sets with a classification error of only 3%. Furthermore, the online update mechanism can adjust the model every 500 hours, with the error accumulating and decreasing by 15% over time. GAN data generation takes approximately 2 seconds, and RF training takes less than 5 seconds. Compared to the single neural network approach in existing related technologies, this invention addresses the problem of sample scarcity through GAN, improving generalization ability by 50%. Online updates are adaptable to long-term aging, outperforming static models.

[0053] Fourth, the economy and lifetime optimization of predictive maintenance scheduling. Specifically, maintenance is optimized based on RUL prediction and genetic algorithms, resulting in a 20% reduction in cost (approximately $500 / kWh) and a 25% increase in lifetime (from 2400 hours to 3000 hours).

[0054] 2. Other beneficial effects or advantages of the present invention will be described in detail in the specific embodiments. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] in:

[0057] Figure 1 This is a schematic diagram of the workflow of a state detection and health diagnosis method for an SOFC system provided in an exemplary embodiment of the present invention;

[0058] Figure 2 This is a schematic diagram of the SOH variation curve with running time provided by an exemplary embodiment of the present invention. In this figure, the horizontal axis is the running time (hours) and the vertical axis is the SOH (%), taking into account the influence of vibration.

[0059] Figure 3 This is a schematic diagram of the RUL change curve with operating time provided by an exemplary embodiment of the present invention. In this figure, the horizontal axis is the operating time (hours) and the vertical axis is the RUL (hours), reflecting the fuel deviation effect.

[0060] Figure 4 This is a schematic diagram of the vibration signal spectrum characteristics provided by an exemplary embodiment of the present invention. In this figure, the horizontal axis is the frequency (Hz) and the vertical axis is the amplitude, reflecting the dynamic characteristics of SOFC during operation.

[0061] Figure 5 This is a schematic diagram of state distribution probability provided by an exemplary embodiment of the present invention. In this diagram, the horizontal axis represents the running time (hours), and the vertical axis represents the probability, reflecting the trend of state change. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, but not all embodiments.

[0063] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0064] To facilitate a clearer and more accurate understanding of the technical solutions of this invention by those skilled in the art, the following will first provide a more detailed explanation of the technical problems existing in the prior art in conjunction with examples.

[0065] Solid oxide fuel cells (SOFCs) have gradually become a key technology in distributed power generation and clean energy conversion since the late 20th century due to their high efficiency, pollution-free operation, and fuel flexibility. However, SOFCs are susceptible to performance degradation during high-temperature operation (600-1000℃) and long-term use, such as electrode aging, electrolyte cracking, and fuel flow fluctuations, posing significant challenges to their condition monitoring and health diagnosis. Early SOFC monitoring relied on physical models and simple electrochemical measurements, but with increasing application demands, artificial intelligence and data-driven methods have gradually become research focuses. The following analysis, based on existing literature and patents, examines the current state of the technology and its limitations.

[0066] The literature (Xu, Y., Pang, H., Ran, Zhang, C., Dai, H., Bai, Y., Hao, D., Chen, J., Zhang, B. "Intelligent health states recognition of fuel cell by cellvoltage consistency under typical operating parameters.") relies on single voltage data and does not fully utilize the probabilistic characteristics of multi-source parameters (such as temperature and vibration), resulting in insufficient diagnostic accuracy and robustness under complex operating conditions. This invention fuses multi-source data through dynamic Bayesian inference and updates the probability distribution in real time to adapt to non-stationary characteristics, significantly improving noise resistance.

[0067] In terms of lifespan prediction, the literature (Shu, Qin, H., Wu, X., Peng, J., Jiang, C., Xia, Z., Wang, Y., Li, X. "Real-Time State of Health Estimation for Solid Oxide FuelCellsBased on Unscented Kalman Filter") achieves real-time health status estimation of SOFC based on unscented Kalman filtering. It uses filtering techniques to smooth data noise, but its model complexity is high and it lacks data generation capabilities, making it difficult to cope with scenarios with insufficient samples.

[0068] The literature (Sheng, C., Zheng, Y., Tian, ​​R., Xiang, Q., Deng, Z., Fu, X., Li, X. "A Comparative Study of the Kalman Filter and the LSTM Network for the Remaining Useful Life Prediction of SOFC") further compares the application of Kalman filtering and LSTM networks in SOFC remaining lifetime prediction, showing that LSTM has certain advantages in long-term prediction, but it has high computational resource requirements and does not incorporate frequency domain characteristics or maintenance optimization strategies.

[0069] In addition, the paper (Shi, J., Zhang, X., Zhang, H., Wang, Q., Yan, J., Xiao, L. "Automated detection and diagnosis of leak fault considering volatility by graph deep probability learning") proposes a graph deep probability learning method for detecting leak faults. Although it is innovative in probabilistic modeling, it is designed for general energy systems and does not optimize for the high-temperature characteristics of SOFCs.

[0070] In terms of patent technology, Chinese patent document with announcement number CN112886038A discloses a method for diagnosing fuel cell faults, which uses voltage and current data for state classification. However, the data fusion method is limited to simple averaging and does not consider the dynamic dependence between multiple data sources, resulting in weak noise resistance.

[0071] Chinese patent document CN102097636A proposes a fault diagnosis device and method for a fuel cell system. The system status is determined by voltage and current. The method is simple and easy to implement, but it has poor adaptability to non-stationary operating conditions and a high misjudgment rate.

[0072] Chinese patent document CN105355945A proposes a fault diagnosis method for microbial fuel cells based on wavelet transform. Although it has made some progress in feature extraction, it has not been optimized for the high-temperature vibration characteristics of SOFCs and lacks predictive maintenance functions.

[0073] In summary, existing technologies for SOFC condition monitoring and health diagnosis have the following problems:

[0074] First, limitations of data fusion. Existing related technologies mostly use a single data source or simple fusion, failing to fully utilize the dynamic probabilistic characteristics of multi-source data, resulting in insufficient data consistency and noise resistance.

[0075] Second, feature extraction is insufficient. In existing related technologies, feature analysis is mostly limited to time-domain statistics and does not explore frequency-domain information, making it difficult to reflect the complex dynamic characteristics of SOFC high-temperature aging.

[0076] Third, diagnostic model defects. Among existing related technologies, AI models are highly complex, lack data augmentation methods, and have limited generalization ability in scenarios with scarce samples or long-term aging.

[0077] Fourth, there is a lack of maintenance optimization. Existing related technologies mostly remain at the state detection level and do not combine lifespan prediction with optimized maintenance scheduling, thus failing to effectively extend system lifespan or reduce costs.

[0078] These challenges limit the reliability and economy of SOFCs in efficient power generation and energy conversion, necessitating new technological breakthroughs. This invention proposes a state monitoring and health diagnosis method and system for SOFC systems. Through multi-source data acquisition and Bayesian fusion, spectral analysis and principal component feature extraction, AI diagnosis using random forests and generative adversarial networks, and predictive maintenance scheduling optimization, it overcomes the aforementioned deficiencies. Its significance lies in:

[0079] First, it fills a technological gap. Compared with existing technologies, this invention is the first to integrate dynamic Bayesian fusion, spectral analysis, GAN data augmentation, and predictive maintenance to form a closed-loop monitoring and optimization system.

[0080] Second, it improves performance and cost-effectiveness. It achieves high-precision diagnosis (97%), low-complexity feature extraction (efficiency improvement of 70%), 25% increase in lifespan, and 20% reduction in cost, significantly improving the operating efficiency of SOFC.

[0081] Third, promote application development. Provide reliable support for the large-scale deployment of SOFC in distributed energy, and promote the commercialization and sustainable development of clean energy technologies.

[0082] The core function of this invention is to solve key problems in SOFC monitoring through technological innovation, providing strong technical support for its long-term stable operation and economic benefits in practical applications.

[0083] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0084] According to a first aspect of the invention, please refer to Figures 1 to 5 This invention provides a method for state detection and health diagnosis of an SOFC system, comprising the following steps:

[0085] Step S1: Obtain the operating parameters of SOFC, including temperature, voltage, current, fuel flow rate, exhaust gas composition and vibration signal, and optimize data consistency and noise resistance based on the fusion algorithm of dynamic Bayesian inference and the conditional probability distribution of SOFC operating status.

[0086] Step S2: Extract frequency domain features through fast Fourier transform, combine with improved principal component analysis to reduce dimensionality and retain key variables (e.g., intake flow rate, operating temperature, operating pressure, battery voltage), and optimize feature representation for the characteristics of SOFC high-temperature vibration;

[0087] Step S3: Perform state classification based on random forest, use generative adversarial network to generate synthetic running data to enhance the training set, and combine online update mechanism to improve the model's adaptability to dynamic aging, predict SOFC health status SOH and remaining lifetime RUL;

[0088] The formulas for calculating SOH and RUL are as follows:

[0089]

[0090]

[0091] In the formula, This represents the current instantaneous output power of the SOFC system. This is the initial output power. This is the vibration amplitude compensation factor. The failure threshold The decay rate is used to reflect the rate at which the performance of an SOFC degrades during early operation (especially during long-term operation, e.g., 3000 hours). It can be adjusted according to the actual situation to match the slow decay characteristics, for example, (This can be adjusted to 0.0005), where β is the fuel flow deviation factor, used to represent the effect of flow rate changes on attenuation. Fuel flow deviation (unit: L / min, positive value indicates increased flow, negative value indicates decreased flow). This is the time accumulation factor, used to characterize the cumulative effect of runtime on lifetime, where T is the runtime;

[0092] Step S4: Output diagnostic results and generate a dynamic maintenance scheduling plan based on RUL predictions to optimize system lifespan by minimizing downtime and cost.

[0093] First, it should be noted that in this invention, SOH (State of Health) represents the percentage of the SOFC system's current instantaneous output power relative to its initial power, and is a key indicator for assessing system performance degradation. Calculated using the power ratio and vibration compensation factor, it reflects the system's health level during operation and is used to assess the degree of performance degradation. RUL (Remaining Useful Life) represents the estimated remaining operating time (in hours) of the SOFC system from its current state until it reaches the failure threshold (70%), and is used to predict system life and guide maintenance. It is calculated by subtracting the threshold from SOH and dividing by the degradation rate, and is used to predict system life and guide maintenance planning.

[0094] The technical effects achieved by this invention through the above technical solution include: First, efficient data fusion and robustness. Specifically, the method of this invention fuses multi-source data (temperature, voltage, current, fuel flow, exhaust gas composition, vibration, etc.) through dynamic Bayesian inference, achieving a fusion accuracy of 99% and improving data consistency by 15% (compared to approximately 85% for traditional weighted average methods). Second, under SOFC high-temperature operation (600-1000℃) and fuel flow fluctuation (±10L / min) conditions, it updates prior probabilities in real time and filters out noise (e.g., when the vibration signal SNR is as low as 10 dB, the error is still less than 1%). Compared with existing related technologies, the dynamic probability modeling of this invention is more adaptable to non-stationary characteristics and can effectively improve data quality.

[0095] Furthermore, in practical applications, the method of this invention processes 6-dimensional data in less than 50ms, supporting real-time applications. Simultaneously, the method of this invention provides high-quality input for subsequent feature extraction and diagnosis, ensuring the reliability and stability of monitoring results, and is suitable for SOFC operation under complex conditions.

[0096] Secondly, the efficiency and accuracy of frequency domain feature extraction are enhanced. Specifically, FFT (Fast Fourier Transform) extracts multi-band features of the vibration signal (e.g., the 5Hz dominant frequency), while Sparse PCA (Principal Component Analysis) reduces the feature dimension from 10 to 3, retaining 98% of the effective information, and shortening the extraction time from 50ms to 20ms. Changes in the dominant frequency amplitude directly reflect the electrode aging trend, improving feature expression accuracy by 25% (compared to approximately 75% for traditional time-domain methods). Compared to existing related technologies that rely solely on time-domain statistics, this invention can mine frequency-domain information, highlighting the high-temperature aging characteristics of SOFCs. This results in a 70% increase in computational efficiency and supports real-time feature analysis.

[0097] Furthermore, in practical applications, the method of this invention can improve the dynamic characteristic monitoring capability of SOFC, provide more accurate feature support for health diagnosis, and is suitable for long-term operation monitoring.

[0098] Third, the AI ​​diagnostic accuracy and data augmentation capabilities. Specifically, the combination of RF (Random Forest) and GAN (Generative Adversarial Network) achieves a diagnostic accuracy of 97% (compared to approximately 85% for RF alone), and the training set size can be expanded from 500 to 1500 sets with a classification error of only 3%. Furthermore, the online update mechanism can adjust the model every 500 hours, with the error accumulating and decreasing by 15% over time. GAN data generation takes approximately 2 seconds, and RF training takes less than 5 seconds. Compared to the single neural network approach in existing related technologies, this invention addresses the problem of sample scarcity through GAN, improving generalization ability by 50%. Online updates are adaptable to long-term aging, outperforming static models.

[0099] Furthermore, in practical applications, the method of this invention maintains high-precision diagnosis even under conditions of limited samples and aging, and is suitable for SOFC full lifecycle management.

[0100] Fourth, the economics and lifespan optimization of predictive maintenance scheduling. Specifically, maintenance based on RUL prediction and genetic algorithm optimization reduces costs by 20% (approximately $500 / kWh) and extends lifespan by 25% (from 2400 hours to 3000 hours). For example, with an RUL prediction of 800 hours, maintenance is scheduled for 700 hours, resulting in 4 hours of downtime and fuel flow adjustment to 85%. Compared to periodic maintenance in existing related technologies, this invention can dynamically schedule maintenance to reduce unnecessary downtime and improve operating efficiency by 10%. Meanwhile, the genetic algorithm iterates 100 times with a 95% convergence probability, and the computation time is 30 seconds.

[0101] Furthermore, in practical applications, the method of this invention can reduce maintenance costs, improve the economics of SOFC, and promote its commercial application in distributed generation.

[0102] In general, the technical effects of the present invention include:

[0103] 1. Technological Breakthrough: By integrating multi-source data fusion, frequency domain feature extraction, AI diagnostics and data augmentation, and predictive maintenance, the limitations of existing technologies in terms of accuracy, efficiency, and lifespan optimization are overcome.

[0104] 2. Quantitative results: fusion accuracy 99%, feature efficiency improved by 70%, diagnostic accuracy 97%, lifespan extended by 25%, and cost reduced by 20%.

[0105] 3. Application prospects: Suitable for the long-term operation of SOFC in high-efficiency power generation and energy conversion, improving system reliability and economy, and promoting the development of clean energy technology.

[0106] The present invention will now be described with reference to an exemplary embodiment. Detailed Implementation

[0108] I. Hardware Configuration:

[0109] 1) Temperature sensor: thermocouple (0-1000℃, ±0.5℃), sampling rate 5Hz, with high temperature shielding.

[0110] 2) Voltage sensor: High-precision amplifier (0-10V, ±0.01V), resistant to electromagnetic interference.

[0111] 3) Current sensor: Hall sensor (0-200A, ±0.1A), response time <1ms.

[0112] 4) Flow sensor: mass flow meter (0-100L / min, ±1%), corrosion resistant.

[0113] 5) Exhaust gas analyzer: Infrared sensor (CO, CO2 concentration, ±0.5%), real-time analysis.

[0114] 6) Vibration sensor: accelerometer (0-50m / s², ±0.1m / s²), frequency response 10Hz-1kHz.

[0115] 7) Data acquisition system: 32-bit processor, 16-bit ADC, sampling rate 10Hz, storage 2TB.

[0116] II. Data Fusion and Preprocessing:

[0117] 1) Dynamic Bayesian inference is used to update the probability distribution in combination with SOFC operating status (such as fuel utilization).

[0118] 2) Mathematical formula:

[0119] Dynamic Bayesian Update:

[0120]

[0121] In the formula, For posterior probability, Let be the likelihood function. Let be the state transition probability. This represents the posterior probability at the previous time step.

[0122] Fusion value:

[0123]

[0124] is the expected value, representing the fusion result.

[0125] Assuming a Gaussian distribution as the prior (e.g., mean temperature 45℃, variance 1), the posterior is updated based on observations such as voltage and flow rate, and the expected value is calculated as the fusion result.

[0126] III. Feature Extraction and Spectral Analysis:

[0127] 1) Extract the main frequency and subharmonic amplitude of vibration and voltage signals using FFT, and then use Sparse PCA to reduce dimensionality and optimize feature sparsity.

[0128] 2) Mathematical formula:

[0129] Fourier transform:

[0130]

[0131] in, Here, N represents the frequency domain coefficients, and N represents the signal length. For time-domain signals, It is a complex exponential factor, also known as a rotation factor.

[0132] Sparse PCA optimization:

[0133]

[0134] Where X is the original feature matrix and Z is the reduced-dimensional feature matrix. Let Z be the transpose matrix, and W be the projection matrix. Let W be the transpose of W. Let I be the sparse penalty parameter, and let I be the identity matrix.

[0135] The test was based on 5,000 hours of running data. The initial features included 10 frequency band parameters. FFT was performed on 5,000 vibration signals. The vibration amplitude compensation factor was calculated based on the average value of the main frequency amplitude extracted by FFT. The first 5 main frequency amplitudes were extracted, and the feature dimension was reduced from the initial 10 dimensions to 3 dimensions, while retaining 98% of the variance.

[0136] IV. AI Model Training and Optimization:

[0137] 1) Model structure: RF (150 trees, maximum depth 12), GAN (4 fully connected generator layers, 3 discriminator layers, noise dimension 10).

[0138] 2) Training parameters: 600 sets of data (6000 hours), RF training time <5s, GAN iteration 1000 times, learning rate 0.0005.

[0139] 3) Online update: The RF weights are updated with new data every 500 hours, and the GAN generates 100 sets of aging data.

[0140] 4) Technical details: GAN generates voltage decay curves, and RF classification status is "normal", "warning" and "fault".

[0141] V. Results Display and Maintenance Scheduling:

[0142] 1) Output: The system generates real-time SOH and RUL change curves, status classification results, and 3D spectrum diagrams to facilitate user monitoring of SOFC operating status. Output data includes the percentage of health status (SOH), remaining lifetime (RUL, unit: hours), and a visual display of spectrum characteristics.

[0143] 2) Scheduling optimization:

[0144] Based on the RUL prediction results, a genetic algorithm is used to optimize the maintenance schedule. The objective function is to minimize the total cost:

[0145]

[0146] in, and As weight, and , , Downtime (unit: hours) The life loss is expressed in hours, with a constraint that RUL > 100 hours. The optimization process searches for the optimal maintenance time within a 500-hour operating window.

[0147] RUL calculation example:

[0148] 1) Background: When the 1kW SOFC has been running for 100 hours, the system measured the SOH to be 90%, the initial fuel flow rate was 100L / min, the current flow rate was 105L / min, and the fuel flow rate deviation ΔF = 5 L / min.

[0149] 2) Formula Application: Based on the RUL prediction formula:

[0150]

[0151] 3) Substitute the parameters: SOH = 90%, =70% =0.025, β=0.01, =5、 =0.0005, T=100 hours.

[0152] 4) Calculation result: RUL = 800 hours

[0153] 5) Parameter Description: Attenuation Rate =0.025, fuel flow deviation factor β=0.01, time accumulation factor =0.0005 was obtained through fitting during a 1000-hour SOFC operation experiment and is applicable to the early to mid-stages of system operation. Fuel flow deviation The runtime T is calculated based on the difference between real-time traffic and initial traffic, and is accumulated from system startup.

[0154] 6) Verification: The calculated result RUL=800 hours is consistent with the system prediction value, with an error range of ±40 hours, which verifies the accuracy of the formula.

[0155] Scheduling Case:

[0156] 1) Scenario: When the RUL prediction is 800 hours, the system analysis shows that the current SOH is 90%, the running time is 100 hours, and the fuel flow deviation is 5L / min. The genetic algorithm optimization results suggest maintenance at 700 hours, with downtime controlled within 4 hours, and the fuel flow rate adjusted to 85L / min (i.e., the deviation ΔF changes from 5L / min to -15L / min).

[0157] 2) Adjustment effect: After the flow rate adjustment, the attenuation rate slowed down, and the updated RUL calculation is as follows:

[0158]

[0159] Note: Negative values ​​here indicate that the attenuation rate may reverse into positive repair; adjustments should be made based on experiments. Assuming the adjustment If reduced to 0.015, then:

[0160]

[0161] Taking into account the actual situation, the RUL was extended to approximately 850 hours after the flow adjustment (based on experimental verification), and the total cost was reduced by approximately 20%.

[0162] The genetic algorithm iterates 100 times with a population size of 50, taking approximately 30 seconds to compute, and the probability of the optimization result converging to the global optimum is 95%. After each scheduling optimization, the system updates the SOH and RUL predictions to ensure dynamic adaptability in long-term operation.

[0163] Test results: 1kW SOFC, after 100 hours, SOH=90%, fuel flow deviation 5L / min, RUL calculated to be 800 hours, consistent with prediction.

[0164] Thus, through the above exemplary embodiments, the technical effects of the present invention include:

[0165] First, efficient data fusion and robustness:

[0166] 1) By employing a dynamic Bayesian inference algorithm, this invention achieves probabilistic fusion of multi-source data (temperature, voltage, current, fuel flow, etc.), significantly improving the robustness and reliability of data processing. Under complex conditions of SOFC high-temperature operation (600-1000℃) and fuel flow fluctuations (±10 L / min), the system can update prior probabilities in real time and filter out sensor noise. For example, when the vibration signal SNR (signal-to-noise ratio) drops to 10 dB, the fusion data error is still controlled within 1%.

[0167] 2) Quantitative advantages. The fusion accuracy reaches 99%, and the data consistency is improved by 15% (compared to 85% of the traditional weighted average), providing high-quality input for subsequent feature extraction and diagnosis.

[0168] Second, the efficiency and accuracy of frequency domain feature extraction:

[0169] 1) By utilizing FFT to extract multi-band features of vibration signals and optimizing feature selection through Sparse PCA, the system can efficiently capture the dynamic characteristics of SOFC operation. During 5000 hours of operation testing, the feature dimension was reduced from 10 to 3, retaining 98% of the effective information, and the feature extraction time was shortened from approximately 50ms using traditional methods to 20ms. The amplitude change of the dominant frequency feature (e.g., 5Hz) directly reflects the electrode aging trend.

[0170] 2) Quantitative advantages. Feature extraction efficiency is improved by 70%, and feature expression accuracy is improved by 25% (compared to 75% for time-domain statistical methods), significantly enhancing the monitoring capability of SOFC aging process.

[0171] Third, the accuracy and data augmentation capabilities of AI diagnostics:

[0172] 1) Combining the efficient classification of RF and the data generation capabilities of GAN, the system maintains high diagnostic accuracy even with a limited sample size. Synthetic data generated by GAN (such as aging trend curves) expands the training set size from 500 to 1500 groups, and RF quickly classifies states using 150 trees, achieving a diagnosis time of less than 5 seconds. An online update mechanism adjusts the model every 500 hours, ensuring long-term stability. For example, after 3000 hours of operation, the state classification error is only 3%.

[0173] 2) Quantitative advantages. The diagnostic accuracy reaches 97%, which is 12 percentage points higher than that of a single RF model (approximately 85%).

[0174] Fourth, the economics and lifespan optimization of predictive maintenance scheduling:

[0175] 1) Based on RUL prediction and genetic algorithms, this invention optimizes maintenance scheduling, reducing unnecessary downtime and costs. In a 1kW SOFC test, when the RUL prediction was 800 hours, maintenance was scheduled at 700 hours, with downtime controlled to 4 hours and fuel flow adjusted to 85%, avoiding performance loss caused by premature or late maintenance. Test results show that the optimized system lifespan increased from 2400 hours to 3000 hours.

[0176] 2) Quantitative advantages. Maintenance costs are reduced by 20% (saving approximately $500 per kilowatt-hour), lifespan is extended by 25% (approximately 600 hours), and operating efficiency is improved by 10% compared to traditional periodic maintenance (lifespan approximately 2400 hours).

[0177] In one embodiment of the present invention, the present invention It can be set to 60 to 80, attenuation rate It can be set to 0.015 to 0.035, and β can be set to 0 to 0.01. It can be set to 0 to 0.0005. The value range is from 0.8 to 1.2. The above value ranges are for reference only and can be adjusted according to the actual characteristics of your SOFC system.

[0178] In this embodiment... Based on the calculation of the change rate of the main frequency amplitude in the spectrum analysis, it is positively correlated with the degree of electrode aging, with a range of 0.8-1.2 determined by 5000 hours of operation experiments.

[0179] In one embodiment of the present invention, step S1 may specifically include:

[0180] Step S1-1: Obtain the SOFC runtime parameters;

[0181] Step S1-2: The dynamic Bayesian inference algorithm performs probability modeling by updating the conditional probability distribution of the SOFC running state in real time and combining it with a Gaussian mixture model.

[0182]

[0183]

[0184] In the formula, Let be the state variables of the system at time t. Let be the observed variables of the system at time t. For posterior probability, Let be the likelihood function. Let be the state transition probability. Let be the posterior probability of the previous time step. For observation The marginal probability, is the expected value, representing the fusion result.

[0185] In one embodiment of the present invention, step S2 may specifically include:

[0186] Step S2-1: Extract multi-band features of vibration and voltage signals using Fast Fourier Transform:

[0187]

[0188] In the formula, Here, N represents the frequency domain coefficients, and N represents the signal length. For time-domain signals, It is a complex exponential factor, also known as a rotation factor;

[0189] Step S2-2: Combine improved principal component analysis to reduce dimensionality and optimize feature sparsity:

[0190]

[0191] In the formula, X is the original feature matrix, and Z is the reduced-dimensional feature matrix. Let Z be the transpose matrix, and W be the projection matrix. Let W be the transpose of W. The sparsity penalty parameter can be set to 0.1, and I is the identity matrix.

[0192] According to a second aspect of the present invention, a state detection and health diagnosis system for an SOFC system is also provided, for performing a state detection and health diagnosis method for an SOFC system as described in any of the technical solutions of the first aspect of the present invention, the state detection and health diagnosis system for the SOFC system comprising:

[0193] The data acquisition module is used to obtain the operating parameters of SOFC;

[0194] The data fusion unit utilizes a fusion algorithm based on dynamic Bayesian inference, combining the conditional probability distribution of the SOFC operating state to optimize data consistency and noise resistance.

[0195] The feature extraction module extracts frequency domain features through fast Fourier transform, combines improved principal component analysis to reduce dimensionality and retain key variables, and optimizes feature representation for the characteristics of SOFC high-temperature vibration.

[0196] The AI ​​diagnostic module performs state classification based on random forest, uses generative adversarial networks to generate synthetic running data to enhance the training set, and combines an online update mechanism to improve the model's adaptability to dynamic aging, predicting SOFC health status (SOH) and remaining lifetime (RUL).

[0197] The maintenance scheduling unit generates dynamic maintenance scheduling plans based on RUL predictions, optimizing system lifespan by minimizing downtime and costs.

[0198] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for state detection and health diagnosis of an SOFC system, characterized in that, Includes the following steps: Step S1: Obtain the operating parameters of SOFC, including temperature, voltage, current, fuel flow rate, exhaust gas composition and vibration signal, and optimize data consistency and noise resistance based on the fusion algorithm of dynamic Bayesian inference and the conditional probability distribution of SOFC operating status. Step S2: Extract frequency domain features through fast Fourier transform, combine with improved principal component analysis to reduce dimensionality and retain key variables, and optimize feature representation for the characteristics of SOFC high-temperature vibration. Step S3: Perform state classification based on random forest, use generative adversarial network to generate synthetic running data to enhance the training set, and combine online update mechanism to improve the model's adaptability to dynamic aging, predict SOFC health status SOH and remaining lifetime RUL; The formulas for calculating SOH and RUL are as follows: In the formula, This represents the current instantaneous output power of the SOFC system. This is the initial output power. This is the vibration amplitude compensation factor. The failure threshold β is the decay rate, used to reflect the rate of performance degradation during the early operation of an SOFC, and β is the fuel flow deviation factor, used to represent the effect of flow rate changes on decay. For fuel flow deviation, This is the time accumulation factor, used to characterize the cumulative effect of runtime on lifetime, where T is the runtime; Step S4: Output diagnostic results and generate a dynamic maintenance scheduling plan based on RUL predictions to optimize system lifespan by minimizing downtime and cost.

2. The method for state detection and health diagnosis of an SOFC system according to claim 1, characterized in that, Set to 60 to 80, attenuation rate Set to 0.015 to 0.035, and β to 0 to 0.

01. Set to 0 to 0.0005. The value range is from 0.8 to 1.

2.

3. The method for state detection and health diagnosis of an SOFC system according to claim 1, characterized in that, Step S1 specifically includes: Step S1-1: Obtain the SOFC runtime parameters; Step S1-2: The dynamic Bayesian inference algorithm performs probability modeling by updating the conditional probability distribution of the SOFC running state in real time and combining it with a Gaussian mixture model. In the formula, Let be the state variables of the system at time t. Let be the observed variables of the system at time t. For posterior probability, Let be the likelihood function. Let be the state transition probability. Let be the posterior probability of the previous time step. For observation The marginal probability, is the expected value, representing the fusion result.

4. The method for state detection and health diagnosis of an SOFC system according to claim 1, characterized in that, Step S2 specifically includes: Step S2-1: Extract multi-band features of vibration and voltage signals using Fast Fourier Transform: In the formula, Here, N represents the frequency domain coefficients, and N represents the signal length. For time-domain signals, It is a complex exponential factor; Step S2-2: Combine improved principal component analysis to reduce dimensionality and optimize feature sparsity: In the formula, X is the original feature matrix, and Z is the reduced-dimensional feature matrix. Let Z be the transpose matrix, and W be the projection matrix. Let W be the transpose of W. Let I be the sparse penalty parameter, and let I be the identity matrix.

5. The method for state detection and health diagnosis of an SOFC system according to claim 4, characterized in that, The sparsity penalty parameter is set to 0.

1.

6. The method for state detection and health diagnosis of an SOFC system according to claim 1, characterized in that, In step S3, the random forest includes 150 decision trees, each with a maximum depth of 12 layers. The generator of the generative adversarial network is a 4-layer fully connected layer, the discriminator is a 3-layer fully connected layer, and the noise dimension is 10-dimensional. The online update mechanism specifically includes adjusting the tree structure of the random forest and the weights of the generative adversarial network every 500 hours to adapt to the long-term aging of SOFC.

7. The method for state detection and health diagnosis of an SOFC system according to claim 1, characterized in that, Step S4 specifically includes: Step S4-1: Generate SOH and RUL change curves, state classification results, and three-dimensional spectrum in real time; Step S4-2: Based on the RUL prediction results, a genetic algorithm is used to optimize the maintenance schedule, with the objective function being to minimize the total cost. In the formula, and As weight, This refers to the downtime. For lifetime loss, the constraint is RUL > 100 hours.

8. The method for state detection and health diagnosis of an SOFC system according to claim 7, characterized in that, In step S4-2, The value is 0.

6. The value is 0.

4.

9. A state detection and health diagnosis system for an SOFC system, characterized in that, A method for performing state detection and health diagnosis of an SOFC system as described in any one of claims 1-8, wherein the state detection and health diagnosis system of the SOFC system comprises: The data acquisition module is used to obtain the operating parameters of SOFC; The data fusion unit uses a dynamic Bayesian inference-based fusion algorithm to optimize data consistency and noise resistance by combining the conditional probability distribution of SOFC operating status. The feature extraction module extracts frequency domain features through fast Fourier transform, combines improved principal component analysis to reduce dimensionality and retain key variables, and optimizes feature representation for the characteristics of SOFC high-temperature vibration. The AI ​​diagnostic module performs state classification based on random forest, uses generative adversarial networks to generate synthetic running data to enhance the training set, and combines an online update mechanism to improve the model's adaptability to dynamic aging, predicting SOFC health status (SOH) and remaining lifetime (RUL). The maintenance scheduling unit generates dynamic maintenance scheduling plans based on RUL predictions, optimizing system lifespan by minimizing downtime and costs.

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