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Control-Oriented Extraction and prediction of Key Performance Features Affecting Performance Variability of Solid Oxide Fuel Cell System

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2024年第1期10卷 1771-1787页

作者： Peng, Jingxuan Huang, Jian Li, Yuanzheng Xu, Yuan-Wu Wu, Xiao-Long Wang, Zhuo Wang, Jingjing Li, Xi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Imaging Proc & Intelligent Control Educ M Wuhan 430074 Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330031 Jiangxi Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518055 Peoples R China

Performance prediction technology enhances the dependability and safety of solid oxide fuel cell (SOFC) systems. However, unreasonable prediction objects and insufficient prediction accuracy impede the advancement of prediction methodologies in SOFC systems. To better predict SOFC system performance changes, two algorithms are developed in this article: a mutual information (MI)-based feature selection algorithm that identifies variables sensitive to SOFC performance changes and a particle swarm algorithm optimized backpropagation (BP) neural network (NN) (PSO_BP). The PSO_BP algorithm predicts hard-to-measure variables (e.g., heat exchanger temperature and combustion chamber temperature) using easy-to-measure data. Real data tests reveal significant improvements in feature reduction, prediction accuracy, and speed. The MI algorithm reduces feature variables by 70% (from 40 to 12), aiding sensor layout optimization. The PSO_BP algorithm trains in 2 s and achieves less than 0.5% prediction error. It can also predict hard-to-measure temperature data, which can assist researchers in optimizing SOFC system performance.

关键词： Feature extraction prediction algorithms Mutual information System performance Temperature sensors Temperature measurement Layout Feature selection mutual information (MI) particle swarm algorithm optimized backpropagation (BP) neural network (NN) (PSO_BP) performance prediction solid oxide fuel cell (SOFC)

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Hybrid Method for Remaining Useful Life prediction of Power IGBT Modules in High-Speed Trains

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2024年第11期39卷 15101-15117页

作者： Liu, Hengzhi Zhang, He-sheng Tang, Yicong Beijing Jiaotong Univ Sch Elect Engn Beijing 100044 Peoples R China

Accurately predicting the remaining useful life (RUL) of power insulated gate bipolar transistor (IGBT) modules is crucial for high-speed trains. Challenges under actual train operations, including significant uncertainty, multivariability, and insufficient full-lifecycle datasets of performance degradation parameters (PDPs), hinder the accurate RUL prediction. Thus, a hybrid RUL prediction approach is proposed. To address the significant uncertainty in PDP, variational mode decomposition (VMD) is utilized to segregate low-frequency trend information from the high-frequency uncertainty information, enabling distinct prediction approaches for diverse data. Besides, kernel density estimation-bidirectional long short-term memory network (KDE-BiLSTM) is proposed to precisely quantify and forecast the significant uncertainty information. Moreover, to tackle the multivariability of PDP, a Wiener-based transferable trend information modeling technique is introduced. Furthermore, to predict PDP trends with insufficient datasets, a model-based method employing regularized particle filter-RIME-least squares support vector machine (RPF-RIME-LSSVM) is proposed for trend information prediction. RIME-LSSVM solves the missing observation problem in the RPF prediction phase. Ultimately, the integration of the trend and uncertainty information yields the final RUL prediction. The proposed method was validated utilizing Infineon 6500 V/750 A IGBT modules. The maximum RUL prediction error was 9000 cycles, validating the method's effectiveness. The prediction error of at least 7000 cycles below the baseline models demonstrated the method's superiority.

关键词： Insulated gate bipolar transistors Market research Uncertainty Predictive models Degradation prediction algorithms Accuracy High-speed train model-based method multialgorithm multivariability performance degradation parameter (PDP) power insulated gate bipolar transistor (IGBT) module remaining useful life (RUL) prediction

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GNN-Based Concentration prediction With Variable Input Flow Rates for Microfluidic Mixers

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2024年第3期18卷 622-635页

作者： Ji, Weiqing Guo, Xingzhuo Pan, Shouan Long, Fei Ho, Tsung-Yi Schlichtmann, Ulf Yao, Hailong Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong 999077 Peoples R China Tech Univ Munich TUM Chair Elect Design Automat D-80333 Munich Germany Minist Educ Key Lab Adv Mat & Devices Postmoore Chips Beijing Peoples R China

Recent years have witnessed significant advances brought by microfluidic biochips in automating biochemical protocols. Accurate preparation of fluid samples is an essential component of these protocols, where concentration prediction and generation are critical. Equipped with the advantages of convenient fabrication and control, microfluidic mixers demonstrate huge potential in sample preparation. Although finite element analysis (FEA) is the most commonly used simulation method for accurate concentration prediction of a given microfluidic mixer, it is time-consuming with poor scalability for large biochip sizes. Recently, machine learning models have been adopted in concentration prediction, with great potential in enhancing the efficiency over traditional FEA methods. However, the state-of-the-art machine learning-based method can only predict the concentration of mixers with fixed input flow rates and fixed sizes. In this paper, we propose a new concentration prediction method based on graph neural networks (GNNs), which can predict output concentrations for microfluidic mixters with variable input flow rates. Moreover, a transfer learning method is proposed to transfer the trained model to mixers of different sizes with reduced training data. Experimental results show that, for microfluidic mixers with fixed input flow rates, the proposed method obtains an average reduction of 88% in terms of prediction errors compared with the state-of-the-art method. For microfluidic mixers with variable input flow rates, the proposed method reduces the prediction error by 85% on average. Besides, the proposed transfer learning method reduces the training data by 84% for extending the pre-trained model for microfluidic mixers of different sizes with acceptable prediction error.

关键词： Mixers Microfluidics Biochips prediction algorithms Predictive models Convolutional neural networks Circuits and systems Microfluidic biochips sample preparation graph convolutional network concentration prediction

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Hybrid Algorithm for Dynamic Fault prediction of HVDC Converter Transformer Using DGA Data

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2024年第4期31卷 2128-2135页

作者： Patil, Manojkumar Paramane, Ashish Das, Suchandan Rao, U. Mohan Rozga, Pawel Natl Inst Technol Elect Engn Dept Silchar 788010 Assam India Univ Quebec Chicoutimi Dept Appl Sci Chicoutimi G7H 2B1 PQ Canada Lodz Univ Technol PL-90537 Lodiz Poland Lodz Univ Technol Elect Power Engn PL-90537 Lodiz Poland

Very limited studies have forecast the gas concentration and faults of high voltage direct current (HVDC) converter transformers. In this study, a hybrid algorithm consisting seasonal autoregressive integrated moving average-convolutional neural network-gated recurrent unit (SARIMA-CNN-GRU) was proposed to forecast the seven gas concentrations, i.e., hydrogen (H-2), methane (CH4), ethane (C2H6), ethylene (C2H4), acetylene (C2H2), carbon-dioxide (CO2), and carbon monoxide (CO). Moreover, extreme gradient boost (XGBoost), adaptive boosting (AdaBoost), and light gradient boosting machine (LightGBM) were utilized to predict the fault inside the HVDC converter transformer. Two types of datasets collected from different sources were used to train the regression and classification models. The performance of the regression model was evaluated by root mean squared error and mean absolute error, whereas the performance of the classification model was evaluated by accuracy, precision, and recall. Finally, a comparative analysis was conducted to showcase the superiority of the proposed methodology.

关键词： Oil insulation Power transformer insulation Forecasting Predictive models HVDC transmission Time series analysis prediction algorithms Classification dissolved gas analysis forecasting machine learning oil-immersed transformer

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Assessing Machine Learning Approaches for Photovoltaic Energy prediction in Sustainable Energy Systems

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IEEE ACCESS 2024年 12卷 107599-107615页

作者： Abdelsattar, Montaser Ismeil, Mohamed A. Zayed, Mohamed M. A. Azim Abdelmoety, Ahmed Emad-Eldeen, Ahmed South Valley Univ Fac Engn Elect Engn Dept Qena 83523 Egypt King Khalid Univ Fac Engn Elect Engn Dept Abha 61411 Saudi Arabia Beni Suef Univ Coll Educ & Technol Elect Technol Dept Bani Suwayf 2722165 Egypt Univ Prince Mugrin Coll Comp & Cyber Sci Dept Comp Sci Madinah 42241 Saudi Arabia Beni Suef Univ Fac Postgrad Studies Adv Sci PSAS Renewable Energy Sci & Engn Dept Bani Suwayf 62511 Egypt

Precise forecasting of solar power output is crucial for integrating renewable energy into power networks, improving efficiency and dependability. This study assesses the efficacy of several Machine Learning (ML) algorithms in predicting solar power generation through a detailed performance comparison. This paper analyzes six algorithms: CatBoost, Gradient Boosting Machines (GBMs), Multilayer Perceptron (MLP) regressor, Support Vector Machines (SVMs), XGBoost, and Random Forest (RF). Using a dataset of 4213 sets of solar power generation data, each model was trained and tested, with performance evaluated based on R-squared (R-2) scores for the whole dataset, training set, and test set. Also, this study examined the mean and standard deviation of test set predictions to gauge how consistent each model was. The results showed that RF had the highest overall R-2 score of 0.940 and a training set score of 0.971. XGBoost demonstrated exceptional performance on the test set, attaining a high R-2 score of 0.822. CatBoost and GBMs exhibited strong performance, albeit with slightly lower R-2 values of 0.786 and 0.829, respectively. Although the MLP regressor and SVMs exhibited high training scores, they encountered difficulties in generalizing to unfamiliar data. This paper highlights the effectiveness of combining XGBoost and RF techniques in improving the accuracy of solar power forecasts. The investigation focuses on enhancing the precision and reliability of renewable energy projections through a comprehensive comparison of various contemporary ML techniques.

关键词： Renewable energy sources Predictive models Solar power generation Accuracy prediction algorithms Solar energy Forecasting Machine learning predictive modeling renewable energy forecasting solar power generation

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A Tunnel Squeezing prediction Model Based on the Hierarchical Belief Rule Base

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IEEE ACCESS 2024年 12卷 161786-161797页

作者： Zhang, Kexin Zhao, Yawen Duan, Xiping Wang, Yuhe Ma, Ning Liang, Jiabin Harbin Normal Univ Sch Comp Sci & Informat Engn Harbin 150025 Peoples R China Sixth Engn Co Ltd China Railway Engn Grp 3 Jinzhong 030600 Peoples R China

Tunnel squeezing is a time-dependent significant deformation problem often occurring in weak rock masses and areas with high horizontal in situ stress. This phenomenon can cause construction delays, increased budgets, tunnel collapse, and additional problems. Therefore, accurately predicting tunnel squeezing is crucial. The surrounding rock of the tunnel is uneven in hardness and softness, and changes frequently due to the influence of geological structure, thereby rendering the prediction of extrusion deformation highly uncertain. The belief rule base (BRB) is a rule-based modeling approach capable of handling uncertain information. However, the performance of the BRB model is not only affected by the combinatorial rule explosion caused by too many input attributes but also by the limitations of expert knowledge. First, to solve the problem of the combinatorial rule explosion, a novel tunnel squeezing prediction model using a hierarchical BRB structure based on the Random Forest (RF) attribute selection method (H-RF-BRB) is proposed. Second, to avoid the limitations of expert knowledge, parameters of the tunnel squeezing prediction model are determined by combining the expert knowledge and the information gain ratio (IGR). The model effectively integrates qualitative knowledge with quantitative information, addressing the issue of limitations in expert knowledge. Additionally, it overcomes the challenge of limited datasets due to the difficulties in collecting tunnel squeezing samples, which enhances the accuracy of the model's predictions. Finally, the model's effectiveness and superiority are validated through five-fold cross-validation and several comparative experiments.

关键词： Predictive models Accuracy Radio frequency Data models Rocks Analytical models prediction algorithms Deformation Stress Optimization Belief rule base tunnel squeezing random forest evidence reasoning

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Remaining-Useful-Lifetime prediction of Proton Exchange Membrane Fuel Cell Considering Model Uncertainty Quantification on the Full-Time Scale

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2024年第3期10卷 7443-7455页

作者： Yu, Xiaoran Yang, Yang Xie, Changjun Li, Yang Zhao, Bo Zhang, Leiqi Song, Jie Deng, Zhanfeng Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China Wuhan Univ Technol Hubei Key Lab Adv Technol Automot Components Wuhan Peoples R China Chalmers Univ Technol Dept Elect Engn S-41258 Gothenburg Sweden State Grid Zhejiang Elect Power Res Inst Hangzhou 310014 Peoples R China State Grid Smart Grid Res Inst Co Ltd Beijing 102211 Peoples R China

A prognostics and health management (PHM) system with prediction at its core optimizes the durability of the proton exchange membrane fuel cell (PEMFC). However, the aging behavior model has some uncertainty due to limited knowledge, affecting the predictive performance in remaining useful life (RUL) prediction. To address this issue, an RUL prediction method based on the Bayesian framework considering uncertainty quantification on the full-time scale is proposed. First, the state of health (SOH) of the PEMFC is estimated, and the behavior of uncertainty is quantified. Afterward, a long short-term memory (LSTM) neural network is employed to make a prediction for its behavior. Finally, the RUL of PEMFC is predicted based on historical SOH and the predicted behavior of uncertainty. Validation indicates that the proposed method can make a long-term prediction and provide RUL prediction with high accuracy. Under the dynamic operating condition, in terms of long-term prediction, compared to unscented Kalman filter (KF), adaptive unscented KF, double-input-echo-state-network, and bidirectional LSTM, the proposed method decreases the error by 88.12%, 41.99%, 13.82%, and 3.21%, respectively. Also, under dynamic operating conditions, the proposed method shows good stability. Moreover, the robustness of this method has also been verified.

关键词： Uncertainty Aging Mathematical models Predictive models Behavioral sciences Fuel cells prediction algorithms Bayesian framework full-time scale prediction of remaining useful life (RUL) proton exchange membrane fuel cell (PEMFC) uncertainty quantification

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Critical Plane Methodology for Intelligent prediction of Fretting Fatigue Lifetime Based on Shear Strain Dynamics

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IEEE ACCESS 2024年 12卷 37120-37128页

作者： Zhou, Wen Sun, Weiming Xu, Hui Duan, Guosheng Zhang, Yuliang Mao, Kun Song, Ming Quzhou Special Equipment Inspection Ctr Quzhou 324000 Peoples R China Quzhou Key Lab New Energy Equipment Safety & Intel Quzhou 324000 Peoples R China Zhejiang Univ Technol Coll Mech Engn Hangzhou 310014 Peoples R China Hangzhou Special Equipment Inspect & Res Inst Hangzhou 310051 Peoples R China Longyou Jinyi Thermal Power Co Ltd Quzhou 324400 Peoples R China Quzhou Univ Coll Mech Engn Quzhou 324100 Peoples R China Quzhou Coll Technol Fac Informat Engn Quzhou 324100 Peoples R China China Special Equipment Inspect & Res Inst Beijing 100029 Peoples R China

An approach based on refined critical plane methodology to fretting fatigue lifetime prediction for structures was proposed. A new shear stress-strain interaction (SSI) critical plane equation is developed, predicated on shear strain energy for predicting fretting fatigue lifetime. In order to obtain the fretting fatigue lifetime quickly and intelligently, the SSI critical plane method intelligent prediction software was developed using the Newton-Raphson iteration method. To corroborate the utility of the SSI critical plane method, we conducted a fretting fatigue lifetime test and then compared the calculated results of the SSI fretting fatigue lifetime prediction and traditional SWT prediction with the experimental values. The results indicate that SSI fretting fatigue lifetime prediction is closer to the experimental results than SWT.

关键词： Fatigue Stress prediction algorithms Testing Servomotors Q measurement Lifetime estimation Strain measurement Stress measurement SSI critical plane method fretting fatigue lifetime intelligent prediction shear strain dynamics

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A Novel Energy Consumption prediction Model Integrating Real-Time Traffic State Recognition and Velocity prediction of BEVs

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IEEE ACCESS 2024年 12卷 196178-196194页

作者： Li, Yue Jiang, Yu Guo, Jianhua Xie, Dong Jilin Univ Coll Automot Engn Changchun 130025 Peoples R China

The widespread adoption of battery electric vehicles (BEVs) has highlighted the critical importance of precise energy consumption prediction models to address the problem of range anxiety among drivers. This study aims to enhance the accuracy of such models by combining real-time traffic state recognition and velocity prediction, thereby mitigating range anxiety and enhancing the driving experience. Consequently, we propose an improved Fuzzy C-Means (FCM) clustering algorithm that use historical traffic data and dynamic traffic information accurately identify traffic conditions. In addition, a Fuzzy-Markov-based velocity prediction model is developed to generate future velocity profiles under diverse traffic scenarios. In the energy consumption prediction stage, a particle swarm optimization-radial basis function neural network (PSO-RBFNN) model is employed to estimation the energy consumption. Simulation results show a significant improvement in prediction accuracy, with the Mean Absolute Percentage Error (MAPE) reduced to below 3.2% under diverse traffic scenarios.

关键词： Energy consumption Predictive models Roads Accuracy prediction algorithms Vehicle dynamics Real-time systems Hidden Markov models Heuristic algorithms Anxiety disorders Battery electric vehicles energy consumption prediction traffic state recognition velocity prediction

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Secrecy Performance Intelligent prediction for Mobile Vehicular Networks: An DI-CNN Approach

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第7期25卷 7363-7373页

作者： Xu, Lingwei Tang, Huihui Li, Hui Li, Xingwang Gulliver, Thomas Aaron Le, Khoa N. Qingdao Univ Sci & Technol Dept Informat Sci & Technol Qingdao 530000 Peoples R China Guangxi Informat Ctr Guangxi Key Lab Digital Infrastruct Nanning 530000 Peoples R China Guangxi Univ Sci & Technol Guangxi Key Lab Automobile Components & Vehicle Te Liuzhou 545006 Peoples R China Henan Polytech Univ Sch Phys & Elect Informat Engn Jiaozuo 454000 Peoples R China Univ Victoria Dept Elect & Comp Engn Victoria BC V8W 2Y2 Canada Western Sydney Univ Sch Comp Engn & Math Kingswood NSW 2747 Australia

The rapid expansion of Internet of Vehicles (IoV) networks has facilitated high throughput and reliable vehicular communications. Mobile vehicular networks face the challenges: diversification of network equipment, user mobility, and the broadcast nature of wireless channels, so physical layer security modeling of IoV communication systems has become important. The complexity of wireless communication channels makes real-time prediction of secrecy performance challenging. This paper presents an analysis of secrecy performance for mobile vehicular networks. To ensure data secure transmission, we have employed the decode-and-forward (DF) relaying scheme. The signal-to-noise ratio (SNR) of the effective end-to-end link is employed to obtain the mathematical expression results, which can evaluate the secrecy performance. The theoretical secrecy performance is confirmed via simulation. Then, we design a dense-inception convolution neural network (DI-CNN) model, and propose a DI-CNN-based intelligent prediction ***, ShuffleNetV2, RegNet and YOLOv5 methods are employed to analyze the performance of DI-CNN algorithm. It is shown that the DI-CNN approach has a prediction accuracy that is 48.8% better than Transformer.

关键词： Predictive models prediction algorithms Convolution Signal to noise ratio Feature extraction Deep learning Transformers Mobile IoV network physical layer security secrecy capacity DI-CNN

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