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Early prediction of battery degradation in grid-scale battery energy storage system using extreme gradient boosting algorithm

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RESULTS IN ENGINEERING 2024年 21卷

作者： Apribowo, Chico Hermanu Brillianto Hadi, Sasongko Pramono Wijaya, Franscisco Danang Setyonegoro, Mokhammad Isnaeni Bambang Sarjiya Univ Gadjah Mada Dept Elect & Informat Engn Yogyakarta 55281 Indonesia Univ Sebelas Maret Dept Elect Engn Surakarta 57126 Indonesia Univ Gadjah Mada Ctr Energy Studies Yogyakarta 55281 Indonesia

The growth of battery energy storage systems (BESS) is caused by the variability and intermittent nature of high demand and renewable power generation at the network scale. In the context of BESS, Lithium-ion (Li-ion) battery occupies a crucial position, although it is faced with challenges related to performance battery degradation over time due to electrochemical processes. This battery degradation is a crucial factor to account for, based on its potential to diminish the efficiency and safety of electrical system equipment, thereby contributing to increased system planning costs. This implies that the health of battery needs to be diagnosed, particularly by determining remaining useful life (RUL), to avoid unexpected operational costs and ensure system safety. Therefore, this study aimed to use machine learning models, specifically extreme gradient boosting (XGBoost) algorithm, to estimate RUL, with a focus on the temperature variable, an aspect that had been previously underemphasized. Utilizing XGBoost model, along with fine-tuning its hyperparameters, proved to be a more accurate and efficient method for predicting RUL. The evaluation of the model yielded promising outcomes, with a root mean square error (RMSE) of 90.1 and a mean absolute percentage error (MAPE) of 7.5 %. Additionally, the results showed that the model could improve RUL predictions for batteries within BESS. This study significantly contributed to optimizing planning and operations for BESS, as well as developing more efficient and effective maintenance strategies.

关键词： Battery energy storage system Battery degradation Remaining useful life extreme gradient boosting algorithm Hyperparameter tuning

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Research on four-bar linkage trajectory synthesis using extreme gradient boosting and genetic algorithm

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2024年第2期11卷 1-21页

作者： Wang, Jianping Chen, Boyan Wang, Youchao Pu, Dexi Jia, Xiaoyue Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Peoples R China

The current study on the synthesis problems of four-bar mechanism trajectories primarily relies on establishing a numerical atlas based on trajectory characteristics and employing neural networks to synthesize mechanism parameters. However, this approach has several shortcomings, including a vast database, inefficient retrieval, and challenges in maintaining accuracy. This paper presents a method for synthesizing a trajectory-generation mechanism that combines the extreme gradient boosting (XGBoost) algorithm with a genetic algorithm (GA). The purpose is to synthesize, based on a particular trajectory, the dimensions and installation position parameters of a four-bar mechanism. The paper classifies the trajectories according to their shape features and geometric center placements, thereby improving the accuracy of the XGBoost model for synthesizing mechanisms. The XGBoost algorithm is employed to synthesize the basic dimensional parameters for the mechanism, with the relative slopes of trajectories as input features. The synthesized basic dimensional parameters are turned into parameters for the actual mechanism by researching the scaling, translation, and rotation relationships between mechanisms and the trajectories they generate. The accuracy of the generated trajectories from the synthesized mechanism can be improved by applying GA to optimize the mechanism parameters. Five comparative examples are provided in this research for the different scenarios of given trajectory curves and trajectory points. The effectiveness and accuracy of the proposed approach in this study are validated in comparison to existing research methods by comparing errors between the generated trajectories and the given trajectories. Graphical Abstract

关键词： mechanism synthesis trajectory generation genetic algorithm extreme gradient boosting algorithm

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Prediction of cement specific surface area in the cement mill grinding process: A method combining dual-frequency principal component analysis and extreme gradient boosting

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2023年第1-2期237卷 69-80页

作者： Shi, Xin Xu, Qingquan Zheng, Lizhao Hao, Xiaochen Zhao, Yantao Yanshan Univ Sch Elect Engn 438 Hebei Ave Qinhuangdao 066004 Hebei Peoples R China

The cement specific surface area is an important indicator of cement quality. The accurate prediction of the cement specific surface area aims to guide operators to control the cement grinding process to improve product quality while reducing system energy consumption. However, due to the complexity of the cement grinding process, the process variables have coupling, time-varying delay, nonlinear characteristics, and different sampling frequency. Herein, we proposed the specific surface area prediction model, which combined dual-frequency principal component analysis and extreme gradient boosting (DF-PCA-XGB). In order to solve the problem of difficulty in modeling due to different sampling intervals of related data, this paper analyzes the low-frequency sampling data and high-frequency sampling data under multiple working conditions, and establishes prediction models respectively. Aiming at the data redundancy problem of high-frequency and low-frequency variable data in the introduced time window, a method based on the combination of principal component analysis (PCA) and extreme gradient boosting (XGB) cross-validation is proposed to reduce data redundancy while retaining most of the characteristics of the data. The final specific surface area prediction results were obtained by weighting the high-frequency data model and the low-frequency data model. The simulation results showed that the prediction method in this paper can improve the prediction accuracy of the specific surface area of the finished cement product under multiple working conditions with high stability and has promising application in the cement manufacturing process.

关键词： Cement grinding process specific surface area prediction different sampling frequency principal component analysis extreme gradient boosting algorithm

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Machine learning in PEM water electrolysis: A study of hydrogen production and operating parameters

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COMPUTERS & CHEMICAL ENGINEERING 2025年 194卷

作者： Shomope, Ibrahim Al-Othman, Amani Tawalbeh, Muhammad Alshraideh, Hussam Almomani, Fares Amer Univ Sharjah Dept Chem & Biol Engn POB 26666 Sharjah U Arab Emirates Amer Univ Sharjah Coll Engn Energy Water & Sustainable Environm Res Ctr POB 26666 Sharjah U Arab Emirates Univ Sharjah Sustainable & Renewable Energy Engn Dept POB 27272 Sharjah U Arab Emirates Univ Sharjah Sustainable Energy & Power Syst Res Ctr RISE POB 27272 Sharjah U Arab Emirates Amer Univ Sharjah Dept Ind Engn PO Box 26666 Sharjah U Arab Emirates Jordan Univ Sci & Technol Ind Engn Dept POB 3030 Irbid Jordan Qatar Univ Dept Chem Engn POB 2713 Doha Qatar

Proton exchange membrane water electrolysis (PEMWE) powered by renewable energy stands out as a promising technology for the sustainable production of high-purity hydrogen. This study employed three machine learning (ML) algorithms, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), to predict hydrogen production in PEMWE. Model performance was evaluated using root mean squared error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) metrics. The top-performing models, RF and XGBoost, were further refined through hyperparameter tuning. The final models demonstrated high reliability in predicting hydrogen production rates, with RF consistently outperforming XGBoost. The RF model achieved a predictive accuracy of R2 = 0.9898, RMSE = 19.99 mL/min, and MAE = 10.41 mL/min, while the XGBoost model achieved R2 = 0.9894, RMSE = 20.43 mL/min, and MAE = 11.50 mL/min. Partial dependency plots (PDPs) emphasized the critical role of optimizing both cell voltage and current to maximize hydrogen production in PEMWE. These insights provide valuable guidance for operational adjustments, ensuring optimal system performance for high efficiency and productivity. The study suggests further research on the impact of parameters like temperature and power density on hydrogen production, incorporating them for better optimization.

关键词： Water electrolysis Machine learning Hydrogen production Random forest Support vector machine extreme gradient boosting algorithm

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Void damage detection of rubber bearings in bridge structures under laboratory conditions using the active sensing method and BO-XGBoost algorithm

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2025年

作者： Deng, Chubing Li, Yunfei Xiong, Feng Zeng, Yi Sichuan Univ Coll Architecture & Environm MOE Key Lab Deep Earth Sci & Engn Chengdu 610065 Peoples R China

In bridge engineering, void damage in rubber bearings refers to the phenomenon where gaps occur between the rubber bearing and the bridge structure due to factors such as aging, overloading, and earthquakes. Void damage can lead to abnormal stress distribution, bearing deformation failure, and uneven load transfer, thereby affecting the overall stability and safety of the bridge. Consequently, long-term monitoring of void damage is essential. However, methods such as manual inspection and computer vision have disadvantages, including high costs, lengthy processes, and limited monitoring scope. To address these challenges in void damage detection, this paper proposes a novel method that combines the active sensing approach with the Bayesian optimization (BO)-optimized extreme gradient boosting (XGBoost) algorithm. A total of 1200 sets of experimental data under different void conditions were obtained through the active sensing method, and a high-precision void damage detection model based on the BO-XGBoost algorithm was proposed. The study found that the proposed void damage detection model achieved an Accuracy value of 95.0% on the test set, indicating that the combination of the active sensing method with the BO-XGBoost algorithm can effectively solve the problem of void damage detection in laminated rubber bearings.

关键词： Laminated rubber bearing void damage active sensing method Bayesian optimization extreme gradient boosting algorithm

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Machine Learning-Based Online Multi-Fault Diagnosis for IMs Using Optimization Techniques With Stator Electrical and Vibration Data

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2024年第4期39卷 2412-2424页

作者： Hsu, Shih-Hsien Lee, Chien-Hsing Wu, Wen-Fang Jiang, Joe-Air Feng Chia Univ Dept Elect Engn Taichung 407 Taiwan Natl Cheng Kung Univ Dept Syst & Naval Mech Engn Tainan 701 Taiwan Natl Taiwan Univ Dept BioMechatron Engn Taipei 106 Taiwan

Induction motors (IMs) have been commonly applied to industrial fields since the past decades;thus, developing advanced fault diagnosis methods becomes vital for IM applications. This study proposed an online fault diagnosis system for IMs based on the Random Forest (RF) and extreme gradient boosting (XGBoost) algorithms to reduce the additional repair costs and prevent unexpected downtime. It focused on detecting healthy three-phase IMs and five common fault conditions of the IMs, involving broken rotor bars, rotor unbalance, and composite faults with short-circuited stator windings that combined two or three types of the faults, for practical purposes. The experimental results show that the model performance improved by 15% over the default model when train-test split ratios, feature selection, and hyperparameter optimization, notably in XGBoost, are considered. The proposed XGBoost model enables a high accuracy of 96.06% for RF to perform a motor fault diagnosis under six different motor conditions. Furthermore, the execution time required by the proposed fault diagnosis system is 57% less than the time required by existing motor fault diagnosis methods. These results successfully demonstrate the effectiveness of the methods proposed in this study for online motor diagnosis.

关键词： extreme gradient boosting algorithm machine learning motor failure diagnosis random forest algorithm

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Prediction of ultimate bearing capacity of single pile in composite formation based on HGS-XGBoost algorithm

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APPLIED GEOPHYSICS 2025年 1-18页

作者： Sun, Zong-jun Han, Ying-feng Jiang, Feng Wang, Gang Zheng, Wen Liu, Fei Wu, Yue Shandong Univ Sci & Technol Dept Civil Engn Qingdao 266590 Peoples R China Qingdao Ruihan Technol Grp Co Ltd Qingdao 266000 Peoples R China Fujian Univ Technol Coll Civil Engn Fuzhou 350118 Peoples R China Fujian HC Technol Internet Things Co Ltd Fuzhou 350004 Peoples R China

The inherent spatial heterogeneity of complex geological formations results in large differences in the ultimate bearing capacity of individual piles, and it is difficult to reliably quantify and assess the bearing capacity of individual piles. In this paper, based on the results of on-site static load test and sensitivity analysis method, eight sensitive factors are screened out, and the extreme gradient boosting algorithm (XGBoost) is used to predict the ultimate load carrying capacity of individual piles, however, the computed coefficient of determination is less than 0.9, and the prediction effect needs to be strengthened. On this basis, three kinds of swarm intelligent optimization algorithms are introduced to adaptively match the XGBoost hyper-parameters, and the results show that the HGS-XGBoost hybrid prediction model can more accurately calculate the ultimate bearing capacity of a single pile under the composite strata, and the prediction effect can satisfy the engineering requirements when using the HGS-XGBoost prediction model for the actual project.

关键词： composite formation sensitivity analysis extreme gradient boosting algorithm ultimate bearing capacity

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Decoding the synergy: unveiling gradient boosting regression model for multivariate quantitation of pioglitazone, alogliptin and glimepiride in pure and tablet dosage forms

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BMC CHEMISTRY 2024年第1期18卷 1-13页

作者： Elkhoudary, Mahmoud M. Marie, Aya A. Hammad, Sherin F. Salim, Mohamed M. Kamal, Amira H. Horus Univ Egypt Fac Pharm Dept Pharmaceut Chem New Damietta 34517 Egypt Tanta Univ Fac Pharm Dept Pharmaceut Analyt Chem Tanta 31527 Egypt Mansoura Univ Fac Pharm Dept Pharmaceut Analyt Chem Mansoura 35516 Egypt Tanta Univ Fac Pharm Med CampusElgeish St Tanta 31111 Egypt

This study represents a comparison among the performances of four multivariate procedures: partial least square (PLS) and artificial neural networks (ANN) in addition to support vector regression (SVR) and extreme gradient boosting (XG Boost) algorithm for the determination of the anti-diabetic mixture of pioglitazone (PIO), alogliptin (ALG) and glimepiride (GLM) in pharmaceutical formulations with aid of UV spectrometry. Key wavelengths were selected using knowledge-based variable selection and various preprocessing methods (e.g., mean centering, orthogonal scatter correction, and principal component analysis) to minimize noise and improve model precision. XG Boost effectively enhanced computing speed and accuracy by focusing on specific spectral features rather than the entire spectrum, demonstrating its advantages in resolving complex, overlapping spectral data. The independent test results of different models demonstrated that XG Boost outperformed other methods. XG Boost achieved the lowest root mean squared error of prediction (RMSEP) and standard deviation (SD) values across all compounds, indicating minimal prediction error and variability. For PIO, XG Boost recorded an RMSEP of 0.100 and SD of 0.369, significantly better than PLS and ANN. For ALG, XG Boost showed near-perfect performance with an RMSEP of 0.001 and SD of 0.005, outperforming SVR and PLS, which had higher error rates. In the case of GLM, XG Boost also excelled with an RMSEP of 0.001 and SD of 0.018, demonstrating superior precision compared to the much higher errors seen in PLS and ANN. These results highlight XG Boost's exceptional ability to handle complex, overlapping spectral data, making it the most reliable and accurate model in this study.

关键词： Partial least squares UV/Vis spectroscopy Artificial neural networks extreme gradient boosting algorithm Support vector regression

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An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2024年第1期12卷

作者： Nagar, Harsh Machavaram, Rajendra Ambuj, Peeyush Soni, Peeyush Saha, Subhajit Bose, T. Subhash Chandra Indian Inst Technol Kharagpur Agr & Food Engn Dept Kharagpur 721302 W Bengal India

Knowing tractor drawbar pull is crucial to ensure the tractor can handle the required workload efficiently and safely, preventing soil damage and optimising field productivity. The present study proposes a novel approach for tractor drawbar pull prediction by utilising the tractor's geometric parameters and forward speed to develop a cloud-infused, server-less, machine learning-based real-time generalised tractor drawbar pull prediction model for any tractor between the 6-58 kW power range. The drawbar pull prediction models from ANN and six ML algorithms were developed, and the data analysis with hyperparameter tuning concluded that the extreme gradient boosting (XGB) ML model outperformed the other ML models. A reasonable accuracy with R2 = 0.93 and MAPE = 6.77% was achieved using the XGB ML model for a separate validation dataset, which was not used for training. Furthermore, a cloud-based serverless Android App integrated with the XGB ML-based drawbar pull prediction model was developed for real-time tractor drawbar pull prediction and monitoring during tillage operations. The field validation demonstrated the XGB ML model's generalisation ability and effectiveness, with R2 = 0.90 and maximum MAPE of 9.86%. It can be used to simulate and optimize tractor performance, guiding manufacturers in selecting geometric parameters for tractor design.

关键词： Tractor drawbar pull prediction generalised model machine learning extreme gradient boosting algorithm cloud-based serverless android app

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Utility of Machine Learning algorithms in Predicting Preoperative Lymph Node Metastasis in Patients With Rectal Cancer Based on Three-Dimensional Endorectal Ultrasound and Clinical and Laboratory Data

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JOURNAL OF ULTRASOUND IN MEDICINE 2023年第11期42卷 2615-2627页

作者： Huang, Weiqin Lin, Ruoxuan Ke, Xiaohui Ni, Shixiong Zhang, Zhen Tang, Lina Fujian Med Univ Fujian Canc Hosp Clin Oncol Sch Dept Ultrasonog 420 Fuma Rd Fuzhou 350014 Peoples R China China Med Univ Affiliated Hosp 1 Dept Ultrasound 155 Nanjing St Shenyang Peoples R China

Background-We aimed to investigate the value of a machine learning (ML) algorithm in the preoperative prediction of lymph node metastasis in patients with rectal cancer. Methods-Based on the histopathological results, 126 rectal cancer patients were divided into two groups: lymph node metastasis-positive and metastasis-negative groups. We collected clinical and laboratory data, three-dimensional endorectal ultrasound (3D-ERUS) findings, and parameters of the tumor for between-group comparisons. We constructed a clinical prediction model based on the ML algorithm, which demonstrated the best diagnostic performance. Finally, we analyzed the diagnostic results and processes of the ML model. Results-Between the two groups, there were significant differences in serum carcinoembryonic antigen (CEA) levels, tumor length, tumor breadth, circumferential extent of the tumor, resistance index (RI), and ultrasound T-stage (P < 0.05). The extreme gradient boosting (XGBoost) model had the best comprehensive diagnostic performance for predicting lymph node metastasis in patients with rectal cancer. Compared with experienced radiologists, the XGBoost model showed significantly higher diagnostic value in predicting lymph node metastasis;the area under curve (AUC) value of the receiver operating characteristic (ROC) curve of the XGBoost model and experienced radiologists was 0.82 and 0.60, respectively. Conclusions-Preoperative predictive utility in lymph node metastasis was demonstrated by the XGBoost model based on the 3D-ERUS finding and related clinical information. This could be useful in guiding clinical decisions on the selection of different treatment strategies.

关键词： endorectal ultrasound extreme gradient boosting algorithm lymph node metastasis machine learning algorithm rectal cancer

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