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Enhancing bearing life prediction: sparse gaussian process regression approach based on sequential ensemble and residual reduction for degradation prediction

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 256卷

作者： Hou, Wanjun Peng, Yizhen Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China State Key Lab Mech Transmiss Adv Equipment Chongqing 400044 Peoples R China

Bearings are critical components of wind turbines, and predicting their remaining useful life is essential for ensuring safe and reliable operation of wind power generators. However, the degradation of wind turbine bearings exhibits distinct multi-stage characteristics, and full-lifecycle degradation samples are rarely available. This lack of samples makes it challenging to accurately predict the service life of bearings. Therefore, we propose a residual-reduction sequential ensemble sparse gaussian process regression model to enhance bearing life prediction. The proposed model introduces a primary learner based on a gaussian process regression serial ensemble strategy, effectively simulating the multi-stage dynamic bearing degradation process. Building on this learner, the model constructs a secondary learner within the gradient-boosting framework by applying residual- reduction techniques to further increase predictive accuracy. The proposed method is applied to a public dataset and a real wind turbine bearing degradation dataset, and its superiority is validated through comparisons with existing methods.

关键词： Bearing life prediction sparse gaussian process regression Gradient boosting Residual-reduction sequential ensemble

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State-of-health estimation method for fast-charging lithium-ion batteries based on stacking ensemble sparse gaussian process regression

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 242卷

作者： Li, Fang Min, Yongjun Zhang, Ying Zhang, Yong Zuo, Hongfu Bai, Fang Nanjing Forestry Univ Coll Automobile & Traff Engn Nanjing 210037 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 210016 Peoples R China Nanjing Res Inst Elect Engn Nanjing 210023 Peoples R China

gaussian process regression (GPR) is extensively employed in lithium-ion battery state-of-health (SOH) estimation, which ensures the safe, reliable operation of electric vehicles (EVs). However, a single GPR can produce performance discrepancies across different fast-charge batteries, as well as high time consumption. Therefore, we propose an SOH estimation method for fast-charging batteries based on stacking ensemble sparse gaussian process regression (SGPR). First, health factors are extracted in partial discharge fragments to reflect battery degradation. Then, SGPRs based on the fully independent training condition (FITC) are developed with different kernel functions as level-1 learners, and a genetic algorithm (GA) is used to optimize the parameters of the kernel function. Further, the level-2 learner integrates the features produced by the level-1 learner based on cross validation. Finally, the accuracy, robustness, and reliability of the proposed method were evaluated under various fast-charging experiments. The results show that the mean absolute error (MAE) and root mean square error (RMSE) of SOH estimation were within 1.0852% and 1.2123%, respectively, and that the average relative time consumption was reduced by 85.68% compared with stacking ensemble GPR. Thus, the proposed method has broad application prospects in processing vast datasets from numerous batteries in monitoring platforms or cloud data centers.

关键词： Lithium -ion battery State of health sparse gaussian process regression Ensemble learning Genetic algorithm

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An efficient method of predicting S-wave velocity using sparse gaussian process regression for a tight sandstone reservoir

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JOURNAL OF APPLIED GEOPHYSICS 2024年 229卷

作者： Dang, Yi Zhang, Yijie Wu, Baohai Li, Hui Gao, Jinghuai Xian Jiaotong Univ sity Sch Informat & Commun Engn Xian 710049 Shaanxi Peoples R China

The shear wave (S-wave) velocity plays a crucial role in interpreting the lithology in seismic data, identifying fluids and predicting reservoirs. However, S-wave velocity is often unavailable due to the high cost of measurement and technical constraints. Conventional methods exhibit limitations that potentially impact the accuracy or efficiency on predicting S-wave velocity. Moreover, these methods always ignore the uncertainty quantification associated with the predicted results. This paper proposes a sparse gaussian process regression (SGPR) method to predict the S-wave velocity in tight sandstone reservoirs. SGPR is a highly efficient regression technique that is based on the gaussian process regression (GPR) method. In the SGPR method, inducing inputs are introduced to approximate the kernel matrix to decrease the computational complexity. A sparse set of inducing inputs and kernel hyperparameters are optimized through minimizing the Kullback-Leibler (KL) divergence between the exact posterior distribution and the approximate one. In this study, we select several types of logging data, which include porosity, water saturation, shale content, lithology and P-wave velocity, as the inputs for the SGPR method to predict S-wave velocity. To validate its effectiveness, we use the SGPR method to predict S-wave velocity in tight sandstone and compare the results with those from the GPR method, the bidirectional long short-term memory (BiLSTM) method and the Xu-White model. Additionally, we conduct cross-validation to demonstrate the robustness of the SGPR method. Our findings indicate that the SGPR method presents better performance and significant advantages about the accuracy and efficiency. Moreover, the SGPR method offers uncertainty quantification for the predicted S-wave velocity.

关键词： S -wave velocity sparse gaussian process regression Tight sandstone reservoir Uncertainty Quantification

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sparse gaussian process regression in real-time myoelectric control

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2021年第1期39卷 51-60页

作者： Jung, Myong Chol Chai, Rifai Zheng, Jinchuan Nguyen, Hung Swinburne Univ Technol Fac Sci Engn & Technol Hawthorn Vic 3122 Australia

In myoelectric control, nonlinear regression models, gaussian process (GP) in specific, have shown promising accuracy in estimation, but no study has been conducted to evaluate the real-time performance of GP regression. In this work, the real-time performance of sparse GP regression is evaluated with 17 able-bodied subjects. Unlike the existing training methods, in which training protocols are strictly pre-determined, a novel training method is proposed. The subjects' real-time performance adjusts training time and the number of training samples. While the majority of subjects showed similar learning rates, there was a significant difference between a few subjects (p < 0.05). As a result of real-time performance, the subjects completed 97% of the average tasks and achieved 80% path efficiency comparable to existing methods.

关键词： sparse gaussian process regression regression real-time myoelectric control real-time control myoelectric control nonlinear regression electromyography EMG human-computer interface rehabilitation engineering

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State-of-charge dependent equivalent circuit model identification for batteries using sparse gaussian process regression

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JOURNAL OF process CONTROL 2022年 112卷 1-11页

作者： Fan, Kesen Wan, Yiming Jiang, Benben Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Tsinghua Univ Ctr Intelligent & Networked Syst Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China

Due to their ease of implementation, equivalent circuit models (ECMs) of batteries are widely used in battery management systems. Generally, ECM parameters vary with operating conditions, thus how such parameter dependencies are addressed substantially influences the accuracy of an ECM over a wide operating range. In this paper, we identify an ECM whose parameters have nonlinear dependence on state-of-charge (SOC). By transforming the SOC-dependent ECM into a linear parameter varying (LPV) input-output model, we propose a non-parametric sparse gaussian process regression (GPR) approach, which alleviates the difficulty of specifying parametric functional SOC-dependencies of model parameters. The proposed approach derives the posterior distributions of ECM parameters, thus is capable to provide both parameter estimates and their associated uncertainties. The computational cost over large datasets is significantly reduced by adopting the sparse GPR. The proposed approach is applied to the above LPV model with two noise model structures, i.e., white and colored noises. Identification results using experimental data illustrate the efficacy of the proposed approach. The use of colored noise enhances robustness under different noise levels, and achieves higher output prediction accuracy over experimental datasets. (C)& nbsp;2021 Published by Elsevier Ltd.

关键词： Equivalent circuit model State-of-charge dependent parameters Linear parameter varying system sparse gaussian process regression

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Online Battery State-of-Charge Estimation Based on sparse gaussian process regression

Online Battery State-of-Charge Estimation Based on Sparse Ga...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Ozcan, Gozde Pajovic, Milutin Sahinoglu, Zafer Wang, Yebin Orlik, Philip V. Wada, Toshihiro Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA Mitsubishi Elect Res Labs 201 Broadway Cambridge MA 02139 USA Mitsubishi Electr Corp Adv Technol R&D Ctr 8-1-1 Tsukaguchi Honmachi Amagasaki Hyogo 6618661 Japan

ISBN: (纸本)9781509041688

This paper presents a new online method for state-of charge (SoC) estimation of Lithium-ion (Li-ion) batteries based on sparse gaussian process regression (GPR). Building upon sparse approximation of the regular GPR, the proposed method is computationally more efficient. The battery SoC is estimated based on measured voltage, current and temperature. The accuracy of the proposed method is verified using LiMn2O4/hard-carbon battery data collected from a constant-current discharge test. In addition, the estimation performance of the proposed method is compared with a SoC estimation method using regular GPR with different covariance functions.

关键词： Battery management system Lithium-ion battery sparse gaussian process regression state of charge estimation

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sparse gaussian process regression based Ground Segmentation for Autonomous Land Vehicles 27

Sparse Gaussian Process Regression based Ground Segmentation...

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27th Chinese Control and Decision Conference (CCDC)

作者： Chen, Tongtong Dai, Bin Liu, Daxue Song, Jinze Natl Univ Def Technol Coll Mechatron Engn & Automat Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Grad Sch Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781479970162

Ground segmentation, which is the base of the successive object detection and recognition, is a crucial component for Autonomous Land Vehicles (ALV) equipped with Velodyne LIDAR navigation in outdoor environments. This paper presents a novel algorithm based on sparse gaussian process regression (GPR) for segmenting three-dimensional scans of various terrains. The 3D points of a scan are firstly mapped into 3D grid map, and then iterative two-dimensional GPR with sparse covariance function is exploited to model the ground surface directly. In order to verify the performance of our approach, It has been compared with two previous ground segmentation techniques on the data collected by our own ALV in different outdoor scenes. The results show that our approach can obtain promising performance.

关键词： sparse gaussian process regression Ground Segmentation Autonomous Land Vehicles Velodyne

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Online Battery State-of-Charge Estimation Based on sparse gaussian process regression

Online Battery State-of-Charge Estimation Based on Sparse Ga...

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IEEE Power and Energy Society General Meeting

作者： Gozde Ozcan Milutin Pajovic Zafer Sahinoglu Yebin Wang Philip V. Orlik Toshihiro Wada Department of Electrical Engineering and Computer Science Syracuse University Mitsubishi Electric Research Laboratories Advanced Technology R&D Center Mitsubishi Electric Corporation

ISBN: (纸本)9781509041695

关键词： Battery management system Lithium-ion battery sparse gaussian process regression State of charge estimation Battery management systems lithium ion battery Battery charging status ground penetrating radar Batteries gaussian processes Gauss

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sparse gaussian process regression based Ground Segmentation for Autonomous Land Vehicles

Sparse Gaussian Process Regression based Ground Segmentation...

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第27届中国控制与决策会议

作者： Tongtong Chen Bin Dai Daxue Liu Jinze Song College of Mechatronic Engineering and Automation National University of Defense Technology The Graduate School National University of Defense Technology

ISBN: (纸本)9781479970186

Ground segmentation,which is the base of the successive object detection and recognition,is a crucial component for Autonomous Land Vehicles(ALV) equipped with Velodyne LIDAR navigation in outdoor *** paper presents a novel algorithm based on sparse gaussian process regression(GPR) for segmenting three-dimensional scans of various *** 3D points of a scan are firstly mapped into 3D grid map,and then iterative two-dimensional GPR with sparse covariance function is exploited to model the ground surface *** order to verify the performance of our approach,It has been compared with two previous ground segmentation techniques on the data collected by our own ALV in different outdoor *** results show that our approach can obtain promising performance.

关键词： sparse gaussian process regression Ground Segmentation Autonomous Land Vehicles Velodyne

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A sparse Nonstationary Trigonometric gaussian process regression and Its Application on Nitrogen Oxide Prediction of the Diesel Engine

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第12期17卷 8367-8377页

作者： Huang, Haojie Song, Yedong Peng, Xin Ding, Steven X. Zhong, Weimin Du, Wei East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Weichai Power Co Ltd Shanghai 200000 Peoples R China Univ Duisburg Essen Inst Automat Control & Complex Syst D-47057 Duisburg Germany

gaussian process regression (GPR) has shown superiority in terms of state estimation for its nonparametric characteristic and uncertainty prediction ability. Due to its heavy computational complexity, GPR is generally used for small datasets. To efficiently deal with the big data, the sparse spectrum approximation method has been successfully applied to GPR to decrease the computational complexity. However, the stationarity of this method is a strict assumption for data and usually mismatches the industrial processes. In this article, we proposed a sparse nonstationary GPR, which can deal with the nonstationary relationship among samples and make the model more flexible, to settle the aforementioned problems. Furthermore, the performance of the proposed method is evaluated using three public datasets and a sampled diesel engine dataset, and the results show the superiority of our proposed method in terms of accuracy.

关键词： Kernel gaussian processes Informatics Computational complexity Standards sparse representation Diesel engines gaussian process regression (GPR) nonstationarity sparse gaussian process regression

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