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A load estimation model for axle box bearings of high-speed trains based on vehicle dynamic response

MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2024年 221卷

作者： Yang, Chen Wu, Xingwen Chi, Maoru Cai, Wubin Ma, Zikui Yao, Xuesong Southwest Jiaotong Univ State Key Lab Rail Transit Vehicle Syst Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Schaeffler Trading Shanghai Co Ltd Shanghai 201804 Peoples R China

The axle box bearing is a critical component of high-speed train bogies, closely related to vehicle safety. Accurate bearing boundary load is crucial for bearing fatigue life assessment. This paper derives the dynamic mechanism equations of a novel axle box bearing-half vehicle system in detail and develops a bearing load estimation model for straight scenarios based on the augmented kalman filter algorithm and mechanism equations. Evaluation indices for the load estimation effectiveness are formulated by comparing estimated results with those calculated by a virtual test model. Subsequently, an improved niche genetic algorithm combined with the virtual mapping relationship is proposed to optimize and match key parameters of the estimation model. Validation and analysis of the load estimation model under different operating conditions are conducted. The results suggest that the estimated vertical, lateral, rolling, and yaw loads of the eight axle box bearings in the entire vehicle are consistent with those calculated by the virtual test model, with correlation coefficients above 0.9. The proposed estimation model has good applicability for identifying the service load of axle box bearings under different measured wheel and rail irregularities, carbody weights, and vehicle speeds, which has important implications for monitoring the boundary load of high-speed train axle box bearings.

关键词： Axle box bearing Load estimation Bearing-half vehicle system dynamics kalman filter algorithm Improved niche genetic algorithm High-speed train

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Wind power prediction based on PSO-kalman

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ENERGY REPORTS 2022年 8卷 958-968页

作者： Li, Daoqing Yu, Xiaodong Liu, Shulin Dong, Xia Zang, Hongzhi Xu, Rui Qilu Univ Technol Shandong Acad Sci Sch Elect Engn & Automat Jinan 250353 Peoples R China State Grid Shandong Elect Power Co Econ & Technol Res Inst Jinan Peoples R China Huaneng JinanHuangtai Power Generat Co Ltd Jinan Peoples R China

Because of its clean and green, wind power is broadly used all over the world. Wind power is random and unstable, so wind power integration will inevitably bring great impact to power system. Accurate wind power prediction can effectively alleviate the impact caused by wind power uncertainty. In order to increase the accuracy of wind power prediction, this article uses paper swarm optimization algorithm (PSO) to improve the traditional kalman filter, and PSO-kalman wind power point prediction model is established. The proposed model solves the problem of low prediction accuracy of traditional kalman filter caused by observation noise and process noise. Finally, based on point prediction error, non-parametric kernel density estimation is used for interval prediction. By experimental simulation, by comparing the error evaluation indexes of point prediction and interval prediction, it can be found that the point prediction error of PSO-kalman is the smallest, indicating that PSO can effectively improve the prediction accuracy of kalman. On this basis, the interval prediction performance is also better than before. Moreover, the model proposed in this article converges fast and has better general applicability. (C) 2022 The Authors. Published by Elsevier Ltd.

关键词： Wind power prediction Particle swarm optimization algorithm (PSO) kalman filter algorithm Non-parametric kernel density estimation

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Very Short-term Electric Load Forecasting for Real-time Power System Operation

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2018年第4期13卷 1419-1424页

作者： Jung, Hyun-Woo Song, Kyung-Bin Park, Jeong-Do Park, Rae-Jun Soongsil Univ Dept Elect Engn Seoul South Korea KEPCO Econ & Management Res Inst Busan South Korea Uiduk Univ Div Energy & Elect Engn Gyeongju South Korea

Very short-term electric load forecasting is essential for real-time power system operation. In this paper, a very short-term electric load forecasting technique applying the kalman filter algorithm is proposed. In order to apply the kalman filter algorithm to electric load forecasting, an electrical load forecasting algorithm is defined as an observation model and a state space model in a time domain. In addition, in order to precisely reflect the noise characteristics of the kalman filter algorithm, the optimal error covariance matrixes Q and R are selected from several experiments. The proposed algorithm is expected to contribute to stable real-time power system operation by providing a precise electric load forecasting result in the next six hours.

关键词： Very short-term electric load forecasting kalman filter algorithm

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Robust maximum correlation entropy kalman filtering algorithm based on S-functions under impulse noise

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第SUPPL 1期18卷 113-127页

作者： Huo, Yuanlian Yang, Kuojian Qi, Yongfeng Xu, Tianci Northwest Normal Univ Coll Phys & Elect Engn 967 Anning East Rd Lanzhou 730070 Gansu Peoples R China Northwest Normal Univ Coll Comp Sci & Engn 967 Anning East Rd Lanzhou 730070 Gansu Peoples R China

The impulse noise is heavy-tailed and distributed, which is common in many real-world scenarios of automatic control and target tracking. In order to improve the stability of kalman filtering algorithms in the context of impulse noise, a number of kalman filtering algorithms have been widely developed using the maximum correlation entropy instead of the minimum mean square error criterion. To better adapt to impulse noise environments. In this paper, we propose a new kalman filtering algorithm called robust maximum correlation entropy kalman filtering algorithm based on S-functions (HWSKF). The algorithm is based on a new cost function framework. On the one hand, this framework employs the properties of the Softplus function fused with the maximum correlation entropy to ensure that the algorithm is better adapted to the impulsive noise environment with faster convergence speed and stronger steady-state properties. On the other hand, the introduction of Huber regression weighting can effectively exclude the effect of outliers in the case of non-Gaussian noise. Under different impulse noise environments, we demonstrate through Monte Carlo simulation experiments that the algorithm proposed in this paper possesses high stationarity compared with the classical kalman filtering algorithm (KF), the maximum correlation entropy kalman filtering algorithm (MCKF), and the maximum correlation entropy kalman filtering algorithm based on the student's kernel (STTKF).

关键词： kalman filter algorithm Huber regression Impulse noise Softplus function Robust estimation

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Optimal Placement and Sizing of Multiple DGs in a Practical Distribution System by Considering Power Loss

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2013年第5期49卷 2262-2270页

作者： Lee, Soo Hyoung Park, Jung-Wook Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Yonsei Univ Sch Elect & Elect Engn Seoul 120749 South Korea

The selection of placement and size for multiple distributed generators based on renewable energies becomes an important issue in operating modern power systems safely and reliably. This paper extends the previous study in our earlier paper by treating the problems for their optimal locations and sizes in more realistic way based on the power loss sensitivities and kalman filter algorithm, respectively. In particular, the proposed method is implemented by the simulation on a part of practical distribution network of 60 MVA scale, which is located at the Do-gok area in Seoul, Korea. The new factor, called as the optimal locator index, is developed to determine the optimal locations in a systematic and effective manner by the power loss sensitivities. Also, the kalman filter algorithm is applied to determine the optimal sizes by choosing the step size of 10 KW, which is a much smaller increment than the 10-MW step used in our earlier paper, without requiring high computational efforts. This selected step size of 10 KW is realistic and suitable to apply in practical test systems.

关键词： Distributed generator (DG) grid connection kalman filter algorithm load-concentration bus optimal location optimal locator index (OLI) optimal size power loss sensitivity power margin

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An adaptive fuzzy neural network for MIMO system model approximation in high-dimensional spaces

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 1998年第3期28卷 436-446页

作者： Chak, CK Feng, G Ma, J Univ New S Wales Sch Elect Engn Dept Syst & Control Sydney NSW 2052 Australia City Univ Hong Kong Dept Informat Syst Kowloon Hong Kong

An adaptive fuzzy system implemented within the framework of neural network is proposed. The integration of the fuzzy system into a neural network enables the new fuzzy system to have learning and adaptive capabilities. The proposed fuzzy neural network can locate its rules and optimize its membership functions by competitive learning, kalman filter algorithm and extended kalman filter algorithms. A key feature of the new architecture is that a high-dimensional fuzzy system can be implemented with fewer number of rules than the Takagi-Sugeno fuzzy systems. A number of simulations are presented to demonstrate the performance of the proposed system including modeling nonlinear function, operator's control of chemical plant, stock prices and bioreactor (multioutput dynamical system).

关键词： competitive learning extended kalman filter algorithm fuzzy systems kalman filter algorithm neural networks supervised learning

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Sound field measurement and evaluation research for radiated acoustic fields in amplitude-variable sonochemical systems

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MEASUREMENT & CONTROL 2019年第9-10期52卷 1499-1507页

作者： Kong, Yaguang Tao, Xuyang Chen, Zhangpin Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Zhejiang Peoples R China

The content of this study is based on the background of sonochemistry processes. First, the hydrophone is used to measure the sound pressure in the reactor (the experimental ultrasonic frequency range is 20 +/- 1 kHz). The sound pressure signals are processed by threshold method, kalman filter algorithm, and five-point three-time smoothing method. These methods eliminate singular items in the sound pressure signal, filter noise, smooth burrs, and so on. In addition, the sound intensity value is calculated by dual hydrophone method. In this paper, the average intensity of ultrasonic cavitation is studied. The radial distribution and longitudinal distribution of cavitation intensity of the same tool head are studied, and the effects of different power and different tool heads on cavitation intensity are also discussed in this paper. The experimental results show that the instrument designed in this paper can effectively measure the distribution of ultrasonic sound field and evaluate the performance of the tool head. We can infer from the experiments that the performance of the diamond tool head is better than that of the nine-section whip and the dumbbell tool head.

关键词： Ultrasonic cavitation acoustic field distribution kalman filter algorithm hydrophone

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Predictive calculation of ion current environment of dc transmission line based on ionised flow model of embedded short-term wind speed

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第16期12卷 3837-3843页

作者： Yi, Yong Chen, Zhengying Tang, Wenxi Wang, Liming Tsinghua Univ Shenzhen Environm Sci & New Energy Technol Engn L TBSI Shenzhen 518055 Guangdong Peoples R China Tsinghua Univ Grad Sch Shenzhen Shenzhen 518055 Guangdong Peoples R China

The predictive calculation and wind effect on the ion flow environment of dc transmission line are evaluated by embedded short-term wind speed based ionised flow model. The wind has a major impact on the electric field and ion current density profiles. In the ionised flow model of embedded short-term wind speed, the short-term wind speed is calculated by using time series model and kalman filter algorithm. The ionised field considering short-term wind speed is solved by finite-element method with acceleration technique and time-domain finite volume method. The algorithm is validated by the coaxial cylinder electrode configuration and practical bipolar dc transmission configuration. Computational results are made to provide a physical understanding of wind effect on the corona formation process. In the timescale of long-term prediction, the time evolution behaviour of ground-level ion current density and electric field with time-varying wind speed is estimated.

关键词： power transmission lines wind power electrochemical electrodes time series kalman filters time-domain analysis finite volume methods finite element analysis corona current density electric fields HVDC power transmission predictive calculation ion current environment dc transmission line ionised flow model embedded short-term wind speed electric field ion current density profiles time series model kalman filter algorithm finite-element method acceleration technique time-domain finite volume method coaxial cylinder electrode configuration practical bipolar dc transmission configuration wind effect corona formation process time evolution behaviour ground-level ion current density time-varying wind speed

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KF-based estimation of diode turn-off power loss using datasheet information

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ELECTRONICS LETTERS 2019年第20期55卷 1082-+页

作者： Naseri, F. Farjah, E. Ghanbari, T. Shiraz Univ Sch Elect & Comp Engn Adv Power Elect & Elect Vehicle Lab Shiraz Iran Shiraz Univ Sch Adv Technol Shiraz Iran

In this Letter, an effective method for estimation of turn-off power losses of power diodes using datasheet information is proposed. An effective regression method with non-linear least-squares algorithm is used to obtain approximate representations of the reverse-recovery charge and softness factor of the diode using datasheet information. kalman filter (KF) algorithm is then used to accurately acquire the value of diode forward current at the turn-off instant. Finally, the obtained information is used to estimate the turn-off power losses with simple calculations, based on approximated reverse-recovery equations. Despite the simplicity of the proposed technique, the results demonstrate that the estimation error does not exceed 4% in the worst case.

关键词： least squares approximations kalman filters regression analysis power semiconductor diodes reverse-recovery charge datasheet information kalman filter algorithm turn-off instant turn-off power losses approximated reverse-recovery equations estimation error KF-based estimation diode turn-off power loss power diodes effective regression method approximate representations nonlinear least-squares algorithm

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Assemblability based on maximum likelihood configuration of tolerances

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IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION 1999年第3期15卷 568-572页

作者： Sanderson, AC Natl Sci Fdn Arlington VA 22230 USA

An assembly is defined by a configuration of parts of known geometries subject to tolerances in the pose, dimensions, and mating relations among part features. Using a tolerance model based on matrix transforms and Gaussian models of geometric variations [1], the pose and dimensional tolerance models are considered as a priori models of the assembly with nominal and variational components for both position and orientation. The mating relations are regarded as linear relational constraints, also with nominal and variational components. With this formulation, estimation of the configuration of parts may be posed as a maximum Likelihood problem and solved by a kalman filter algorithm. The resulting maximum likelihood configuration of the assembly may be used to evaluate the required deviation from nominal and the assemblability as defined by the maximum likelihood clearance from constraints. In addition. application of the technique to intermediate subassemblies may be used to evaluate assemblability of specific steps and discriminate among alternative assembly sequence plans.

关键词： ANSI standards assemblability Assembly assembly planning design for manufacture dimensional tolerance model filtering theory Fixtures Gaussian distribution Gaussian models geometric variations Information geometry intermediate subassemblies kalman filter algorithm kalman filters linear relational constraints Manufacturing mating relations matrix algebra matrix transforms maximum likelihood estimation maximum likelihood tolerance configuration pose tolerance model Product design Solid modeling Stability tolerance model Uncertainty variational components

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