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A Novel Method for Aging Prediction of Railway Catenary Based on Improved kalman filter

Structural Durability & Health Monitoring 2024年第1期18卷 73-90页

作者： Jie Li Rongwen Wang Yongtao Hu Jinjun Li School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang453003China Technology Center Sichuan Injet Electric Co.Ltd.Deyang618000China Embedded System Research Institute Xinxiang Engineering Research Center for Intelligent Condition Monitoring of MachineryXinxiang453003China

The aging prediction of railway catenary is of profound significance for ensuring the regular operation of electrified trains.However,in real-world scenarios,accurate predictions are challenging due to various interferences.This paper addresses this challenge by proposing a novel method for predicting the aging of railway catenary based on an improved kalman filter(KF).The proposed method focuses on modifying the priori state estimate covariance and measurement error covariance of the KF to enhance accuracy in complex environments.By comparing the optimal displacement value with the theoretically calculated value based on the thermal expansion effect of metals,it becomes possible to ascertain the aging status of the catenary.To improve prediction accuracy,a railway catenary aging prediction model is constructed by integrating the Takagi-Sugeno(T-S)fuzzy neural network(FNN)and KF.In this model,an adaptive training method is introduced,allowing the FNN to use fewer fuzzy rules.The inputs of the model include time,temperature,and historical displacement,while the output is the predicted displacement.Furthermore,the KF is enhanced by modifying its prior state estimate covariance and measurement error covariance.These modifications contribute to more accurate predictions.Lastly,a low-power experimental platform based on FPGA is implemented to verify the effectiveness of the proposed method.The test results demonstrate that the proposed method outperforms the compared method,showcasing its superior performance.

关键词： Railway catenary Takagi-Sugeno fuzzy neural network kalman filter aging prediction

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A dual adaptive unscented kalman filter algorithm for SINS-based integrated navigation system

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Journal of Systems Engineering and Electronics 2024年第3期35卷 732-740页

作者： LYU Xu MENG Ziyang LI Chunyu CAI Zhenyu HUANG Yi LI Xiaoyong YU Xingkai Department of Precision Instrument Tsinghua UniversityBeijing 100084China College of Mechanical and Power Engineering Three Gorges UniversityYichang 443002China Unit 91001 of the PLA Beijing 100161China School of Control and Computer Engineering North China Electric Power UniversityBeijing 100096China

In this study, the problem of measuring noise pollution distribution by the intertial-based integrated navigation system is effectively suppressed. Based on nonlinear inertial navigation error modeling, a nested dual kalman filter framework structure is developed. It consists of unscented kalman filter (UKF)master filter and kalman filter slave filter. This method uses nonlinear UKF for integrated navigation state estimation. At the same time, the exact noise measurement covariance is estimated by the kalman filter dependency filter. The algorithm based on dual adaptive UKF (Dual-AUKF) has high accuracy and robustness, especially in the case of measurement information interference. Finally, vehicle-mounted and ship-mounted integrated navigation tests are conducted. Compared with traditional UKF and the Sage-Husa adaptive UKF (SH-AUKF), this method has comparable filtering accuracy and better filtering stability. The effectiveness of the proposed algorithm is verified.

关键词： kalman filter dual-adaptive integrated navigation unscented kalman filter(UKF) robust

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kalman filter-Based CNN-BiLSTM-ATT Model for Traffic Flow Prediction

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Computers, Materials & Continua 2023年第7期76卷 1047-1063页

作者： Hong Zhang Gang Yang Hailiang Yu Zan Zheng Institute of Transportation Inner Mongolia UniversityHohhot010020China Inner Mongolia Engineering Research Center for Urban Transportation Data Science and Applications Hohhot010020China

To accurately predict traffic flow on the highways,this paper proposes a Convolutional Neural Network-Bi-directional Long Short-Term Memory-Attention Mechanism(CNN-BiLSTM-Attention)traffic flow prediction model based on kalman-filtered data processing.Firstly,the original fluctuating data is processed by kalman filtering,which can reduce the instability of short-term traffic flow prediction due to unexpected accidents.Then the local spatial features of the traffic data during different periods are extracted,dimensionality is reduced through a one-dimensional CNN,and the BiLSTM network is used to analyze the time series information.Finally,the Attention Mechanism assigns feature weights and performs Soft-max regression.The experimental results show that the data processed by kalman filter is more accurate in predicting the results on the CNN-BiLSTM-Attention model.Compared with the CNN-BiLSTM model,the Root Mean Square Error(RMSE)of the Kal-CNN-BiLSTM-Attention model is reduced by 17.58 and Mean Absolute Error(MAE)by 12.38,and the accuracy of the improved model is almost free from non-working days.To further verify the model’s applicability,the experiments were re-run using two other sets of fluctuating data,and the experimental results again demonstrated the stability of the model.Therefore,the Kal-CNN-BiLSTM-Attention traffic flow prediction model proposed in this paper is more applicable to a broader range of data and has higher accuracy.

关键词： Highway traffic flow prediction kalman filter CNN-BiLSTM-Attention

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kalman filter and H_(∞)filter Based Linear Quadratic Regulator for Furuta Pendulum

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Computer Systems Science & Engineering 2022年第11期43卷 605-623页

作者： N.Arulmozhi T.Aruldoss Albert Victorie Department of Electronics and Instrumentation Engineering Government College of TechnologyCoimbatoreTamil NaduIndia Department of Electrical and Electronics Engineering Anna UniversityRegional CampusCoimbatoreTamil NaduIndia

This paper deals with Furuta Pendulum(FP)or Rotary Inverted Pendulum(RIP),which is an under-actuated non-minimum unstable non-linear process.The process considered along with uncertainties which are unmodelled and analyses the performance of Linear Quadratic Regulator(LQR)with kalman filter and H∞filter as two filter configurations.The LQR is a technique for developing practical feedback,in addition the desired x shows the vector of desirable states and is used as the external input to the closed-loop system.The effectiveness of the two filters in FP or RIP are measured and contrasted with rise time,peak time,settling time and maximum peak overshoot for time domain performance.The filters are also tested with gain margin,phase margin,disk stability margins for frequency domain performance and worst case stability margins for performance due to uncertainties.The H-infinity filter reduces the estimate error to a minimum,making it resilient in the worst case than the standard kalman filter.Further,when theβrestriction value lowers,the H∞filter becomes more robust.The worst case gain performance is also focused for the two filter configurations and tested where H∞filter is found to outperform towards robust stability and performance.Also the switchover between the two filters is dependent upon a user-specified co-efficient that gives the flexibility in the design of non-linear systems.The non-linear process is tested for set point tracking,disturbance rejection,un-modelled noise dynamics and uncertainties,which records robust performance towards stability.

关键词： Furuta pendulum linear quadratic regulator kalman filter non-linear process two filter configurations

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Resilient timekeeping algorithm with multi‑observation fusion kalman filter

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Satellite Navigation 2023年第3期4卷 142-152页

作者： Xiaobin Wang Yuanxi Yang Bo Wang Yuting Lin Chunhao Han Space Engineering University Beijing 101416China Beijing Satellite Navigation Center Beijing 100094China State Key Laboratory of Geo‑Information Engineering Xi’an Research Institute of Surveying and MappingXi’an 710054China Department of Precision Instrument Tsinghua UniversityBeijing 100094China National Astronomical Observatories Chinese Academy of SciencesBeijing 100012China

The timescales incorporated into the Primary Frequency Standard(PFS)exhibit excellent stability and accuracy.However,during the dead time of PFS,the reliability of the timescale can be compromised.To address this issue,a resilient timekeeping algorithm with a Multi-observation Fusion kalman filter(MFKF)is proposed.This algorithm fuses the frequency measurements from hydrogen masers with various reference frequency standards,including PFS and commercial cesium beam atomic clocks.The simulation results show that the time deviation and instability of the timescale generated by MFKF are improved compared to those with kalman filtering.The experimental results demonstrate that even within 70 days of PFS dead time the resilient timescale generated by MFKF can operate reliably.Furthermore,it is theoretically proven that MFKF produces a smaller post-covariance than that with singleobservation kalman filtering.

关键词： Resilient timekeeping kalman filter Primary frequency standard

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On distributed kalman filter based state estimation algorithm over a bearings-only sensor network

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Science China(Technological Sciences) 2023年第11期66卷 3174-3185页

作者： LIANG ChenXu XUE WenChao FANG HaiTao ZHANG Ran The Key Laboratory of Systems and Control National Center for Mathematics and Interdisciplinary SciencesAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Astronautics Beihang UniversityBeijing 100083China

This paper studies the distributed state estimation problem for a class of discrete-time linear time-varying systems over a bearingsonly sensor network.A novel fusion estimation algorithm of the distance between the target and each sensor is constructed with the mean square error matrix of corresponding estimation being timely provided.Then,the refined estimation of distance is presented by minimizing the mean square error matrix.Furthermore,the distributed kalman filter based state estimation algorithm is proposed based on the refined distance estimation.It is rigorously proven that the proposed method has the consistency and stability.Finally,numerical simulation results show the effectiveness of our methods.

关键词： bearings-only measurements sensor network kalman filter distributed filter

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Gaussian process regression-based quaternion unscented kalman robust filter for integrated SINS/GNSS

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Journal of Systems Engineering and Electronics 2022年第5期33卷 1079-1088页

作者： LYU Xu HU Baiqing DAI Yongbin SUN Mingfang LIU Yi GAO Duanyang College of Electrical Engineering Naval University of EngineeringWuhan 430033China Beijing Huahang Radio Measurement Research Institute Beijing 100000China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121001China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

High-precision filtering estimation is one of the key techniques for strapdown inertial navigation system/global navigation satellite system(SINS/GNSS)integrated navigation system,and its estimation plays an important role in the performance evaluation of the navigation system.Traditional filter estimation methods usually assume that the measurement noise conforms to the Gaussian distribution,without considering the influence of the pollution introduced by the GNSS signal,which is susceptible to external interference.To address this problem,a high-precision filter estimation method using Gaussian process regression(GPR)is proposed to enhance the prediction and estimation capability of the unscented quaternion estimator(USQUE)to improve the navigation accuracy.Based on the advantage of the GPR machine learning function,the estimation performance of the sliding window for model training is measured.This method estimates the output of the observation information source through the measurement window and realizes the robust measurement update of the filter.The combination of GPR and the USQUE algorithm establishes a robust mechanism framework,which enhances the robustness and stability of traditional methods.The results of the trajectory simulation experiment and SINS/GNSS car-mounted tests indicate that the strategy has strong robustness and high estimation accuracy,which demonstrates the effectiveness of the proposed method.

关键词： integrated navigation Gaussian process regression(GPR) quaternion kalman filter robustness

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Applying the kalman filter particle method to strange and open charm hadron reconstruction in the STAR experiment

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Nuclear Science and Techniques 2023年第10期34卷 148-157页

作者： Xin-Yue Ju Yue-Hang Leung Sooraj Radhakrishnann Petr Chaloupka Xin Dong Yury Fisyak Pavol Federic Ivan Kisel Hong-Wei Ke Michal Kocan Spyridon Margetis Ai-Hong Tang Iouri Vassiliev Yi-Fei Zhang Xiang-Lei Zhu Maksym Zyzak University of Science and Technology of China Hefei 230026China Lawrence Berkeley National Laboratory BerkeleyCA 94720USA University of Heidelberg 69120 HeidelbergGermany Kent State University KentOH 44242USA Czech Technical University in Prague PragueCzech Republic Brookhaven National Laboratory UptonNY 11973USA Nuclear Physics Institute of the Czech Academy of Sciences PragueCzech Republic Goethe-Universitat Frankfurt Frankfurt am MainGermany Frankfurt Institute for Advanced Studies Frankfurt am MainGermany GSI Helmholtzzentrum für Schwerionenforschung GmbH DarmstadtGermany Tsinghua University Beijing 100084China

We applied KF Particle, a kalman filter package for secondary vertex finding and fitting, to strange and open charm hadron reconstruction in heavy-ion collisions in the STAR experiment. Compared to the conventional helix swimming method used in STAR, the KF Particle method considerably improved the reconstructed Λ, Ω, and D~0 significance. In addition, the Monte Carlo simulation with STAR detector responses could adequately reproduce the topological variable distributions reconstructed in real data using the KF Particle method, thereby retaining substantial control of the reconstruction efficiency uncertainties for strange and open charm hadron measurements in heavy-ion collisions.

关键词： Heavy-ion collisions Secondary vertex finding kalman filter

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Dynamic State Estimation of Power Systems with Uncertainties Based on Robust Adaptive Unscented kalman filter

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Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1065-1074页

作者： Dongchen Hou Yonghui Sun Jianxi Wang Linchuang Zhang Sen Wang College of Energy and Electrical Engineering Hohai UniversityNanjing210098China

In this paper,a robust adaptive unscented kalman filter(RAUKF)is developed to mitigate the unfavorable effects derived from uncertainties in noise and in the model.To address these issues,a robust M-estimator is first utilized to update the measurement noise covariance.Next,to deal with the effects of model parameter errors while considering the computational complexity and real-time requirements of dynamic state estimation,an adaptive update method is produced.The proposed method is integrated with spherical simplex unscented transformation technology,and then a novel derivative-free filter is proposed to dynamically track the states of the power system against uncertainties.Finally,the effectiveness and robustness of the proposed method are demonstrated through extensive simulation experiments on an IEEE 39-bus test system.Compared with other methods,the proposed method can capture the dynamic characteristics of a synchronous generator more reliably.

关键词： Dynamic state estimation kalman filter synchronous generator unscented transformation robust estimation

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Adaptive nonlinear kalman filters based on credibility theory with noise correlation

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Chinese Journal of Aeronautics 2024年第6期37卷 232-243页

作者： Quanbo GE Zihao SONG Bingtao ZHU Bingjun ZHANG School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing 210044China School of Electrical and Automation Engineering East China Jiaotong UniversityNanchang 330013China School of Logistics Engineering Shanghai Maritime UniversityShanghai 201306China School of Electronic and Information Engineering Tongji UniversityShanghai 200092China

To solve the divergence problem and overcome the difficulty in guaranteeing filtering accuracy during estimation of the process noise covariance or the measurement noise covariance with traditional new information-based nonlinear filtering methods,we design a new method for estimating noise statistical characteristics of nonlinear systems based on the credibility kalman filter(KF)theory considering noise correlation.This method first extends credibility to the Unscented kalman filter(UKF)and Extended kalman filter(EKF)based on the credibility theory.Further,an optimization model for nonlinear credibility under noise related conditions is established considering noise correlation.A combination of filtering smoothing and credibility iteration formula is used to improve the real-time performance of the nonlinear adaptive credibility KF algorithm,further expanding its application scenarios,and the derivation process of the formula theory is provided.Finally,the performance of the nonlinear credibility filtering algorithm is simulated and analyzed from multiple perspectives,and a comparative analysis conducted on specific experimental data.The simulation and experimental results show that the proposed credibility EKF and credibility UKF algorithms can estimate the noise covariance more accurately and effectively with lower average estimation time than traditional methods,indicating that the proposed algorithm has stable estimation performance and good real-time performance.

关键词： kalman filter Extended kalman filter(EKF) Unscented kalman filter(UKF) Credibility Noise correlation

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