scholarly journals Tension Estimation Method for Cable With Damper Using Natural Frequencies

2021 ◽  
Vol 7 ◽  
Author(s):  
Aiko Furukawa ◽  
Katsuya Hirose ◽  
Ryosuke Kobayashi
Keyword(s):  
Natural Frequencies ◽  
Numerical Models ◽  
Estimation Error ◽  
Estimation Method ◽  
Higher Order ◽  
Bending Stiffness ◽  
Estimation Accuracy ◽  
Test Cases ◽  
Numerical Verification ◽  
Vibration Method

In the maintenance of cable structures, such as cable-stayed bridges and extra-dosed bridges, it is necessary to estimate the tension acting on the cables. The safety of a cable is confirmed by checking whether the tension acting on the cable is within the allowable value. In current Japanese practice, the tension of a cable is estimated using the vibration method or the higher-order vibration method, which considers the natural frequencies of the cable. However, in recent years, the aerodynamic vibration of cables caused by wind has become a problem owing to the recent increase in the cable length and low damping performance of the cable itself. To suppress the aerodynamic vibration of cables, dampers are installed onto the cables. Because the damper changes the cable’s natural frequencies, the vibration method and higher-order vibration method are inappropriate for measuring the tension of a cable with a damper. In this paper, a new tension estimation method for a cable with a damper is proposed. To model a cable with a tensioned Bernoulli-Euler beam, theoretical equations for estimating the natural frequencies were derived. The proposed method inversely estimates the tension and bending stiffness of the cable and damper parameters, simultaneously, from the natural frequencies. The validity of the proposed method was confirmed by conducting numerical simulations and experiments. In the numerical verification, the performance of the proposed method was investigated using 80 numerical models. In the experimental verification, the estimation accuracy of the proposed method was investigated by considering 16 test cases. Thus, it was confirmed that the tension estimation accuracy was high, whereas the bending stiffness and damper parameter estimation accuracy was unsatisfactory. The tension estimation error was within 10% in all experimental cases, and within 5% if two test cases are excluded. The results obtained by the numerical and experimental verifications confirmed the effectiveness of the proposed method in tension estimation.

scholarly journals VIAE-Net: An End-to-End Altitude Estimation through Monocular Vision and Inertial Feature Fusion Neural Networks for UAV Autonomous Landing

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6302
Author(s):  
Xupei Zhang ◽  
Zhanzhuang He ◽  
Zhong Ma ◽  
Peng Jun ◽  
Kun Yang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Feature Fusion ◽  
Estimation Error ◽  
Estimation Method ◽  
Signal Interference ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
End To End ◽  
Altitude Estimation

Altitude estimation is one of the fundamental tasks of unmanned aerial vehicle (UAV) automatic navigation, where it aims to accurately and robustly estimate the relative altitude between the UAV and specific areas. However, most methods rely on auxiliary signal reception or expensive equipment, which are not always available, or applicable owing to signal interference, cost or power-consuming limitations in real application scenarios. In addition, fixed-wing UAVs have more complex kinematic models than vertical take-off and landing UAVs. Therefore, an altitude estimation method which can be robustly applied in a GPS denied environment for fixed-wing UAVs must be considered. In this paper, we present a method for high-precision altitude estimation that combines the vision information from a monocular camera and poses information from the inertial measurement unit (IMU) through a novel end-to-end deep neural network architecture. Our method has numerous advantages over existing approaches. First, we utilize the visual-inertial information and physics-based reasoning to build an ideal altitude model that provides general applicability and data efficiency for neural network learning. A further advantage is that we have designed a novel feature fusion module to simplify the tedious manual calibration and synchronization of the camera and IMU, which are required for the standard visual or visual-inertial methods to obtain the data association for altitude estimation modeling. Finally, the proposed method was evaluated, and validated using real flight data obtained during a fixed-wing UAV landing phase. The results show the average estimation error of our method is less than 3% of the actual altitude, which vastly improves the altitude estimation accuracy compared to other visual and visual-inertial based methods.

Rail Neutral Temperature Estimation Using Field Data, Numerical Models, and Machine Learning

2021 ◽  
Author(s):  
Yuning Wu ◽  
Xuan Zhu ◽  
Chi-Luen Huang ◽  
Sangmin Lee ◽  
Marcus Dersch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Field Data ◽  
Thermal Stresses ◽  
Numerical Models ◽  
Estimation Method ◽  
Supervised Machine Learning ◽  
Estimation Accuracy ◽  
Optimization Approach ◽  
Neutral Temperature ◽  
Wide Range

Abstract Effective Rail Neutral Temperature (RNT) management is needed for continuous welded rail (CWR). RNT is the temperature at which the longitudinal stress of a rail is zero. Due to the lack of expansion joints, CWR develops internal tensile or compressive stresses when the rail temperature is below or above, respectively, the RNT. Mismanagement of RNT can lead to rail fracture or buckling when thermal stresses exceed the limits of rail steel. In this work, we propose an effective RNT estimation method structured around four hypotheses. The work leverages field-collected vibration test data, high-fidelity numerical models, and machine learning techniques. First, a contactless non-destructive and non-disruptive sensing technology was developed to collect real-world rail vibrational data. Second, the team established an instrumented field test site at a revenue-service line in the state of Illinois and performed multi-day data collection to cover a wide range of temperature and thermal stress levels. Third, numerical models were developed to understand and predict rail vibration behavior under the influence of temperature and longitudinal load. Excellent agreement between model and experimental results were obtained using an optimization approach. Finally, a supervised machine learning algorithm was developed to estimate RNT using the field-collected rail vibration data. Sensitivity studies and error analyses were included in this work. The system performance with field data indicates that the proposed framework can support reasonable RNT estimation accuracy when measurement or model noise is low.

scholarly journals Consensus-Based Distributed Target Tracking with False Data Injection Attacks over Radar Network

2021 ◽  
Vol 11 (10) ◽  
pp. 4564
Author(s):  
Yongtao Shui ◽  
Yu Wang ◽  
Yu Li ◽  
Yongzhi Shan ◽  
Naigang Cui ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
State Estimation ◽  
Target Tracking ◽  
Estimation Error ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Consensus Method ◽  
False Data Injection ◽  
Radar Network ◽  
Global Consistency

For target tracking in radar network, any anomaly in a part of the system can quickly spread over the network and lead to tracking failures. False data injection (FDI) attacks can damage the state estimation mechanism by modifying the radar measurements with unknown and time-varying attack variables, therefore making traditional filters inapplicable. To tackle this problem, we propose a novel consensus-based distributed state estimation (DSE) method for target tracking with FDI attacks, which is effective even when all radars are under FDI attacks. First, a real-time residual-based detector is introduced to the DSE framework, which can effectively detect FDI attacks by analyzing the statistical properties of the residual. Secondly, a simple yet effective attack parameter estimation method is proposed to provide attack parameter estimation based on a pseudo-measurement equation, which has the advantage of decoupled estimation of state and attack parameters compared with augmented state filters. Finally, for timely attack mitigation and global consistency achievement, a novel hybrid consensus method is proposed which can compensate for the estimation error caused by FDI attacks and provide estimation accuracy improvement. The simulation results show that the proposed solution is effective and superior to the traditional DSE method for target tracking in the presence of FDI attacks.

scholarly journals Robust Online State of Charge Estimation of Lithium-Ion Battery Pack Based on Error Sensitivity Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ting Zhao ◽  
Jiuchun Jiang ◽  
Caiping Zhang ◽  
Kai Bai ◽  
Na Li
Keyword(s):  
Lithium Ion Battery ◽  
Estimation Error ◽  
Estimation Method ◽  
Dynamic Test ◽  
Lithium Ion ◽  
Vital Role ◽  
State Of Charge ◽  
Battery Pack ◽  
Estimation Accuracy ◽  
Soc Estimation

Accurate and reliable state of charge (SOC) estimation is a key enabling technique for large format lithium-ion battery pack due to its vital role in battery safety and effective management. This paper tries to make three contributions to existing literatures through robust algorithms. (1) Observer based SOC estimation error model is established, where the crucial parameters on SOC estimation accuracy are determined by quantitative analysis, being a basis for parameters update. (2) The estimation method for a battery pack in which the inconsistency of cells is taken into consideration is proposed, ensuring all batteries’ SOC ranging from 0 to 1, effectively avoiding the battery overcharged/overdischarged. Online estimation of the parameters is also presented in this paper. (3) The SOC estimation accuracy of the battery pack is verified using the hardware-in-loop simulation platform. The experimental results at various dynamic test conditions, temperatures, and initial SOC difference between two cells demonstrate the efficacy of the proposed method.

Research of Time Delay Estimation Based on Higher Order Statistics

2014 ◽  
Vol 530-531 ◽  
pp. 550-553 ◽  
Author(s):  
Qing Liu ◽  
Shuang Wang Han ◽  
Xiao Shu Ma
Keyword(s):  
Time Delay ◽  
Order Statistics ◽  
Estimation Method ◽  
Higher Order ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Estimation Accuracy ◽  
Higher Order Statistics ◽  
Delay Test ◽  
Bispectrum Estimation

The time delay estimation (TDE) is one of the most important techniques of characterization signal parameter. Delay parameter estimation is active in the field of signal processing. In this paper, On the basis of the basic theory method of time delay, the correlated time delay estimation, mutual-cumulants estimation and bispectrum estimation are analysed and discussed. Theoretical analysis and experimental results show that three methods is effectively used in time delay test, but higher order statistics (HOS) method can achieve the best effect. The HOS estimation method has many advantages, such as time delay estimation accuracy, high relative error small for Gaussian noise elimination ability.

scholarly journals Influence of A/D Quantization in an Interpolated DFT Based System of Power Control with A Small Delay

2014 ◽  
Vol 21 (3) ◽  
pp. 423-432 ◽  
Author(s):  
Józef Borkowski ◽  
Dariusz Kania ◽  
Janusz Mroczka
Keyword(s):  
Time Window ◽  
Frequency Estimation ◽  
Estimation Error ◽  
Estimation Method ◽  
Quantization Noise ◽  
Estimation Accuracy ◽  
Signal Frequency ◽  
Practical Applications ◽  
Dominant Component ◽  
Signal Value

Abstract Fast and accurate grid signal frequency estimation is a very important issue in the control of renewable energy systems. Important factors that influence the estimation accuracy include the A/D converter parameters in the inverter control system. This paper presents the influence of the number of A/D converter bits b, the phase shift of the grid signal relative to the time window, the width of the time window relative to the grid signal period (expressed as a cycle in range (CiR) parameter) and the number of N samples obtained in this window with the A/D converter on the developed estimation method results. An increase in the number b by 8 decreases the estimation error by approximately 256 times. The largest estimation error occurs when the signal module maximum is in the time window center (for small values of CiR) or when the signal value is zero in the time window center (for large values of CiR). In practical applications, the dominant component of the frequency estimation error is the error caused by the quantization noise, and its range is from approximately 8×10-10 to 6×10-4.

scholarly journals Effects of Precast Cladding Systems on Dynamic Characteristics of Steel Frame Buildings by Ambient and Free Vibration Tests

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
Jun Ma ◽  
Shinji Nakata ◽  
Akihito Yoshida ◽  
Yukio Tamura
Keyword(s):  
Free Vibration ◽  
Dynamic Characteristics ◽  
Steel Frame ◽  
Ambient Vibration ◽  
Test Cases ◽  
Vibration Method ◽  
External Wall ◽  
Wide Range ◽  
Cladding Panels ◽  
Vibration Tests

Full-scale tests on a one-story steel frame structure with a typical precast cladding system using ambient and free vibration methods are described in detail. The cladding system is primarily composed of ALC (Autoclaved Lightweight Concrete) external wall cladding panels, gypsum plasterboard interior linings, and window glazing systems. Ten test cases including the bare steel frame and the steel frame with addition of different parts of the precast cladding system are prepared for detailed investigations. The amplitude-dependent dynamic characteristics of the test cases including natural frequencies and damping ratios determined from the tests are presented. The effects of the ALC external wall cladding panels, the gypsum plasterboard interior linings, and the window glazing systems on the stiffness and structural damping of the steel frame are discussed in detail. The effect of the precast cladding systems on the amplitude dependency of the dynamic characteristics and the tendencies of the dynamic parameters with respect to the structural response amplitude are investigated over a wide range. Furthermore, results estimated from the ambient vibration method are compared with those from the free vibration tests to evaluate the feasibility of the ambient vibration method.

scholarly journals Low-complexity beam-domain channel estimation and power allocation in hybrid architecture massive MIMO systems

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xiao Chen ◽  
Zaichen Zhang ◽  
Liang Wu ◽  
Jian Dang
Keyword(s):  
Channel Estimation ◽  
Power Allocation ◽  
Spectral Efficiency ◽  
Communication Systems ◽  
Mimo Systems ◽  
Beam Steering ◽  
Estimation Method ◽  
Low Complexity ◽  
Estimation Accuracy ◽  
Hybrid Architecture

Abstract In this journal, we investigate the beam-domain channel estimation and power allocation in hybrid architecture massive multiple-input and multiple-output (MIMO) communication systems. First, we propose a low-complexity channel estimation method, which utilizes the beam steering vectors achieved from the direction-of-arrival (DOA) estimation and beam gains estimated by low-overhead pilots. Based on the estimated beam information, a purely analog precoding strategy is also designed. Then, the optimal power allocation among multiple beams is derived to maximize spectral efficiency. Finally, simulation results show that the proposed schemes can achieve high channel estimation accuracy and spectral efficiency.

scholarly journals Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

2009 ◽  
Vol 132 (3) ◽  
Author(s):  
Donald L. Simon ◽  
Sanjay Garg
Keyword(s):  
Kalman Filter ◽  
Estimation Error ◽  
Engine Performance ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Performance Estimation ◽  
Experimental Simulation ◽  
Tuning Parameter ◽  
Estimation Accuracy ◽  
On Line

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multivariable iterative search routine that seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared with the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy.

scholarly journals UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

2021 ◽  
Vol 13 (4) ◽  
pp. 803
Author(s):  
Lingchen Lin ◽  
Kunyong Yu ◽  
Xiong Yao ◽  
Yangbo Deng ◽  
Zhenbang Hao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Forest Canopy ◽  
Estimation Method ◽  
Vertical Direction ◽  
Estimation Accuracy ◽  
3D Point Cloud ◽  
Area Index ◽  
Better Than

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

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