Robotic Systems and Applications
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Determination of optimal drive parameters for high-speed linear systems
Get PDFThe problem of optimizing the drive design parameters for a high-speed linear system is solved based on minimizing the inertial torque. New analytical expressions are obtained for determining the optimal gear ratio of the intermediate transmission, taking into account the moments of inertia of rotating masses, the carriage mass, and the screw pitch. An optimization problem is proposed to determine the number of gear teeth and the screw pitch by minimizing a function that includes the relative error between the actual and calculated gear ratio, as well as the total number of teeth required to ensure the specified travel speed of a carriage. At the next calculation stage, the number of gear teeth is refined based on the nearest standard screw pitch values. The resulting parameters are evaluated using a transient dynamic analysis according to key kinematic and energy characteristics
Estimation of vehicle state based on maximum correntropy square-root cubature Kalman Filter
Get PDFState estimation of a vehicle is an important direction under the research branch of automotive dynamics, with the aim of determining state variables that reflect vehicle handling stability and other characteristics. In order to solve the problem of poor estimation accuracy caused by heavy tailed non Gaussian noise in traditional state estimation methods, a new filtering algorithm based on the Maximum Correlation Entropy criterion (MCC) and the Square-root Cubature Kalman Filter (MCSCKF) is proposed. On the basis of establishing a nonlinear 3-DOF vehicle model, the yaw rate and the side slip angle as well as the longitudinal velocity of the vehicle were estimated. And the effectiveness of the algorithm was verified through joint simulation with Carsim and Matlab/Simulink. The results show that the MCSCKF algorithm can adapt to complex working conditions and has better accuracy in vehicle state estimation than traditional state estimation algorithms. Meanwhile, the MCSCKF algorithm can effectively reduce the impact of heavy tail non Gaussian noise and improve the accuracy of vehicle state estimation
Denoising for ECG signals based on VMD and RLS
Get PDFElectrocardiogram (ECG) signals often encounter various types of noise interference, which annihilates their waveform characteristics and exhibits strong instability. To facilitate clinical diagnosis and analysis, it is necessary to perform denoising processing in advance. A denoising method for ECG signals based on variational mode decomposition (VMD) and recursive least square (RLS) has been proposed. VMD was used for the modal decomposition of noisy ECG signals, and the recursive least square (RLS) algorithm was used for adaptive filtering of various intrinsic mode functions (IMFs) components. The problem construction, solution, and decomposition characteristics of VMD were analyzed. The IMFs filtered by RLS were reconstructed. This achieved the elimination of interference noise in the ECG signal. The Sym8 wavelet basis, LMS, NLMS, RLS, and VMD-RLS denoising method were compared by using ECG signals including Gaussian white noise, baseband drift, electrode motion, electromyographic interference, and electrical interference noise. The experimental results showed that the VMD-RLS denoising method has significantly better denoising performance than the other four methods, achieving better values in the quantitative evaluation indicators. This algorithm improved convergence speed and signal estimation accuracy, and it has good effectiveness, superiority, and practicality. Therefore, the VMD-RLS denoising method can enable doctors and researchers to analyze and diagnose ECG signals of heart diseases more accurately
Improved CEEMD-based correction method for low-frequency shock response spectrum in large dual-wave shock tester devices
Get PDFThe shock response spectrum (SRS), calculated from a shock acceleration signal, is a critical indicator of shock environments. However, under intense loads, acceleration sensors are prone to trend term errors that can cause significant drift in the low-frequency spectral lines of large dual-wave shock tester devices. To address this issue, the complementary ensemble empirical mode decomposition (CEEMD) method was employed to decompose acceleration signals and restore the actual shock environment. Intrinsic mode functions (IMFs) were cross-correlated and compared to a predefined threshold to identify the effective IMF components required to reconstruct the signal. K-means clustering was employed to further validate the effectiveness of the IMFs for enhanced selection accuracy. Finally, the reconstructed acceleration signal was used to calculate a corrected SRS. The proposed approach demonstrated significant improvements over the traditional CEEMD algorithm. The corrected SRS exhibits a 5.6316 dB/oct slope in the low-frequency band, reflecting an equal displacement trend. The maximum error at the corresponding frequency was less than 6 % in comparison to the relative displacement response measured by low-frequency spring oscillators. This improved CEEMD correction method can effectively restore the actual shock environment of a dual-wave shock tester device, offering a valuable reference for evaluating shock resistance in onboard equipment
Experimental analysis of running wheel for a straddle monorail vehicle
Get PDFThis article conducts in-depth research on the force analysis of the test running wheel of a certain type of straddle monorail vehicle, based on the tire six-component force test and wheel dynamic stress test. The main research objective is to accurately identify the factors affecting the wheel strength, thereby providing a solid foundation for subsequent design optimization and safety enhancement. The research commences with a meticulous calibration of the vehicle connecting rod in the laboratory, aiming to acquire the “force-strain” coefficients under both tension and compression conditions. A novel approach lies in the verification of calibration accuracy through a detailed comparison with experimental results, ensuring the reliability of subsequent data acquisition. By strategically installing displacement sensors at various positions to measure the vehicle's dynamic displacement and detecting the strain of the connecting rod, the study innovatively calculates the six-component force data of the tire, which provides a comprehensive data basis for analyzing the forces acting on the wheel hub. Then evaluating the fatigue strength of the wheel hub under AW0 and AW3 operating conditions based on the IIW standard, the research uncovers unique findings. It is revealed that, although the maximum dynamic loads of the vertical force of the running wheel, the lateral force of the guide wheel, and the lateral force of the stabilizing wheel are within the limit load range with a certain safety margin, there are 1 point and 3 points on the wheel hub under AW0 and AW3 working conditions, respectively, that fail to meet the fatigue strength criterion requirements. The maximum equivalent force amplitude at Measurement Point 3 of the inner hub reaches 51.4 MPa, while the calculated service mileage is only 31,000 kilometers. This discovery is of great significance as it precisely pinpoints the weak points of the wheel hub, which is a major contribution to the field. Moreover, during the analysis of the wheel hub's dynamic stress during emergency braking and the influence of polygonal wear on it, the research confirms that there is no abnormal change in the wheel hub’s dynamic stress during emergency braking, and the polygonal wear of the tire shoulder has a negligible impact on the wheel hub’s dynamic stress. These results not only calculate the six-component force data of the tire but also break new ground in understanding the interaction between different factors and the wheel hub’s performance
Dynamic detection and evaluation of wheel flats in heavy-haul railway wheelsets using wayside monitoring systems
Get PDFIn recent years, heavy-haul railways have become a critical direction for freight transport in China, with wheel flats in wheelsets posing significant threats to operational safety and infrastructure integrity. Traditional detection methods (e.g., manual inspection, TPDS) suffer from low efficiency or limited accuracy in characterizing flat features. To address this, this study develops a rigid-flexible coupling dynamic model for C80 wagons with K6 bogies, uniquely integrated with field data from the Truck Operation Detection System (TODS) to bridge simulation and engineering application gaps. Focusing on wheel-rail force responses under wheel flat conditions, we establish a quantitative mapping relationship between flat length, vehicle speed, and impact force through polynomial fitting of simulation data (10-80 km/h for empty/loaded vehicles). To validate feasibility, a 56-channel wayside monitoring system (TODS) is installed on a heavy-haul railway, calibrated via hydraulic loading to ensure measurement accuracy. Field tests (80,541 vehicles monitored) confirm that TODS can infer flat length from detected impact forces, with results consistent with TPDS alarms but offering finer characterization of flat dimensions. This work provides a practical solution for real-time wheel flat detection, enhancing maintenance efficiency and safety in heavy-haul operations
Analysis of the natural characteristics of fiber-reinforced cantilever beams using 8-node solid elements
Get PDFA combined theoretical and experimental approach is employed to investigate the dynamic characteristics of fiber-reinforced cantilever beams. An 8-node element method establishes the theoretical model of the cantilever beam, allowing for the determination of its dynamic properties. A relevant experimental platform is constructed to test the fiber-reinforced cantilever beams, thereby validating the accuracy of the theoretical model. The results indicate that the theoretical model accurately predicts the dynamic characteristics of fiber-reinforced cantilever beams. Finally, based on the established theoretical model, the effects of cantilever beam length, width, and elastic modulus on the dynamic characteristics of the cantilever beam are discussed
Improving the quality of cast blanks by applying force to the solidifying metal
Get PDFIn the course of the research, the authors studied the formation of structures of cast blanks on various alloys, depending on the conditions of metal crystallization, including under the influence of vibration. An analysis of the structures of the control samples (blanks) confirmed that solidification under normal conditions occurs mainly according to the sequential crystallization scheme, as evidenced by the width of the structural zones in them. The external vibration effect on solidifying alloys leads to a significant change in the conditions of their crystallization, in particular, to a significant grinding of the macrostructure of the workpieces and a change in the size of the structural zones, which indicates a volume-sequential scheme of their crystallization. It is established that vibration increases the physico-mechanical properties of cast metal and significantly reduces their anisotropy over the section of the workpieces
Data analytics-based model for optimizing cationic retarder and acetic acid in polyacrylic yarn dyeing
Get PDFThe study aims to develop a model for optimizing the concentration of solution parameters in the dyeing process of polyacrylic yarns. Specifically, the study examines the use of cationic retaiders and acetic acid, which affect the wavelength as an indicator of yarn color aging. By optimizing these parameters, the objective is to improve the color stability and longevity of dyed polyacrylic yarns. The application of Response Surface Methodology (RSM) encompassed two distinct types of data distribution properties: linear and non-linear. The R-squared (R2) value for the non-linear RSM model was 0.96, compared to 0.86 for the linear RSM model. These results indicate that the model formed based on the non-linear RSM offers superior predictive ability in optimizing solution concentration as a parameter in the polyacrylic yarn dyeing process compared to the linear RSM-based model. In addition to providing practical implications for textile practitioners, this study contributes theoretically by emphasizing the effectiveness of statistical methods such as RSM in manufacturing process analysis
Structural damage detection by progressive continuous wavelet transform and singular value decomposition of noisy mode shapes
Get PDFFor decades, damage identification based on structural mode shapes has been a popular research topic. While mode shapes provide valuable spatial structural information, the sensitivity to localized damage remains limited. In contrast, modal curvature exhibits high sensitivity to local damage, enabling precise damage localization. However, its susceptibility to environmental noise poses a significant limitation. To this end, a novel damage identification method is proposed by integrating continuous wavelet transform (CWT) and singular value decomposition (SVD). First, the CWT is applied to structural mode shapes for generating continuous wavelet coefficients. Subsequently, the SVD is performed on these coefficients, yielding new damage indicator termed as the singular image of continuous wavelet coefficients (SICWC). The SICWC enhances damage sensitivity and localization accuracy by suppressing noise-induced global trends in structural mode shapes. The effectiveness of proposed method is validated through numerical simulations of a cantilever beam under noisy conditions, as well as experimental detection of a cracked beam using mode shapes acquired via a scanning laser vibrometer. The results demonstrate that SICWC effectively mitigates the limitations of traditional damage detection methods based on mode shape and curvature