Robotic Systems and Applications
Not a member yet
20223 research outputs found
Sort by
Dynamic performance analysis of 1000 MW double reheat steam turbine foundation
Get PDFIn recent years, power equipment has been developing towards low-carbon, high-efficiency, and green environmental protection. The double reheat unit has been increasingly employed in power plants due to its advantages of low energy consumption and less pollution. As a core component of power plants, the dynamic performance analysis of the steam turbine foundation is essential for ensuring the overall safety of double reheat unit. For this reason, the dynamic performance of a steam turbine foundation is investigated based on the engineering background of frame-type reinforced concrete foundations of 1000 MW double reheat steam turbine set in a power plant. The solid finite element model of the steam turbine foundation is first established by using ANSYS software, along with a detailed description of foundation information and modelling methodology. Subsequently, the dynamic characteristic and response analyses of the steam turbine foundation are performed to evaluate its dynamic performance, respectively. The results indicate that the 1000 MW steam turbine foundation demonstrates satisfactory dynamic performance. Within the operating speed range, the transverse, longitudinal, and vertical vibration displacements of the foundation bearings and columns remain below 20 μm, while the vibration velocity does not exceed 3.8 mm/s, both of which comply with relevant specifications. Moreover, enhancing the stiffness of the fifth and sixth beams, along with increasing the cross-sectional area of columns C3 and C4 on the steam turbine foundation, should be considered to mitigate its vibration responses and thus improve its dynamic performance. The research findings can serve as a reference for the type selection and optimization design of 1000 MW double reheat steam turbine foundations
Stodola-Vianello iteration method for the free flexural vibration frequencies of Shimpi’s single variable shear deformable beams
Get PDFThe natural vibration frequency analysis of beams is vital for their design against resonance failures because such failures occur when the excitation load frequencies of vibration coincide with such natural frequencies. This work presents a single variable shear deformable beam equation formulated using Shimpi’s displacement field assumptions. This results in a quadratic shear stress profile over the depth and a satisfaction of the transverse shear stress-free boundary conditions. The governing equation is obtained using a first principles consideration and equilibrium method as a partial differential equation (PDE) which is non-homogenous for forced vibrations and homogeneous for free vibrations. The study then used the Stodola-Vianello iteration method to solve the resulting homogeneous PDE for simply supported boundary conditions and harmonic response. The problem reduced to an iterative problem of algebra involving the computation of an (n+1)th vibratory modal shape function from an nth shape function that satisfies the boundary conditions. This work used a sinusoidal shape function which is exact for the simply supported boundary condition investigated. The use of boundary conditions solved the integration constants involved. Application of the convergence rule led to the eigenequation from which the eigenvalues were found. The eigenvalues were presented for the first four modes of vibration and for a rectangular beam. It was found that for l/h varying from 5 to 100, the natural vibration frequencies were identical with the ωn values obtained using Navier method for other thick beam vibration problems. It was also found that ωnwas close to the exact values for all vibration modes and for all values of l/h between 5 and 100. For all vibration modes and all considered l/h values negligible differences, were observed between the ωn obtained using SVIM and the exact values obtained by previous researchers
Effect of Si addition on phase structure and wear resistance of CoCrFeMoNi alloy coatings
Get PDFCoCrFeMoNi high entropy alloy coating was prepared on Q235 substrate by plasma cladding method. The phase structure, morphology characteristics, element distribution, microhardness, and wear resistance for this alloy without and with Si doping were investigated by XRD, OM, SEM, EDS, microhardness tester, and friction-wear tester, respectively. The results show that CoCrFeMoNi alloy is composed of a single FCC phase, while Si-containing alloy is composed of FCC main phase and HCP phase. Both alloys have a typical dendritic structure. There is a layer of isotropic fine-grained region near the fusion line, and a columnar crystal region away from the fusion line. After adding Si element, the enrichment of Mo element in the interdendrite region and Co element in the dendrite region significantly decreased, which is related to the Si-containing alloy can provide a liquid environment with longer duration, lower viscosity, and greater fluidity. The change of Cr element enrichment from interdendrite region to dendrite region is the result of comprehensive competition of mixing enthalpy, atomic radius difference, electronegativity, density, and melt flowability between alloying elements. The friction coefficients of the two alloys show a rapid increase first and then gradually stabilize with the increase of time. After adding Si element, the hardness and wear resistance of the alloy are greatly improved, which is mainly related to the increase of the lattice distortion of FCC phase, the formation of high-strength HCP phase and the reduction of internal defects
Feature data analysis of dance movements by motion capture
Get PDFMotion capture technology has been applied in more and more fields, but the research in the field of dance is relatively rare. In order to combine motion capture technology with dance research, better understand the characteristics of dance movements, and provide support for their digital analysis, this paper mainly studied the application of a motion capture technology called Kinect in the analysis of dance movement feature data. The skeleton data of different dance movements was first collected based on Kinect v2, and then the collected data was analyzed using a spatio-temporal graph convolutional network (ST-GCN). On the basis of the original ST-GCN, the multi-branch structure was adopted to realize co-occurrence feature learning, and the bone length feature and direction feature were introduced to further enrich the feature data. Experiments were carried out on the NTU RGB+D and dance datasets. It was found that the improved ST-GCN had better performance than other current motion classification approaches on the NTU RGB+D. The top-1 accuracy for cross-subject (CS) and cross-view (CV) was 92.4 % and 96.7 %, respectively, and the average accuracy of different dance movements for the dance dataset was 96.035. The findings confirm the effectiveness of the proposed approach in the analysis of dance movement feature data, and it can be applied in the actual research of dance movements
PSO-PPO-based reinforcement learning control strategy for active suspension systems under multiple operating conditions
Get PDFTo address the poor generalization capability and extended training duration of reinforcement learning (RL)-based active suspension control systems, this study proposes a PSO-PPO algorithm for multiple operating condition suspension control. The methodology initiates with establishing a 4-DOF suspension dynamic model under three characteristic driving conditions: constant-speed operation, vehicle launch, and emergency braking, which is subsequently converted into state-space representation. The novel PSO-PPO framework synergizes particle swarm optimization with proximal policy optimization to train condition-specific agents. Based on the trained optimal agents, the entropy weight method is applied to adjust the reward function weight coefficients to develop a generalized multi-condition controller. Finally, the control effectiveness of the PSO-PPO algorithm is validated through constant-speed, launch, emergency braking, and multi-condition concatenated scenarios. Simulation results demonstrate that the PSO-PPO algorithm achieves shorter training times while maintaining balanced performance in ride comfort, handling stability, and safety across all conditions
Acoustic detection of fan blade faults based on dynamic Cauchy swarm algorithm to optimize support vector machine
Get PDFFan blades operate in outdoor environments, where the detection of sound signals is susceptible to interference from background noise such as random loads, wind speed, rainwater, and other ambient noise. Therefore, this article proposes an acoustic detection method for wind turbine blade faults based on a dynamic Cauchy bee colony algorithm-optimized support vector machine. First, the signal is preprocessed using a Butterworth bandpass filter, and the full frequency band is divided into sub-bands using the octave band feature extraction method. Based on frequency domain analysis, the natural frequency offset of the blade is determined. Next, the dynamic Cauchy bee colony algorithm is applied to optimize support vector machine parameters, while moving average and bandpass filtering are used to smooth the noise power curve and extract impeller speed information. The experimental results show that the proposed method converges in fitness value after 22 iterations, with a detection time of only 6.8 seconds and small fluctuations in impeller speed amplitude. In terms of classification performance, the accuracy of detecting normal samples is 0.95, the recall rate is 0.96, and the F1 score is 0.95. The method demonstrates high prediction accuracy and stability for various types of fault samples and can be reliably applied to the acoustic detection of wind turbine blade faults
Small sample fault diagnosis method based on dual convolutional kernel feature fusion and channel attention weighted temporal convolutional network (DCK-CAM-TCN)
Get PDFIn actual industrial environments, equipment failures often occur sporadically during operation, resulting in insufficient labeled data for training. To address the issues of difficult feature extraction and poor generalization caused by insufficient data in small-sample fault diagnosis, a small sample fault diagnosis method based on dual convolutional kernel feature fusion and channel attention weighted temporal convolutional network (DCK-CAM-TCN) is proposed. Firstly, dual convolution kernels are employed to extract signal features, with the large kernel capturing low-frequency components and the small kernel extracting additional features to enhance the network's expressiveness. Secondly, the channel attention mechanism adaptively adjusts the feature responses of each channel, enabling the network to focus on the most informative and relevant features while suppressing unimportant ones. Finally, the Temporal Convolutional Network (TCN) is utilized to capture dependency features within long time series, further improving the model's ability to process sequential data. Experimental results demonstrate that the DCK-CAM-TCN model significantly outperforms traditional Convolutional Neural Networks (CNNs) and other comparison models in small-sample scenarios. The results indicate the significant advantages of the DCK-CAM-TCN model in small-sample fault diagnosis
Complex fault diagnosis in wind turbine bearings: a hybrid approach combining the improved feature mode decomposition and convolutional neural networks
Get PDFThe complex noise interference and diverse fault-induced signals in vibration data from wind turbine equipment pose significant challenges for bearing fault diagnosis, including cumbersome methodologies, prolonged processing times, and compromised accuracy. To address these limitations, this study proposes a novel composite fault diagnosis framework that integrates Feature Mode Decomposition (FMD), Fast Spectral Kurtosis (FSK), and Convolutional Neural Network (CNN). While conventional Empirical Mode Decomposition (EMD) exhibits limited noise robustness and struggles to extract subtle fault signatures in composite failure scenarios, our approach employs FMD to decompose fault-related intrinsic mode functions (IMFs)and further filters the IMF components using fast spectral cliffs with enhanced feature separability. Subsequently, the Short-Time Fourier Transform (STFT) is applied to derive time-frequency representations, followed by Fast Spectral Kurtosis analysis to identify optimal demodulation bands for non-stationary signals. The energy spectrum of denoised signals is converted into grayscale images, serving as input to a tailored CNN architecture for hierarchical feature learning. Experimental validation demonstrates that this hybrid methodology achieves a fault recognition accuracy of 98 % under compound fault conditions, outperforming conventional EMD-based approaches in terms of noise immunity and diagnostic precision. Comparative analysis reveals an 8 % improvement in detection reliability over standalone deep learning models, particularly in low signal-to-noise ratio (SNR) environments. The proposed framework offers a robust solution for multi-fault identification in industrial Bearing machinery, demonstrating superior generalization capability across varying operational conditions
Study on the compaction and dynamic properties of loess enhanced by waste tyre rubber particles
Get PDFThis study investigates the compaction and dynamic properties of rubber particle-loess from Inner Mongolia through laboratory tests, including compaction tests and dynamic triaxial tests. Four rubber particle sizes (10 mesh, 20 mesh, 40 mesh, and 100 mesh) and four contents (5 %, 10 %, 15 %, and 20 % by volume) were tested under varying conditions: confining pressures of 50 kPa, 100 kPa, and 200 kPa, and freeze-thaw cycles of 0, 1, 3, 6, and 9. The tests aimed to simulate environmental conditions relevant to infrastructure in Inner Mongolia's loess regions. Results revel that adding 5 % 40-mesh rubber particles maximized dynamic shear modulus, damping ratio, and compactness. The dynamic shear modulus exhibited strain-softening behavior, which decreased with increasing dynamic strain, rubber content, and freeze-thaw cycles, but increased with confining pressure. The damping ratio showed a non-linear relationship with moisture content, showing a minimum at optimum moisture and increasing with freeze-thaw cycles while decreasing with confining pressure. Notably, the damping ratio of rubber particle-loess consistently exceeded that of plain soil. These results highlight the potential of waste tire rubber particles as an eco-friendly material to enhance loess engineering properties, particularly in cold regions with significant freeze-thaw effects. The study provides a theoretical basis for improving loess stability and seismic performance in geotechnical applications
Identification and analysis of pavement structure features based on vibration behavior parameters
Get PDFTo clarify the correlation between the service performance of asphalt pavement structures and their vibration behavior parameters, this study focuses on asphalt pavement structures as the primary research subject. A quarter-vehicle two-degree-of-freedom model of a standard vehicle was selected as the simplified vehicle dynamics model, while a semi-rigid asphalt pavement was adopted as the simplified pavement model. Based on the elastic layered system theory, a three-dimensional finite element model of the asphalt pavement was constructed by using the software of Abaqus. The effects of modulus variations in asphalt pavement structural layers on modal frequencies were analyzed. The impacts of coupled working conditions, such as structural layer cracking positions and interlayer failure, on the modal frequencies of asphalt pavement were investigated. Additionally, the attenuation process of dynamic responses in asphalt pavement structures under transient impact loads was examined. Building on this, the dynamic response behaviors of asphalt pavement structures under working conditions including structural layer cracking and interlayer failure were studied. The results demonstrate that as the vertical depth of the asphalt pavement structure increases, the modulus attenuation of structural layers significantly affects the overall modal frequencies and vibrational effects. When internal cracking and interlayer failure coexist in the asphalt pavement structure, the vibration acceleration characteristics under load align more closely with those of interlayer failure, while the vibration displacement exhibits greater magnitudes