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SSRN 2022年

作者： Hou, Xuyan Zhang, Li Gao, Guowei Liu, Yuhui Na, Zhonglai Xu, QingZhang Ding, Tianxiang Li, Long Chen, Tao Research Center of Aerospace Mechanism and Control School of Mechatronics Engineering Harbin Institute of Technology Harbin150080 China Department of MECHANICAL ENGINEERING FACULTY OF ENGINEERING Hong Kong Polytechnic University Hong Kong Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China Jiangsu Provincial Key Laboratory of Advanced Robotics School of Mechanical and Electric Engineering Soochow University Suzhou215123 China

With the rapid development of space crawling robotics technology, tactile perception, a significant source for the robot to sense the external environment, has become the preferred solution to gather information in space. Sensors embedded in the robotic end-effector unit can collect and encode the large tactile data, allowing the robot to feel and perceive the real surroundings. Therefore, a space crawling robotic bio-paw (SCRBP) inspired by a cat paw, which performs as a compliant device and can flat the impulse force during the robot’s contact process to the target surface, is proposed in this paper. Meanwhile, a touch-sensing system embedded on SCRBP with self-powered sensors based on the triboelectric nanogenerator (TENG) technology is proposed which can provide the multi-dimensional sensation information in real time. By combining with machine learning (ML), the sensory system can be used for surface identification from the footfall process to the robot’s controller hub. In conclusion, SCRBP device proposed in this paper has obvious advantages in surface information acquisition, space adaptability, power consumption, cost, reliable signal and minimized data. Accordingly, SCRBP system shows fabulous potential in space robotics. © 2022, The Authors. All rights reserved.

关键词： End effectors

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The Selection of Optimal Mode Pair of Nonlinear Guided Waves in Waveguide of Arbitrary Cross-section

The Selection of Optimal Mode Pair of Nonlinear Guided Waves...

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Far East Forum on Nondestructive Evaluation/Testing: New technology & Application (FENDT)

作者： Xiao-Chuan Niu Li-Qiang Zhu Zu-Jun Yu Key Laboratory of Vehicle Advanced Manufacturing Measuring and Control Technology Ministry of Education Beijing Jiaotong University Beijing China

ISBN: (数字)9781665440820

ISBN: (纸本)9781665440837

The existing researches have indicated the conditions that mode pair of fundamental mode and second harmonic mode need to satisfy to produce the cumulative second harmonic. However, due to the multimodal characteristics of ultrasonic guided waves, the conditions are very complex. In this article, a numerical simulation method is proposed to select the optimal mode pair that can generate the cumulative second harmonic mode with the largest amplitude in waveguide of arbitrary cross-section. The optimal mode pair is determined by calculating and comparing the amplitude coefficients of different mode pairs based on mode shape analysis. The CHN60 rail is taken as an example for simulation verification, and the optimal mode pair of nonlinear guided waves in rail is selected.

关键词： Rails Shape Harmonic analysis Numerical simulation Numerical models Finite element analysis Standards

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Prognosis of Rotor Parts Fly-Off Based on Rough-Fine Scale

Prognosis of Rotor Parts Fly-Off Based on Rough-Fine Scale

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Prognostics and System Health Management Conference (PHM)

作者： Jin Si Hongmei Shi Jinsong Yang Key Laboratory of Vehicle Advanced Manufacturing Measuring and Control Technology Ministry of Education Beijing Jiaotong University Beijing China

ISBN: (数字)9781728156750

ISBN: (纸本)9781728156767

Rotor parts fly-off of large rotating machines could lead to high maintenance costs and accidents with disastrous consequences. The earlier fault features extraction and prognosis of unit breakdown are relatively difficult and has become a worldwide challenge. A rough-fine-scale prognosis (RFSP) model of compressors and steam-turbines fault is proposed in this paper. Firstly, a roughly classifying method for the health stage of rotating units based on Gradient Boosting Decision Tree is performed. The Peak-Difference-Vectors of multi-sensor vibration signals in time and frequency domain are extracted as fault features. Secondly, for the failure development processes, which signals have similar features, the fault severity index is built to quantify the trend of failure and distinguish different stages on fine-scale. The validity of the proposed method is verified with 5 processes for each of 9 large rotating units in real industrial scenarios. The best model achieves 99.5% accuracy for rough classifying health stages. Besides, the fine-scale prognosis achieves 100% accuracy for the determination of malfunctioning machinery and the division of health stages. This work highlights the promise of capturing the early symptom of rotor faults and predicting the failure of the rotor component in terms of few fault cases, changeable working conditions and interference of other unknown faults.

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Optimization of Wheel Maintenance Strategy for Railway Freight Train Based on Remaining Useful Life Prediction

Optimization of Wheel Maintenance Strategy for Railway Freig...

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Prognostics and System Health Management Conference (PHM)

作者： Hongmei Shi Jinsong Yang Jin Si Key Laboratory of Vehicle Advanced Manufacturing Measuring and Control Technology Ministry of Education Beijing Jiaotong University Beijing China

ISBN: (数字)9781728156750

ISBN: (纸本)9781728156767

Wheels are essential parts of railway freight cars, thus scheduling reasonable maintenance time can improve transportation efficiency and reduce maintenance cost, while ensuring operation safety. In this paper, an opportunity-centralized maintenance strategy is presented based on Remaining Useful Life (RUL) prediction. First, a degradation model of wheel tread wear is constructed with non-linear Wiener process, while model parameters are estimated using Gibbs sampling algorithm and wheel degradation data. With the updated model, real-time RUL of wheels are predicted. Next, an opportunity-centralized maintenance strategy based on the different failure time of all the wheels is proposed in order to make a reasonable maintenance schedule, which is difficult to set due to the significantly varying individual degradation processes on different wheels. The decision of maintenance strategy is transformed into a multi-objective optimization problem, in which maintenance times and cost are the optimization objectives. A cost function reflecting the wheel operation cost is constructed, while the average cost per time unit is defined as an optimization objective. Furthermore, NSGA-II algorithm is employed to solve the optimization problem. And a semi-randomized method is proposed to increase the diversity of the initial population. Finally, the effectiveness of the presented algorithm is verified using 500-days on-line monitoring data of wheel tread wear in a 54 fixed group of railway freight cars. The proposed method can be applied to maintenance strategy formulation of any equipment with many similar parts.

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Online Non-Contact Weighing Duck Carcasses Using 2d Images and Deep Learning

SSRN

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SSRN 2022年

作者： Zhao, Yuliang Tian, Kaixuan Chen, Ruoyu Yang, Yongliang School of Control Engineering Northeastern University Hebei Qinhuangdao066001 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China

In the poultry meat processing industry, non-contact weighing methods based on computer vision have become increasingly popular due to their low costs and high efficiency. However, the accuracy of the traditional 2D image weighing techniques is reduced by the overlap and sway of carcasses. In this paper, we propose a more accurate regression model, Vggx, to weigh duck carcasses through image processing and deep learning. We adopt an image segmentation technique to obtain 2D images of a single duck section under a complex background. The images of the same duck are fused together to estimate its 3D information. Then, we use a convolutional neural network regression model to estimate the weight of duck carcass with the acquired 3D information. The Mean Absolute Percentage Error of our model is 1.46% and the average weight error of our model is 23.1 gram. Experimental results demonstrate that our method is high performance. © 2022, The Authors. All rights reserved.

关键词： Image segmentation

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Lean evaluation system of manufacturing enterprises based on full lifecycle

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International Journal of Wireless and Mobile Computing 2019年第3期17卷 285-292页

作者： Yang, Le Jiang, Guozhang Li, Zhongyuan Li, Gongfa Xu, Chao Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China 3D Printing and Intelligent Manufacturing Engineering Institute Wuhan University of Science and Technology Wuhan430081 China Research Centre of Biologic Manipulator and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China

The production of manufacturing enterprises is a complex process. Lean production is hard to achieve lean weight reduction. It is very important to propose a lean life assessment framework. This paper evaluates the lean production level of the whole life cycle of the company by using the fuzzy theory and the neural network technology, establishes a scientific and systematic framework of the lean evaluation index of the whole life cycle, and establishes the evaluation model of the fuzzy neural network. The example analysis shows that the actual output value of the fuzzy neural network model is not much different from the predicted output value, which indicates that the model has high-prediction accuracy. The experimental results further verify the reliability and validity of the model. The system framework can well plan and evaluate the whole lean production level. Accurate assessment of lean production level is an effective way to improve lean levels. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Fuzzy neural networks

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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Printability and properties of PSNZT piezoelectric ceramics fabricated by DLP additive manufacturing

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Ceramics International 2025年

作者： Yu, Mengyuan Fan, Guifen Li, Yingkang Ling, Xile Gong, Feng Liu, Gang Bai, Jiaming Li, Mantian Zhou, Peng Zhang, Dongyang Sun, Lining Hubei Key Laboratory of Plasma Chemistry and Advanced Materials School of Materials Science and Engineering Wuhan Institute of Technology Wuhan430205 China Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic University Shenzhen518055 China MOE Huazhong University of Science and Technology Wuhan430074 China Department of Mechanical and Electronic Engineering College of Mechatronics and Control Engineering Shenzhen University Shenzhen518055 China Shenzhen Blueswin Industrial Co. Ltd Shenzhen518117 China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China Institute of Critical Materials for Integrated Circuits Shenzhen Polytechnic University Shenzhen518055 China Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Suzhou215021 China

Achieving the synergy of low viscosity, high printing precision and enhanced curing ability for lead zirconate titanate (PZT) components is a critical issue in the field of digital light processing (DLP). Herein, the effect of calcination temperature on the morphology and grain size of PSNZT [(Pb0.92Sr0.08)(Zr0.533Ti0.443Nb0.024)O3]ceramic powder has been investigated, and a lowest viscosity, higher printing precision and enhanced curing ability of PSNZT ceramic slurry has been achieved, resulting in a high piezoelectric constant (d33 = 379pC/N) and a high dielectric constant (Εr = 1308@100 KHz) via DLP process combined with following sintering densification (1220 °C/3 h). © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Piezoelectric ceramics

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Forming mechanisms based simulation and prediction of grinding surface roughness for abrasive belt rail grinding

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Procedia CIRP 2020年 87卷 503-508页

作者： Chaoyue Zhao Jianyong Li Wenxi Wang School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing 100044 China Key Laboratory of Vehicle Advanced Manufacturing Measuring and Control Technology Ministry of Education State Key Laboratory of Mechanical Transmissions Chongqing University Chongqing 400044 China

Abrasive belt rail grinding (ABRG) was recently applied to repair the contour of the rail and eliminate the surface damage of the rail. Besides, it is desirable to not only suppress the occurrence of new damage but also to improve driving comfort and reduce wheel-rail wear. The surface roughness of the rail directly affects the contact noise, vibration, friction, and wear of the wheel-rail. However, due to the complicated contact state in belt grinding, the surface roughness forming mechanisms are still unclear. Therefore, it is necessary to study the influence of ABRG on surface roughness and to obtain a prediction method for roughness. In this paper, the initial morphology of the abrasive belt was created by using 2D digital filtering technology. The discretization method was used to simplify the entire belt grinding process into several local surface grinding processes carrying different contact pressure. The grinding depth of the abrasive particles on the local abrasive belt at various times was calculated, and the envelope of the cutting edge was induced according to the grinding depth of the effective abrasive grits. The roughness simulation of ABRG based on the forming mechanism was realized, and the 2D roughness of the processed surface was investigated. The good consistency between simulation and experimental results successfully proved the validity of the proposed method, and they both show that the contact force has a significant positive correlation to both Ra and Rsm .

关键词： Abrasive belt grinding Roughness profile simulation Grinding Mechanism

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Short Interval Solar Power Prediction for Energy Harvesting with Low Computation Cost on Edge Computation Network

Short Interval Solar Power Prediction for Energy Harvesting ...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Yue Chen Xing Cheng Yanxin Yao Bei Wan School of Information and Communication Engineering Beijing Information Science and Technology University Beijng China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science & Technology University Advanced Equipment Intelligent Perception and Control Beijing International Cooperation Base for Science and Technology Beijing Information Science & Technology University

ISBN: (数字)9781665414906

ISBN: (纸本)9781665430586

Solar energy forecasting methods including artificial neural network (ANN) are mostly for planning large or medium scale grid connected photovoltaic (PV) systems. The small prediction step will cause expensive computational cost with ANN. Using the concept of edge computing (EC), the paper proposes an effective short time step PV energy predicting model I based on ANN and wavelet transformation to reduce the computation costs. Sparse coefficients, which are tried by both different wavelet types and similar day classification, and a few forecast meteorological data are used as inputs to reduces the scale of ANN sharply. The simulation results demonstrate that, when the proposed method processes the whole year data, the proposed ANN model I has low computation complexity and superior precision.

关键词： Training Photovoltaic systems Computational modeling Artificial neural networks Solar energy Predictive models Wavelet coefficients

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