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Investigation on the Effect of the Tension Degree of Tracks on the Adsorption Performance of Tracked Wall Climbing Robot

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Journal of Harbin Institute of Technology(New Series) 2023年第1期30卷 52-60页

作者： Shun Xiao Guangping He Jiejian Di Jie Zhang Zhonghai Zhang Lin Zhou Department of Mechanical and Electrical Engineering North China University of TechnologyBeijing 100144China Beijing Aerospace Measurement&Control Technology Co. Ltd.Beijing 100041China

In this paper,a new tracked wall climbing robot with permanent magnetic units is designed,and the tension degree of robot’s tracks is found to have a significant impact on the robot’s adsorption *** tracked wall climbing robot is a remotely controlled *** the control devices can be installed on the robot *** the permanent magnetic units are arranged on the light *** order to illustrate the relationship between the tension degree and the adsorption performance,when absorbed on the vertical surface and 180⁃degree inverted surface,the static force analysis of the robot is ***,experiments were demonstrated to prove that higher tension degree of tracks can make adsorption performance better.

关键词： permanent magnetic adsorption track tension degree wall climbing robot

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A Prognosis Swin Transformer Network for Rolling Bearing Remaining Useful Life Prediction

A Prognosis Swin Transformer Network for Rolling Bearing Rem...

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2023 International Conference on Sensing, measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2023

作者： Sun, Yuyan Hu, Aijun Liu, Suixian Wang, Liyong Xiang, Ling North China Electric Power University Department of Mechanical Engineering Baoding China Beijing Information Science and Technology University Key Laboratory of Modern Measurement and Control Technology Beijing China

ISBN: (纸本)9798350318012

The research on the remaining useful life (RUL) of rolling bearings based on deep learning plays an important role in the safe and economic operation of rotating machinery. In order to improve the matching degree of network structure and vibration signal data, a novel method named Prognosis Swin Transformer (ProgSwT) is developed for bearing RUL prediction. In the ProgSwT network, the hierarchical structure and sliding window feature extraction are retained, and the number of stages in the Swin Transformer and the number of network layers per level are optimized. Specifically, the input signal first obtains features through the Patch Partition, then transmits into three consecutive stages composed of Linear Embedding and Swin Transformer Block, and finally enters the overall average pooling layer to output the RUL result. As a result, the flexibility and portability of the network structure can be heightened while the computational complexity of the network can be decreased when extracting the characteristics of vibration signals. The experimental results show that the ProgSwT network has good prediction accuracy in the RUL prediction. © 2023 IEEE.

关键词： Forecasting

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Modeling Axial Compressor Systems Using Deep Learning Methods

Modeling Axial Compressor Systems Using Deep Learning Method...

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Intermountain engineering, Technology and Computing (IETC)

作者： Kellie Wilson Marco P. Schoen Department of Mechanical Engineering Measurement and Control Engineering Research Center Idaho State University Pocatello

Studying the characteristics of complex nonlinear systems and designing control strategies necessitates the development of computationally efficient methods. In particular, compressor systems will benefit from accurate models that capture the nonlinear dynamics. In this paper, an overview of neural networks is given with a focus on Long-Short Term Memory (LSTM) networks for dynamic systems. A simple test system is presented for verifying the LSTM approach for single input single output (SISO) systems. An axial compressor system is used to simulate its nonlinear dynamics using the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS). The proposed LSTM approach is demonstrated for the multiple input multiple output (MIMO) axial compressor model. The proposed approach indicates that LSTM based modeling for highly nonlinear MIMO systems can yield accurate model characterizations.

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Numerical modeling of droplets injection in the secondary flow of the wet steam ejector in the refrigeration cycle

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INTERNATIONAL JOURNAL OF REFRIGERATION 2022年 136卷 103-113页

作者： Dadpour, Daryoush Lakzian, Esmail Gholizadeh, Mohammad Ding, Hongbing Han, Xu [a]Center of Computational Energy Department of Mechanical Engineering Hakim Sabzevari University Sabzevar Iran [b]Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin 300072 China [c]Key Lab of Condition Monitoring and Control for Power Plant Equipment North China Electric Power University Ministry of Education Baoding 071003 China

Steam ejector plays a key role in the refrigeration system. The water has been used as a working fluid because water is more economical and non-toxic. The operating fluid often enters the evaporator as a water liquid and droplets evaporate by absorbing heat. Cooling load changing causes the vapor in the outlet of the evaporator to be accompanied by droplets. Therefore, there are droplets at the inlet of secondary nozzle and flow should be simulated as a wet steam flow with injection droplets at the secondary flow. In this paper, the effects of droplets injection at secondary flow have been investigated on the performance of the refrigeration cycle. For this purpose, the Eulerian-Eulerian method has been used and the results have been validated with experimental data. Six cases are considered with different wetness and number of droplets in the secondary flow. The results of case 6 show COP, T-outlet, and ER decrease about 22.93%, 15.66%, and 22.93%, respectively. In addition, the steam accompanies by droplets at the outlet of ejector. Therefore, it is essential for designers and operators to model and consider the effects of the droplets on the wall erosion to optimize the wet steam ejector and refrigeration cycle performance.

关键词： Wet steam ejector Refrigeration cycle Droplets injection COP ER

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Innovative Automatic Tool Changing Mechanism for Robotic Arms

Innovative Automatic Tool Changing Mechanism for Robotic Arm...

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Intermountain engineering, Technology and Computing (IETC)

作者： Shaibal Das Matthew Berry Taher Deemyad Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Department of Mechanical and Measurement & Control Engineering Idaho State University Pocatello ID USA

ISBN: (数字)9798350373073

ISBN: (纸本)9798350373080

This paper presents the development and implementation of an automatic tool-changing mechanism for industrial and collaborative robotic arms. The primary objective is to enable integration with a variety of end effectors, both active and passive, allowing the robot to autonomously switch tools without manual intervention. The system comprises three key components: a universal end-effector connector, various tools, and a customized toolbox. The universal connector will be screwed to the end of the robotic arm. It features a mechanical lock mechanism and accommodates electrical and pneumatic inlets for diverse tools. A dedicated toolbox, situated within the robot's workspace range, houses all tools for easy access. The study focuses on the design, 3D printing, and testing of two tools - an active and a passive tool - along with the innovative mechanical lock mechanism, which has been successfully developed and tested. Additionally, two industrial tasks have been defined to demonstrate the mechanism's functionality in real-world scenarios, emphasizing non-singular path trajectories between tasks. Future work includes the integration of additional sensors for enhanced automatic robot control. Overall, this research will contribute to the advancement of fully autonomous robotic systems in industrial settings.

关键词： Connectors Program processors Service robots Force Pneumatic systems Robot sensing systems End effectors

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Data Augmentation Integrated with Feature-Enhanced Convolutional Neural Network for Imbalanced Fault Diagnosis in Rolling Bearings 13

Data Augmentation Integrated with Feature-Enhanced Convoluti...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Wang, Li Qu, Qilin Wang, Yanwei Wong, David Shan-Hill Zheng, Ying School of Artificial Intelligence and Automation Huazhong University of Science and Technology China -Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China School of Mechanical & Electrical Engineering Wuhan Institute of Technology 693 Chuxiong Road Wuhan430073 China National Tsing-Hua University Department of Chemical Engineering Hsinchu30071 Taiwan

ISBN: (纸本)9798350361674

Imbalance in fault samples is a key problem with most rolling bearing datasets in real industrial processes, limiting the performance of fault detection. In addition, existing fault diagnosis methods for rolling bearings do not adopt a modular division between the initial layer and the classification layer. This lack of modularization inhibits the full activation of the network's feature extraction capabilities and impedes subsequent model improvements. In this paper, a novel fault diagnosis method based on data augmentation integrated with a feature-enhanced convolutional neural network (DA-FECNN) is proposed. First, Variational Autoencoder-Generative Adversarial Network (VAE-GAN) is utilized to expand the number of fault samples and improve the diversity of few-shot. Second, a uniform feature-enhanced structure is designed with a feature extraction module, a multi-scale classification module, and a reconstruction module. The network's ability on feature extraction is improved by focusing on high quality features. Thereby, accurate fault classification is realized simultaneously. Experiments on real world data show the effectiveness of the proposed method on fault diagnosis task. © 2024 IEEE.

关键词： Convolutional neural networks

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CFD Investigation Comparing Mixing and Displacement Ventilation Systems for Contaminant Dispersion in a Fitting Room 5th

CFD Investigation Comparing Mixing and Displacement Ventilat...

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5th International Conference on Building Energy and Environment, COBEE 2022

作者： Khan, Mohammed Abdul Hameed Bennia, Abderazak Lateb, Mohamed Fellouah, Hachimi Laboratory of Control Testing Measurement and Simulation in Mechanics University Hassiba Benbouali Chlef Algeria Department of Electromechanics University Mohamed El-Bachir El-Ibrahimi Bordj-Bou-Arreridj Algeria Department of Mechanical Engineering Université de Sherbrooke SherbrookeQC Canada

ISBN: (纸本)9789811998218

This work presents an investigation of air ventilation systems to evaluate contaminant dispersion in the human-occupied spaces. The room model represents the rooms commonly found in apparel stores and shopping malls designated to try clothes. In this investigation, one occupant was assumed sick, and assigned to release contaminant gas from the mouth. Two ventilation systems (i.e. mixing and displacement ventilation systems) are incorporated in the room model in order to assess—by means of numerical steady-state simulation—the contaminants concentration in the room. The dispersion of contaminants in the room demonstrates that the mixing ventilation system is highly risky in terms of the spread of contagious airborne diseases. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational fluid dynamics

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Moving-Window Integrated Physics-Data-Based Modeling of Lateral Dynamics

Moving-Window Integrated Physics-Data-Based Modeling of Late...

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American control Conference (ACC)

作者： Wenpeng Wei Guodong Yin Jinxiang Wang Tianyi He School of Mechanical Engineering Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Department of Mechanical and Aerospace Engineering Utah State University Logan USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper proposes a moving-window Integrated Physics-Data-Based (IPDB) modeling method for the lateral dynamics of autonomous vehicles. The IPDB modeling method initiates from the fundamental physical principles of vehicle motions and express the nonlinear lateral dynamics directly by data snapshots in a moving window. Therefore, the IPDB model is physically interpretable and inherits adaptiveness of data-driven approaches. Specifically, the physics-based lateral dynamics are expressed into affine linear parameter-varying (LPV) formulation and system matrices can be successfully represented by the data snapshots in finite-length moving window without training or calibrations. After that, the CarSim simulations are conducted for data collection and to derive IPDB model. The data-efficient and online adaptive characteristics are validated by the data under several driving scenes. Simulation results confirm that the moving-window IPDB model only uses a small amount of data, but can exhibit great online modeling capability with strong adaptation and excellent model accuracy under varying driving scenarios.

关键词： Training Adaptation models Accuracy Computational modeling Dynamics Data models Mathematical models

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Combined Longitudinal-Lateral Dynamic Modeling and control via an Integrated Physics-Data-Based Approach

Combined Longitudinal-Lateral Dynamic Modeling and Control v...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Wenpeng Wei Zhaoyu Qiu Xiaoyuan Zhu Guodong Yin Tianyi He School of Mechanical Engineering Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Department of Mechanical and Aerospace Engineering Utah State University Logan UT USA

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

This paper presents an Integrated Physics-DataBased (IPDB) modeling and control scheme of the combined longitudinal-lateral vehicle dynamics. A nonlinear bicycle vehicle model is used to derive the linear parameter-varying (LPV) system representation, where four vehicle motion variables are considered as scheduling parameters. Taking advantage of kernels from LPV representation, the combined longitudinal-lateral dynamics are further expressed by the data snapshots of states, inputs, and scheduling parameters, which formulate the IPDB model. After that, the IPDB model is used to design a state-feedback gain-scheduling tracking controller to follow a reference trajectory. For validation purposes, the proposed modeling and control method is implemented on a QCar experimental platform. First, the IPDB model of coupled longitudinal-lateral dynamics is derived from experimental data and is further validated with excellent model accuracy under various driving conditions. Furthermore, an IPDB model-based gain-scheduling controller is synthesized and compared with the baseline Stanley controller in the experiment to track a given trajectory. The experimental results demonstrate that the IPDB model-based controller renders better tracking control performance.

关键词： Accuracy Mechatronics Tracking Optical variables measurement Dynamic scheduling Data models Trajectory

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Autonomous Surveillance Breakthrough by Implementing Facial Recognition in Dog Robots

Autonomous Surveillance Breakthrough by Implementing Facial ...

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Intermountain engineering, Technology and Computing (IETC)

作者： Amir Hafezi Minhaz Zibran Taher Deemyad Department of Mechanical and Measurement & Control Engineering Idaho State University Pocatello ID USA Department of Computer Science Idaho State University Pocatello ID USA

ISBN: (数字)9798350373073

ISBN: (纸本)9798350373080

As the importance of security in today's world continues to escalate, technology's role in its enhancement has evolved significantly. From the inception of fixed security cameras monitored by individuals, advancements have steered towards autonomous threat recognition systems and the deployment of mobile robots for proactive security measures. Despite these technological strides, the exploration of autonomous robots in security has been relatively scant. This study takes a novel approach by repurposing the commercial GO2 Edu dog robot for security purposes, equipping it with a thermal camera and custom software for robust face detection in varying lighting conditions, aimed at achieving safer and more reliable face recognition. Furthermore, the mapping and navigational capabilities are studied by conducting experiments with a Turtlebot outfitted with a 2D RPLIDAR and depth camera using SLAM, laying the groundwork for the GO2 robot's autonomous operations. The results of these enhancements have shown to be promising in the context of bolstering security measures. The paper concludes with a discussion on potential future enhancements, paving the way for more sophisticated and autonomous security robots.

关键词： Simultaneous localization and mapping Navigation Face recognition Robot vision systems Lighting Dogs Cameras

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