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13th International Conference on Quality, Reliability, Risk, Maintenance, and Safety engineering, QR2MSE 2023

作者： Chen, Ronghua Gu, Yiliu Li, Jing Qiu, Guangqi Gu, Yingkui School of Mechanical and Electrical Engineering Jiangxi University of Science and Technology Ganzhou China College of Computer Scienceand Electronic Engineering Hunan University Changsha China School of Automotive Jiangxi College of Applied Technology Ganzhou China

ISBN: (纸本)9781839539848

The conventional convolutional neural network (CNN) adopts the softmax function as a classifier, which has problems of overflow and underflow. To address this issue, CNN and bi-directional long short-term memory (BiLSTM) are combined with multi-scales (MCNN-BiLSTM) as the feature extractor to improve feature extraction capability, and then support vector machine (SVM) is employed as the classifier to improve classification performance. Thus, an intelligent fault diagnosis method combined with MCNN-BiLSTM and SVM (MCNN-BiLSTM-SVM) is proposed. The wavelet threshold denoising algorithm is adopted for signal preprocessing. Transfer learning is used in MCNN-BiLSTM-SVM for different conditions. According to the bearing fault diagnosis experiments, the proposed MCNN-BiLSTM-SVM fault diagnosis method has higher diagnostic accuracy, stronger anti-noise performance and better stability under different conditions than other diagnostic methods. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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Fault Prediction of P-R-N-D Shifting Force Based on BP Neural Network 16

Fault Prediction of P-R-N-D Shifting Force Based on BP Neura...

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16th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2023

作者： Wang, Shuxian Liu, Shiyou Su, Zuqiang Wang, Xin Liu, Linlin Zhu, Hang Chongqing University of Posts and Telecommunications School of Advanced Manufacturing Engineering Chongqing China Chongqing University School of Computer Science Chongqing China Xi'An University of Architecture and Technology School of Mechanical and Electrical Engineering Xian China

ISBN: (纸本)9798350327144

As an important reference indicator for transmissions, it is necessary to conduct offline testing of PRND shifting force. By studying the shift force of PRND(Parking, Reverse, Neutral, and Drive), a fault prediction method for PRND shift force based on BP neural network is proposed to improve the offline detection efficiency of PRND shift force. Based on the data obtained from the offline detection platform, a dataset for network training is obtained after processing;Build a PRND shift force fault prediction model, design and train a BP neural network, and after error backpropagation, the next layer gradient determines the gradient of the current layer to obtain the prediction network;Bringing the dataset into the network for simulation and comparing the simulation results, it was found that the network can achieve ideal results. © 2023 IEEE.

关键词： Digital storage

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Design of a Low-Cost Wireless Charging Station Based on the Robotic Vision System 20

Design of a Low-Cost Wireless Charging Station Based on the ...

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20th IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2023

作者： Cheng, Wen-Yu Cheng, Wen-Chung Ni, Zhen Engeberg, Erik D. Zhong, Xiangnan Florida Atlantic University Department of Ocean and Mechanical Engineering Boca RatonFL33431 United States Florida Atlantic University Department of Electrical Engineering & Computer Science Boca RatonFL33431 United States

ISBN: (纸本)9798350331110

Physical training of autonomous robotic systems are often limited by battery capacity since the intelligent training cycles have to be halted for battery recharge. Many researchers have focused on various solutions of power management for autonomous robots. However, these solutions are usually custom-fitted for the specific applications, and would be difficult to be generalized to other autonomous robotic systems. In this paper, we propose to develop a low-cost wireless charging system based on the on-board vision/camera sensor. This design is capable of autonomous docking for the robot chassis without further external supervision. Specifically, we use the QR codes to label the charging station and establish the robot's camera vision system to recognize the dock. The robot will drive to the station automatically. The low-cost charging station is also designed for wireless charging of the robot. We present the specific designs of charging dock, wireless connection, and battery charging experiments explicitly. This low-cost wireless charging system provides advancements toward a fully autonomous physical training infrastructure for robotic navigation experiments. © 2023 IEEE.

关键词： 3D printing

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High-Lift Prediction Workshop 5: High-Order Discretization Technology Focus Group Summary

High-Lift Prediction Workshop 5: High-Order Discretization T...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Galbraith, Marshall C. Ruiz-Gironés, Eloi Roca, Xevi Wang, Z.J. Holst, Kevin R. Skaperdas, Vangelis Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States Applications in Science and Engineering Barcelona Supercomputing Center - BSC Barcelona Spain Department of Aerospace Engineering University of Kansas LawrenceKS66045 United States Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Customer Service BETA CAE Systems Thessaloniki Greece

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper summarizes the High-Order Technical Focus Group (HO-TFG) submissions forthe 5th AIAA High-Lift Prediction Workshop (HLPW-5). The goal of the workshop was to assess the state-of-the-art in mesh generation and computational fluid dynamics software for predicting peak lift of transport aircraft with flaps and slats deployed. Numerous geometric build ups of the Common Research Model High-Lift (CRM-HL) variant and Reynolds number considerations served as the primary focus of the workshop, and a simple wing-body variant of the CRM-HL was used as a verification test case. The HO-TFG was tasked with generating high-order curved meshes for these complex geometries, and computing high-order solutions using both Reynolds Averaged Navier-Stokes (RANS) and scale resolving Large Eddy Simulation(LES) formulations. While participation in the HO-TFG was limited, the effort produced curved meshes on highly-complex geometric configurations and computational results from two participants. The LES results demonstrate the potential for producing results comparable to 2nd-order accurate methods, but with fewer degrees of freedom. The RANS submission for this workshop does not demonstrate a benefit over 2nd-order schemes. However, the authors will argue a lack of understanding for optimal mesh distributions for high-order methods is the cause for the limited benefits observed for the RANS results. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Reynolds number

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Optimizing Battery Management Systems For Electric Vehicles: Current Trends And Future Directions 5

Optimizing Battery Management Systems For Electric Vehicles:...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Prakash, V.S. Muthu, Annamalai Anandan, B. Velmurugan, V. Raja, T. Alex Stanley Purushothaman, V. Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Science Electrical Section Engineering Department Nizwa Oman Siddharth Institute of Engineering & Technology Department of Mechanical Engineering Andhrapradesh Puttur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Avadi Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Electrical and Electronics Engineering Sathyamangalam India Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350376685

The rapid global shift towards electric vehicles (EVs) marks a transformative era in transportation, driven by the imperative to reduce carbon emissions and reliance on fossil fuels. Central to the performance and longevity of EVs are Battery Management Systems (BMS), which optimize battery usage, enhance safety, and extend operational life. This comprehensive review explores current trends and future directions in BMS optimization, focusing on key technological advancements and their implications for electric mobility. Battery Management Systems (BMS) serve as critical components in electric vehicles, orchestrating the efficient operation of battery packs through real-time monitoring, state-of-charge (SoC) estimation, thermal management, and fault diagnosis. These systems not only ensure optimal performance but also safeguard against potential hazards, thereby enhancing vehicle reliability and user confidence. Current advancements in BMS technology emphasize enhanced accuracy in SoC estimation, leveraging sophisticated algorithms and sensor integration to provide precise information on battery charge levels. Coupled with innovative thermal management strategies, which include active cooling and heating systems, BMS can mitigate thermal stress and optimize battery performance across diverse environmental conditions. By synthesizing technological advancements, emerging trends, and potential challenges, it offers valuable insights for researchers, engineers, and policymakers aiming to advance the efficiency, reliability, and sustainability of electric mobility solutions worldwide. © 2024 IEEE.

关键词： State of charge

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Machine learning-based real-time visible fatigue crack growth detection

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Digital Communications and Networks 2021年第4期7卷 551-558页

作者： Le Zhang Zhichen Wang Lei Wang Zhe Zhang Xu Chen Lin Meng School of Chemical Engineering and Technology Tianjin UniversityTianjinChina College of Science and Engineering Ritsumeikan University KusatsuShigaJapan Department of Mechanical and Electrical Engineering Shenyang Aerospace UniversityShenyangLiaoningChina

Many large-scale and complex structural components are applied in the aeronautics and automobile ***,the repeated alternating or cyclic loads in service tend to cause unexpected fatigue ***,developing real-time and visible monitoring methods for fatigue crack initiation and propagation is critically important for structural *** paper proposes a machine learning-based fatigue crack growth detection method that combines computer vision and machine *** our model,computer vision is used for data creation,and the machine learning model is used for crack *** computer vision is used for marking and analyzing the crack growth path and *** apply seven models for the crack classification and find that the decision tree is the best model in this *** experimental results prove the effectiveness of our method,and the crack length measurement accuracy achieved is 0.6 ***,the slight machine learning models help us realize real-time and visible fatigue crack detection.

关键词： Fatigue crack Growth prediction Mechanoresponsive luminogen Structural health monitoring computer vision Machine learning

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Adaptive neural network control of a 2-DOF helicopter system considering input constraints and global prescribed performance

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science China(Information sciences) 2024年第7期67卷 224-239页

作者： Zhijia ZHAO Jiale WU Zhijie LIU We HE C.L.Philip CHEN School of Mechanical and Electrical Engineering Guangzhou University School of Intelligence Science and Technology and Key Laboratory of Intelligent Bionic Unmanned Systems of Ministry of Education University of Science and Technology Beijing School of Computer Science and Engineering South China University of Technology Pazhou Lab

In this study, an adaptive neural network(NN) control is proposed for nonlinear two-degree-offreedom(2-DOF) helicopter systems considering the input constraints and global prescribed ***, radial basis function NN(RBFNN) is employed to estimate the unknown dynamics of the helicopter system. Second, a smooth nonaffine function is exploited to approximate and address nonlinear constraint functions. Subsequently, a new prescribed function is proposed, and an original constrained error is transformed into an equivalent unconstrained error using the error transformation and barrier function transformation methods. The analysis of the established Lyapunov function proves that the controlled system is globally uniformly bounded. Finally, the simulation and experimental results on a constructed Quanser's test platform verify the rationality and feasibility of the proposed control.

关键词： adaptive NN control 2-DOF helicopter global prescribed performance input constraints

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Coupling Smoothed Particle Hydrodynamics Method with Finite Element Method for Air-solid Interaction Modelling of Clapping Wing Micro Air Vehicles

Coupling Smoothed Particle Hydrodynamics Method with Finite ...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Zhang, Yanwei Wang, Zhonglai Pereira Castro, Saullo Giovani School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China Delft University of Technology Faculty of Aerospace Engineering HS 2629 Netherlands

ISBN: (纸本)9781665481090

Inspired by natural birds and insects, a flapping wing micro air vehicle with a double fling-clap mechanism is presented to achieve flapping flight. Smoothed particle hydrodynamics model has proven to be successfully applied to implement water cases. This study focuses on the fluid-solid interaction model coupling smoothed particle hydrody-namics method with the finite element method to handle three-dimensional numerical simulations of flexible clapping wings. Additionally, a validation study is carried out to examine the accuracy of our numerical model. Using the proposed model, analysis under a parametric study shows wingspan of the clapping wings is highly correlated with lift, lift/drag, and lift coefficient. This study provides a high-fidelity air-solid interaction model towards flexible clapping wings and a better understanding of the aerodynamic characteristics of clapping wing micro air vehicles. © 2022 IEEE.

关键词： Finite element method

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Enhancing Non-Destructive Examining of Composite Materials Using Lightweight Structures And Machine Learning

Enhancing Non-Destructive Examining of Composite Materials U...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Gangwar, Prashant Kumar Pattanaik, Balachandra Madiajagan, M. Sohail, Mohammed Ali Babu, M. Dinesh Sarala, B. Woldia University Department of Construction Technology & Management Amhara Region Ethiopia Wollega University College of Engineering and Technology Department of Electrical and Computer Engineering Nekemte Ethiopia Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India Jazan University College of Computer Science & Information Technology Department of Computer & Network Engineering Jazan Saudi Arabia Rajalakshmi Institute of Technology Department of Mechanical Engineering Chembarabakkam Chennai India R.M.K Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350344387

Composite materials and structures are becoming more efficient and cost-effective, and new special features are being developed to improve their specific properties. Their use in a variety of applications such as wind turbines, aeronautical, medical equipment, transportation, and other load-bearing structures is becoming increasingly important. It is necessary for non-destructive composite examining to be robust and trustworthy in order to limit the risk of damage as well as the cost of maintenance. In order to ensure the product quality throughout its life cycle, many non-destructive examining procedures based on a wide range of concepts have been created. In this paper, nondestructive examining was used to obtain vibration measurements of composites in high-frequency regimes, which were then analysed. Both end-to-end classifications of the data and classifications of data acquired by feature engineering with modal analysis employ Support Vector Machine (SVM) to carry out damage localization in their respective applications. Finally, the findings display a large number of plates made of various materials and with defects of varying magnitudes. © 2023 IEEE.

关键词： Composite Localization Machine Learning Nondestructive Examining

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Permanent Magnet Synchronous Motor Control and Implementation of this Control into the Microprocessor and its Realization

Permanent Magnet Synchronous Motor Control and Implementatio...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Rathnakar, G. Negi, Harendra Singh Shashibhushan, G. Nandankar, Praful V. Vekariya, Daxa Maranan, Ramya Jss Science and Technology University Department of Mechanical Engineering Karnataka Mysuru570006 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Sir M Visvesvaraya Institute of Technology Department of Electronics and Communication Engineering Karnataka Bengaluru562 157 India Government College of Nagpur Department of Electrical Engineering Maharashtra 441108 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Post Limda Gujarat 391760 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350375237

Traditional methods of motor control, such as scalar control or field-oriented control (FOC), may struggle to provide the same level of precision and responsiveness as vector control. This work explores the implementation of vector control in the STM32F302R8 microprocessor for precise regulation of speed and torque in electric motors, commonly utilized in industrial automation and electric vehicles. Prior to vector control implementation, the microprocessor interfaces with essential components: the inverter for DC to AC power conversion, the control module housing control algorithms, and the resolver for rotor position feedback. The process involves programming and testing to accurately evaluate the rotor's rotation angle, crucial for effective vector control by ensuring correct voltage application to motor windings. Upon successful implementation and testing, the vector control system demonstrates functionality in regulating motor speed responsively to desired speed changes. © 2024 IEEE.

关键词： Vectors

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