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6th International Conference on Robotics, Control and automation engineering, RCAE 2023

作者： Zhang, Hao Guo, Li Wang, Guodong Li, Ming Yuan, Xuechao Anhui Polytechnic University School of Electrical Engineering Wuhu China Anhui Polytechnic University Anhui Institute of Future Research Wuhu China Wuhu Googao Automation Technology Co. Ltd Wuhu China

ISBN: (纸本)9798350315301

In order to reconstruct the acoustic emission (AE) waveform from the original sound emission signals of industrial robot reducers, the method of generating simulated AE signal sources on industrial robot reducers using pencil-lead breaking is commonly employed. This paper proposes a reconstruction method combining wavelet decomposition and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Firstly, the motor drives the reducer at 900 r/min, and the original AE signals are acquired using a DS5-16C acoustic emission instrument at a sampling rate of 3 MHz. Secondly, the original AE signals are subjected to wavelet analysis to obtain sub-signals, and representative sub-signals are selected for CEEMDAN decomposition and reconstruction of the AE waveform. Finally, Fast Fourier Transform (FFT) and energy proportion analysis are performed on the reconstructed AE waveform to obtain its frequency domain characteristics and energy proportion features. Experimental results demonstrate that the frequency range of 150 kHz to 170 kHz dominates the reconstructed AE waveform, and the noise energy proportion in the signal decreases from 93% to 64%, validating the effectiveness of the proposed method in this paper. © 2023 IEEE.

关键词： Empirical mode decomposition

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automation 5.0: The Key to Systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

automation has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： automation machinery intelligence

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Research on the Method of Sudden Failure Identification for Industrial Robot Gearboxes Based on Acoustic Emission Technology 6

Research on the Method of Sudden Failure Identification for ...

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6th International Conference on Robotics, Control and automation engineering, RCAE 2023

作者： You, Jun Qiao, Shuai Cheng, Zian Zhang, Hao Yuan, Xuechao Li, Ming Anhui Polytechnic University School of Electrical Engineering Wuhu China Wuhu Googao Automation Technology Co. Ltd Wuhu China Anhui Polytechnic University Anhui Institute of Future Research Wuhu China

ISBN: (纸本)9798350315301

In order to identify sudden failures in industrial robot gearboxes, a fault identification method based on time-frequency domain characteristics of Acoustic Emission (AE) signals is proposed, using the pencil-lead breaking method to simulate fault sources. Firstly, a DS5-16C AE instrument is used to build a gearbox AE acquisition platform, with the resonant frequency of the AE sensor set at 150 kHz and a sampling frequency of 3 MHz. Then, according to ASTM AE testing standards, sudden failures are simulated on the surface of a normally operating gearbox using pencil-lead break. Finally, based on calculating time-domain indicators such as the absolute mean, pulse index, and margin index of the raw AE signals, the time-frequency characteristic of the AE signals are further obtained through continuous wavelet transform, thus proposing a time-frequency domain identification method for sudden failures in gearboxes. The research results show that although the characteristic frequency of the same AE signal on the gearbox and the support are 170 kHz and 100 kHz respectively, the pulse index and margin index of the AE signals at different locations can significantly identify sudden failures. © 2023 IEEE.

关键词： Industrial robots

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Optimal controller design for state estimation of Boolean control networks 10

Optimal controller design for state estimation of Boolean co...

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10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021

作者： Chen, Yantao Yang, Junqi Cui, Lizhi Zhu, Junjie College of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo454000 China

ISBN: (纸本)9781665424233

In this paper, a kind of optimal controller is proposed to estimate the state of Boolean control networks (BCNs). Different from the standard observer, the optimal state estimation is completed by designing the control input instead of directly using it, where the maximum-minimum method is employed such that the state of BCNs can be uniquely estimated in possible short time steps. A set observer is first proposed to estimate the state of BCNs at any time steps. Based on the set observer, an initial output-dependent reconstructible state tree is developed, where an algorithm is provided to generate the nodes of such tree and can be implemented offline. The optimal control sequence for uniquely determining the state of BCNs is derived from the reconstructible state tree by a breadth-first search algorithm, where the output of BCNs is dynamically employed. An example is given to illustrate the applicability and usefulness of the developed methods. © 2021 IEEE.

关键词： State estimation

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Multivariable Decoupling Control of Civil Turbofan Engines Based on Fully Actuated System Approach

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Journal of Systems Science & Complexity 2023年第3期36卷 947-959页

作者： PENG Kai WANG Hongxia ZHANG Huanshui ZHANG Zhaorong YANG Fan School of Power and Energy Northwestern Polytechnical UniversityXi’an 710072China School of Electrical and Automation Engineering Shandong University of Science and TechnologyQingdao 266590China Department of Applied Mathematics The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina. Xi’an Aeronautics Computing Technique Research Institute AVICXi’an 710068China

Gas turbine engines must be operated by means of control,and how to achieve multivariable control decoupling with aero-engine control constraints is an open thorny issue attracting increasingly more *** paper considers the multivariable decoupling problems of aero-engines by using a compound controller,which originates from the fact that it is impossible to eliminate all the nonlinear dynamics of system to obtain desired constant linear closed-loop system by using full actuated control because of modeling errors and some physical *** controllers are involved in the compound *** is a fully actuated controller and the other is classical feedback *** order to use fully actuated control and maintain the accuracy of engine model,a full state scheduling linear parameter-varying(LPV)modeling method is proposed based on fuzzy neural network *** a general input matrix of the system,its generalized inverse is applied to design fully actuated controller to result in a pseudolinear *** with a feedback controller and control limiter,the control synthesis is *** simulation shows that the proposed method is possessed of a better decoupling and tracking effect compared with traditional control approach.

关键词： Compound control decoupling control fully actuated control turbofan engine

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WaveFA-Net: Bearing Fault Diagnosis Model Based on Multi-Domain Blind Deconvolution and Frequency Domain Attention Mechanism 5

WaveFA-Net: Bearing Fault Diagnosis Model Based on Multi-Dom...

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5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Ma, Ning Liu, Wenqiang Yang, Haonan Shi, Linjun Liu, Zhigang Shan, Xiangyu School of Electrical Engineering Southwest Jiaotong University Chengdu610031 China Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen518057 China Chinese National Rail Transit Electrification and Automation Engineering Technology Research Centre The Hong Kong Polytechnic University 999077 Hong Kong

ISBN: (纸本)9798331529192

Bearings are critical to mechanical systems, impacting reliability, safety, and efficiency. Traditional fault detection methods relying on vibration signal sensors face challenges with multi-scale, nonlinear, and noisy data, affecting performance and accuracy. This paper presents WaveFA-Net, a deep neural network integrating multi-resolution decomposition, frequency attention, multi-domain blind deconvolution, and attention mechanisms to improve diagnostic accuracy. The process begins with applying a discrete wavelet transform (DWT) for multi-resolution decomposition, extracting and refining key features. Next, frequency attention and multi-domain blind deconvolution are employed to isolate critical components. The multi-head and multi-scale attention mechanisms are then incorporated into the network ' s backbone and neck modules to capture global dependencies and multi-scale information. Finally, a physically inspired loss function, data augmentation, and hyperparameter optimisation are applied to reinforce robustness and stability. Experimental results demonstrate that WaveFA-Net performs well across varying signal-to-noise ratios (SNR), proving its effectiveness in complex environments. © 2024 IEEE.

关键词： Signal to noise ratio

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The Multi-Objective Optimization of AFPM Generators with Double-Sided Internal Stator Structures for Vertical Axis Wind Turbines

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Energy engineering 2021年第5期118卷 1439-1452页

作者： Dandan Song Lianjun Zhou Ziqi Peng Senhua Luo Jun Zhu Jiangsu Jinfeng Technology Co. Ltd.Yancheng224100China School of Automation Nanjing University of Science and TechnologyNanjing210094China School of Electrical Engineering and Automation Henan Polytechnic UniversityJiaozuo454000China

The axial flux permanent magnet(AFPM)generator with double-sided internal stator structure is highly suitable for vertical axis wind turbines due to its high power *** performance of the AFPM generator with double-sided internal stator structure can be improved by the reasonable design of electromagnetic *** further improve the overall performance of the AFPM generator with double-sided internal stator structure,multivariable(coil widthω_(c),permanent magnet thickness h,pole arc coefficient α_(p) and working air gap l_(g))and multi-objective(generator efficiencyη,total harmonic distortion of the voltage THD and induced electromotive force amplitude EMF)functional relationships are innovatively *** analysis,mean analysis and variance analysis are performed on the influence parameters by combining the Taguchi method and response surface methodology to study the influence degrees of each influence parameter on the optimization objectives to determine the most appropriate electromagnetic *** optimization results are verified by 3D finite element *** optimized APFM generator with double-sided internal stator structure exhibits superior economy,stronger magnetic density,higher efficiency and improved power quality.

关键词： Wind turbine double-sided internal stator structure multi-objective optimization axial flux permanent magnet generator

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Research on Industrial Monitoring Model Based on Neural Network Improvement 4

Research on Industrial Monitoring Model Based on Neural Netw...

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4th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2023

作者： Qian, Shenyi Tian, Ziqiao Wang, Guozhu Zou, Qiang Information Management Center Zhengzhou University of Light Industry Zhengzhou China College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang China School of Electrical Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350326444

BP neural network is often used in industrial process monitoring and fault diagnosis. For complex industrial process with large amount of data, the training cycle of the network is long and the convergence speed is slow. In this paper, a new industrial process monitoring method is proposed, that is, principal component analysis is introduced to reduce the dimension of data samples. On this basis, the structure of neural network is determined according to TE process data variables, and then a new BP neural network monitoring model is established, and the feasibility of this method is proved by simulation experiments. The test results show that the model has the advantages of fast calculation speed, short training time and low error detection rate. © 2023 IEEE.

关键词： Principal component analysis

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Exponential stabilisation for nonlinear PDE systems via sampled-data static output feedback control

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Cyber-Physical Systems 2021年第3期7卷 179-196页

作者： Hu, Dongxiao Song, Xiaona Wang, Mi Lu, Junwei School of Information Engineering Henan University of Science and Technology Luoyang China School of Electrical and Automation Engineering Nanjing Normal University Nanjing China

This paper focuses on the problem of exponential stabilisation for nonlinear partial differential equation (PDE) systems by designing sampled-data static output feedback controller. First, the nonlinear PDE systems are reconstructed by Takagi-Sugeno (T-S) fuzzy model. Subsequently, the closed-loop systems are obtained by introducing sampled-data static output feedback fuzzy controller. Then, stability conditions of the closed-loop system are obtained based on Lyapunov function approach, which can guarantee that the considered systems are exponential stabilisation and satisfy dissipative performance. Finally, the effectiveness of the developed method is demonstrated by a simulation study. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Closed loop systems

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Improved contrastive learning with MoCo framework 3

Improved contrastive learning with MoCo framework

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3rd International Conference on Consumer Electronics and Computer engineering, ICCECE 2023

作者： Li, Yihan Liu, Qingmin Zhou, Ling Zhao, Wenyi Tian, Yongqin Zhang, Weidong Stamford International University Business and Technology Bangkok10250 Thailand School of Information Engineering Henan Institute of Science and Technology Xinxiang453003 China School of Automation and Electrical Engineering University of Science and Technology Beijing100084 China

ISBN: (纸本)9798350331578

Self-supervised learning typically suffers from lacking contrastive pairs and extracting unrepresentative vectors. To handle above mentioned challenges, this paper introduces a novel self-supervised learning framework that integrates the location-based sampling manner and a well-designed dimensionality reduction module. In the location-based sampling module, this paper embeds a multi-crop sampling paradigm into the memory bank-based framework. In the dimensionality reduction module, this paper introduces a principal component dimensionality reduction to capture the most comprehensive features. Experiments on popular datasets demonstrate the superior performance of our proposed method. © 2023 IEEE.

关键词： Location

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