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3rd International Conference on Advanced Sensing, intelligent Manufacturing, ASIM 2024

作者： Xuan, Liang Teng, Shaowei Yu, Haowei Yu, Changxin Liu, Xue Detection and Control of Specialized Equipment Jianghan University Hubei Engineering Research Center for Intelligent Wuhan China Wafangdian Bearing Group Corp. Ltd Dalian China

ISBN: (纸本)9798331524456

Due to various limitations such as the cost of test benches, loading techniques, and equipment, conducting performance and life assessment tests on wind turbine bearings has made full-scale testing even more challenging. Therefore, in order to save experimental costs while ensuring accurate and reasonable research into the reliable operation of wind turbine main shaft bearings. Before officially putting the wind turbine into use, it is crucial to carry out bearing performance and enhanced similarity tests, explore the scaling theory for wind turbine main bearings, and validate accelerated life tests for the bearings. This paper reviews the current development status of scaling tests for main bearings both domestically and internationally. It also discusses and analyzes the similarity theory for scaled- down tests of large wind turbine bearings, as well as the scaled-down design of wind turbine bearing tests, which provides theoretical support for the research of gear scaling. © 2024 IEEE.

关键词： component: Wind turbine main shaft bearing Scaling-down theory for wind turbine main bearings Similarity experiment Similarity theory

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research on the Theory and Methodology of Wind Turbine Bearing Downscaling Tests

Research on the Theory and Methodology of Wind Turbine Beari...

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intelligent Manufacturing, Advanced Sensing and Big Data (IMASBD), International Conference on

作者： Liang Xuan Shaowei Teng Haowei Yu Changxin Yu Xue Liu Hubei Engineering Research Center for Intelligent Detection and Control of Specialized Equipment Jianghan University Wuhan China Wafangdian Bearing Group Corp. Ltd Dalian China

ISBN: (数字)9798331524456

ISBN: (纸本)9798331524463

关键词： Shafts Costs Accuracy Reviews Wind power generation Reliability theory Wind turbines Sensors Manufacturing Test equipment

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Arc Grounding Fault Identification Using Integrated Characteristics in the Power Grid

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Energy engineering 2024年第7期121卷 1883-1901页

作者： Penghui Liu Yaning Zhang Yuxing Dai Yanzhou Sun School of Electrical Engineering and Automation Henan Polytechnic UniversityJiaozuo454003China Research and Development Center Guangdong Zhicheng Champion Group Co.Ltd.Dongguan523000China Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment Henan Polytechnic UniversityJiaozuo454003China

Arc grounding faults occur frequently in the power grid with small resistance grounding neutral *** existing arc fault identification technology only uses the fault line signal characteristics to set the identification index,which leads to detection failure when the arc zero-off characteristic is *** solve this problem,this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound ***,the waveform characteristics of the fault line and sound lines under an arc grounding fault are *** that,the convex hull,gradient product,and correlation coefficient index are used as the basic characteristic parameters to establish fault identification ***,the logistic regression algorithm is employed to deal with the reference samples,establish the machine discrimination model,and realize the discrimination of fault ***,simulation test results and experimental results verify the accuracy of the proposed *** comparison analysis shows that the proposed method has higher recognition accuracy,especially when the arc dissipation power is smaller than 2×10^(3) W,the zero-off period is not *** conclusion,the proposed method expands the arc fault identification theory.

关键词： Arc fault convex hull algorithm correlation coefficient fault identification gradient logistic regression

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research on Key Technologies of intelligent Acupuncture Robot 3

Research on Key Technologies of Intelligent Acupuncture Robo...

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3rd International Conference on Advanced Sensing, intelligent Manufacturing, ASIM 2024

作者： Xuan, Liang Xu, Yufei Zhang, Hong-Xing Li, Yifeng Pan, Xiao-Li Sun, Chun Jianghan University Hubei Engineering Research Center for Intelligent Detection and Control of Specialized Equipment Wuhan China Jianghan University Acupuncture and Moxibustion Research Institute Medical Department Wuhan China Hubei University of Chinese Medicine National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion Wuhan China First Teaching Hospital of Tianjin University of Traditional Chinese Medicine National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion China

ISBN: (纸本)9798331524456

Acupuncture has been widely applied and recognized globally. With the integration of artificial intelligence, robotic technology, and traditional Chinese acupuncture therapy, intelligent acupuncture robots have become a cutting-edge focus in the research of intelligent medical equipment in Traditional Chinese Medicine. This paper combed through the relevant literature on the research of key technologies such as acupoint localization, treatment planning, and closed-loop feedback control of intelligent acupuncture robots in recent years, focusing on key issues such as accurate acupoint localization, individual adaptability of acupuncture trajectory, force sensing ability, and operational safety in the practical application of acupuncture, and put forward the solutions to help intelligent acupuncture robots in the field of application. © 2024 IEEE.

关键词： acupoint localization acupuncture robot feedback control trajectory planning

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Multi-Channel Based on Attention Network for Infrared Small Target detection

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Journal of Shanghai Jiaotong university(Science) 2024年第3期29卷 414-427页

作者：张彦军王碧云蔡云泽 Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Institute of Aerospace Control Technology Shanghai 201109China Infrared Detection Technology R&D Center of China Aerospace Science and Technology Corporation Shanghai 201109China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai 200240China Institute of Marine Equipment Shanghai Jiao Tong UniversityShanghai 200240China

Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking ***,the complex background,the strong noise,and the characteristics of small scale and weak intensity of targets bring great difficulties to the detection of infrared small targets.A multi-channel based on attention network is proposed in this paper,aimed at the problem of high missed detection rate and false alarm rate of traditional algorithms and the problem of large model,high complexity and poor detection performance of deep learning ***,given the difficulty in extracting the features of infrared multiscale and small dim targets,the multiple channels are designed based on dilated convolution to capture multiscale target ***,the coordinate attention block is incorporated in each channel to suppress background clutters adaptively and enhance target *** addition,the fusion of shallow detail features and deep abstract semantic features is realized by synthesizing the contextual attention fusion ***,it is verified that,compared with other state-of-the-art methods based on the datasets SIRST and MDFA,the proposed algorithm further improves the detection effect,and the model size and computational complexity are smaller.

关键词： infrared image small target detection deep learning attention mechanism feature fusion

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Mobilenetv2-SSD Target detection Method Based on Multi-scale Feature Fusion 8th

Mobilenetv2-SSD Target Detection Method Based on Multi-scale...

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8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023

作者： Li, Boao Jiang, Du Tang, Xinjie Sun, Ying Weng, Yaoqing Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China Hubei Longzhong Laboratory Hubei Xiangyang441000 China

ISBN: (纸本)9789819980208

Most of the deep learning networks used in target detection algorithms are very complex, which poses difficulties for edge devices with limited storage capacity and computing power, such as mobile terminals. A MobileNetv2-SSD target detection algorithm based on multi-scale feature fusion is proposed in this paper. We use MobileNetv2 to improve the SSD algorithm by replacing the original network’s backbone network VGG16 with the lightweight network MobileNetv2 to perform the model streamlining. The problem of accuracy loss due to model compression is improved by the designed feature fusion module and finally tested on the dataset. The experimental results show that the mAP and FPS are 90.37% and 50FPS, respectively, and the detection accuracy is improved by 3.24% compared with the original SSD network, the detection speed is 1.9 times of the original one, and the size of the model is only 28.5M, which is only 30.5% of the original model. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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research on Key Technologies of intelligent Acupuncture Robot

Research on Key Technologies of Intelligent Acupuncture Robo...

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intelligent Manufacturing, Advanced Sensing and Big Data (IMASBD), International Conference on

作者： Liang Xuan Yufei Xu Hong-Xing Zhang Yifeng Li Xiao-Li Pan Chun Sun Hubei Engineering Research Center for Intelligent Detection and Control of Specialized Equipment Jianghan University Wuhan China Medical Department Acupuncture and Moxibustion Research Institute Jianghan University Wuhan China National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion Hubei University of Chinese Medicine Wuhan China National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion First Teaching Hospital of Tianjin University of Traditional Chinese Medicine China

ISBN: (数字)9798331524456

ISBN: (纸本)9798331524463

关键词： Location awareness Point cloud compression Three-dimensional displays Trajectory planning Robot kinematics Force Robot sensing systems Acupuncture Feedback control research and development

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Object detection with Depth Information in Road Scenes 8th

Object Detection with Depth Information in Road Scenes

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8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023

作者： Liu, Ruowang Chen, Xinbo Tao, Bo Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan China Precision Manufacturing Institute Wuhan University of Science and Technology Wuhan China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9789819980208

In recent years, depth estimation has witnessed significant advancements because of the development of deep learning. It's important to note that depth estimation tasks focus solely on predicting the depth of each pixel in an image and do not include object detection or object recognition. Depth estimation is the use of pixel transformations in the image to obtain distance information from each point in the scene to the camera to generate a depth map. Object detection is the process of classifying and localizing an image, given a picture, so as to identify the objects in the picture and determine their location. To overcome this limitation and integrate object detection into the depth estimation process, this paper proposes a novel self-supervised monocular depth estimation algorithm that leverages an attention mechanism. By combining object detection and depth estimation, a real-time multi-task model is designed to enable simultaneous detection and depth estimation of objects. The framework comprises four essential components: an object detection sub-network, a depth estimation sub-network, a lateral sharing unit, and an attention loss. These components work collaboratively to enhance distance estimation accuracy for objects and improve the object detection performance. Throughout experiments, it is evident that the proposed approach can effectively estimate distances to objects and enhances the accuracy of object detection. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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A Dsspi Phase Unwrapping Method for Improving the detection Efficiency of Cfrp-Reinforced Concrete Defect

SSRN

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SSRN 2023年

作者： Zhang, Xiaowei Wang, Huifeng Peng, Haonan Du, Hao Jiao, Yunmei Li, Shaoqiang Zhang, Jinxi Pan, Zefeng Huang, He Ju, Yongfeng School of Electronic & Control Engineering Chang’an University Xi’an710064 China Road Traffic Intelligent Detection and Equipment Technology Research Center of Shanxi Xi’an710064 China

Digital shear speckle pattern interferometry is a non-destructive detection method for non-contact measurement of object deformation and defects in a full field. In recent years, this technology has been widely applied in the field of non-destructive detection of concrete. However, the defect detection of this technology usually requires steps such as stripe processing, phase extraction, phase filtering, and phase unwrapping, which are cumbersome and reduce detection efficiency. Therefore, under the premise of discarding the phase filter, this paper proposes a method to directly unwrap the noise-wrapped phase. Through the combination of the Zernike polynomial and the wrapped Kalman filter, the phase unwrapping is transformed into the problem of solving the fitting coefficient in the noise-wrapped phase, thereby reducing the detection steps and which improves the detection efficiency. In this paper, the unwrapping applicability and robustness of the algorithm are verified through simulation and experiment, and the performance of the algorithm for unwrapping the noise wrapping phase is verified by comparative experiments in the actual environment for the first time. The results show that the algorithm has the best phase unwrapping effect (RMSE=0.0168, Time=5.18 seconds), reflecting its advantages compared to traditional phase unwrapping methods, At the same time, the algorithm was applied to the internal defect detection of CFRP-reinforced concrete, and the detection results show that the algorithm can effectively detect the location of internal defects, providing a new detection approach for non-destructive detection of concrete. © 2023, The Authors. All rights reserved.

关键词： Kalman filters

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Deep Learning-Based Dimension Measurement System for Industrial Part Inspection

Deep Learning-Based Dimension Measurement System for Industr...

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2024 China Automation Congress, CAC 2024

作者： Li, Xiao-Di Fan, Xing-Rong Zhang, Xian-Ming Zhang, Han-Zhi Huang, Chun-Jie Xu, Cheng Chongqing Engineering Laboratory for Detection Control and Integrated System School of Artificial Intelligence Chongqing Technology and Business University Chongqing China Engineering Research Centre for Waste Oil Recovery Technology and Equipment Ministry of Education Chongqing Key Laboratory Chongqing Technology and Business University Chongqing China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China Chongqing Intelligent Supply Chain Engineering & Technology Research Center Chongqing Jiaotong University Chongqing China Chongqing Aerospace Bashan Motorcycle Manufacturing Co. Ltd. Chongqing China

ISBN: (纸本)9798350368604

This paper develops a deep learning-based system for the dimension measurement of industrial parts. The system employs a general self-supervised image segmentation network to perform dichotomous image segmentation (DIS) on the parts, effectively extracting binary images of the target components from complex backgrounds without requiring preprocessing steps such as filtering or denoising. Following segmentation, contour detection, and dimension measurement are executed. The system utilizes a CMOS area scan camera and was tested on a computer equipped with a GTX1050 graphics card. The tests, conducted on two typical thrust ball bearing components (i.e., the ball retainer and the shaft ring) ü yielded ten measurements per part. Comparison with a vernier caliper as the reference standard reveals that under normal operating conditions, the maximum standard deviation is 0.007 mm and the maximum absolute error is 0.017 mm. © 2024 IEEE.

关键词： Ball bearings

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