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International Conference on Internet technology and Applications, ITAP 2010

作者： Zhang, Zhong-Jie Institute of Signal Capturing and Processing Technology North China University Taiyuan Sanxi China

ISBN: (纸本)9781424451432

CNC machine tools of networking and information-oriented are the requests which the modern society develops. Networking Method of CNC machine tools has many kinds. This article describes several networking mode and Analysis of the Ethernet networking of CNC machine tool control system design advantages of. And Introduced the Ethernet-based networking remote monitoring system for CNC machine tools and design principles. Detailed design of Ethernet-based internal communications system and system software design. CNC machine tools to ensure real-time monitoring and reliability, but also economical and practical. ©2010 IEEE.

关键词： Ethernet

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Lamb wave metal plate defect detection based on COMSOL and finite element analysis

Lamb wave metal plate defect detection based on COMSOL and f...

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2016 International Conference on Automotive Engineering,Mechanical and Electrical Engineering(AEMEE 2016)

作者： Peng Qin Institute of Signal Capturing and Processing Technology North University of China School of Computer Science and Control Engineering North University of China

With the rapid development of China's economy, and with metal plates being widely used in aerospace, the national defence industry, shipbuilding industry, high-speed railway and so on, the domestic demand for metal plates is increasing. However, in sheet metal processing and production processes, due to the influence of the environment, there will inevitably be a variety of different types of defects. As a result, it is of great significance to choose a reliable method to carry out a large area detection of the metal plate. The electromagnetic ultrasonic guided wave transducer, because of possessing the advantages of both the Electromagnetic Ultrasonic Transducer(EMAT) and the ultrasonic guided wave, which is namely non-contact, large detection area, high sensitivity, small attenuation, and long distance transmission, is very suitable for non-destructive testing of large areas of sheet metal. However, its energy-change efficiency is not high. In order to improve the non-destructive testing of the electromagnetic ultrasonic Lamb wave transducer, this paper studies the optimisation method of the Lamb wave transducer in the detection of metal plate, carries out simulation calculations of the Lamb wave transducer after optimisation, and applies the optimised Lamb wave transducer in the detection of sheet metal defects. Simulation and experimental results show that for the Lamb wave, in the face of defect mode conversion, as the defect depth increases, the received echo signal amplitude increases, which characterises the defect echo size.

关键词： Lamb wave Defect detection Finite element analysis

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Convolutional Neural Network-Based Machine Vision for Non-Destructive Detection of Flooding in Packed Columns

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Sensors 2023年第5期23卷 2658-2658页

作者： Liu, Yi Jiang, Yuxin Gao, Zengliang Liu, Kaixin Yao, Yuan Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China Shanxi Key Laboratory of Signal Capturing & Processing North University of China Taiyuan030051 China Department of Chemical Engineering National Tsing Hua University Hsinchu300044 Taiwan

In chemical processes, packed columns are frequently employed in various unit operations. However, the flow rates of gas and liquid in these columns are often constrained by the risk of flooding. To ensure the safe and efficient operation of packed columns, it is crucial to detect flooding in real time. Conventional flooding monitoring methods rely heavily on manual visual inspections or indirect information from process variables, which limit the real-time accuracy of results. To address this challenge, we proposed a convolutional neural network (CNN)-based machine vision approach for non-destructive detection of flooding in packed columns. Real-time images of the packed column were captured using a digital camera and analyzed with a CNN model, which was been trained on a dataset of recorded images to identify flooding. The proposed approach was compared with deep belief networks and an integrated approach of principal component analysis and support vector machines. The feasibility and advantages of the proposed method were demonstrated through experiments on a real packed column. The results showed that the proposed method provides a real-time pre-alarm approach for detecting flooding, enabling process engineers to quickly respond to potential flooding events. © 2023 by the authors.

关键词： Convolutional neural networks

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Deep convolutional autoencoder thermography for artwork defect detection

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Quantitative InfraRed Thermography Journal 2024年第6期21卷 367-383页

作者： Liu, Yi Wang, Fumin Liu, Kaixin Mostacci, Miranda Yao, Yuan Sfarra, Stefano Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China Shanxi Key Laboratory of Signal Capturing & Processing North University of China Taiyuan China Professional Restorer Celano Italy Department of Chemical Engineering National Tsing Hua University Hsinchu Taiwan Department of Industrial and Information Engineering and Economics University of L’Aquila L’Aquila Italy

Infrared thermography is a cost-effective non-destructive evaluation technique that plays a critical role in extracting information about defects in cultural heritage such as works of art. However, in-depth studies on the internal structure of contemporary artworks using infrared thermography remain lacking, and therefore, a deep convolutional autoencoder thermography (DCAT) data analysis method is proposed for defect detection of contemporary artworks. In the proposed method, original data are enhanced by convolution to reduce the noise and inhomogeneous background in the original thermal images;subsequently, a deep autoencoder is used to extract nonlinear features from the enhanced thermographic data, and the results of the hidden layer are visualised. These visualised images highlight information regarding the internal structure and defects of the artwork. The case study on a handmade replica of Picasso’s ‘La Bouteille de Suze’, reproduced exactly as the original, has shown that DCAT significantly improved the accuracy of defect detection when compared with that of commonly used methods. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Nondestructive examination

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