The Experts below are selected from a list of 8748 Experts worldwide ranked by ideXlab platform
B. Sohlberg - One of the best experts on this subject based on the ideXlab platform.
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Monitoring and failure diagnosis of a Steel Strip process
IEEE Transactions on Control Systems Technology, 1998Co-Authors: B. SohlbergAbstract:This paper deals with condition monitoring and failure diagnosis of a Steel Strip rinsing process. Modeling and identification of the process is based on a priori knowledge about the process and data from the process. In the model, the worn parts are modeled explicitly and estimated online by an extended Kalman filter. The parameter estimation is used for supervision and as an advisory system for the process operators to decide which worn parts should be changed at the next planned stop. In addition to the normal wear, other types of abrupt failures may suddenly occur. It is not possible to detect these failures directly and the failures will give a biased parameter estimate and mislead the process operators into thinking that a part subject to wear should be changed although it is performing well. Therefore, the condition monitoring system is complemented with a fault detection and diagnosis system, which distinguishes normal wear from sudden abrupt failures.
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Optimal control of a Steel Strip rinsing process
Proceedings of IEEE International Conference on Control and Applications, 1993Co-Authors: B. SohlbergAbstract:Deals with optimal control of a Steel Strip rinsing process. The rinsing process is a dynamic nonlinear process. Modelling and identification of the process is based on a priori knowledge about the process and measured data from the process. Some parts of the process wear out. In the model, the worn parts are modelled explicitly and estimated on-line by an extended Kalman filter. The process is influenced by changing production variables, which are measurable but not controllable. The process is also influenced by disturbances. The optimal controller is based on a mathematical model of the process which includes on-line estimation of unknown parameters. The model is expressed in a discrete state space form, which makes the model suitable for optimal control. The physical limits of the process consist of the limits of the control signal. The optimal control signal is achieved by minimization of a quadratic loss function.
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Supervision of a Steel Strip rinsing process
[1992] Proceedings of the 31st IEEE Conference on Decision and Control, 1992Co-Authors: B. SohlbergAbstract:Condition supervision of Steel Strip rinsing process is considered. The rinsing process is a dynamic nonlinear process. Modeling and identification of the process is based on knowledge about the process and measured data from the process, known as gray-box identification. Some parts of the process wear out and are changed after manual inspection. In the model, the worn parts are modeled explicitly and estimated from measured data from the process. Data are collected before and after the worn parts are exchanged. The estimation is first made by offline identification by optimizing the likelihood function. Second, the estimation is made by using an extended Kalman filter. The result of the estimation is used to give a basis for a decision on which worn parts are to be exchanged.
Li Chen - One of the best experts on this subject based on the ideXlab platform.
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Surface Inspection System of Steel Strip Based on Machine Vision
2009 First International Workshop on Database Technology and Applications, 2009Co-Authors: Bo Tang, Jian-yi Kong, Xing-dong Wang, Li ChenAbstract:The traditional surface quality inspection of Steel Strip is carried by human inspectors, which is far from satisfactory because of its low productivity, low reliability and poor economy. It is a promising way to inspect surface quality of Steel Strip based on machine-vision technology. In this paper, the structure of the surface automated inspection system is described. The software and image processing of Steel Strip surface inspection is presented and the algorithms of detect surface defects of Steel Strip is discussed. The system is capable of both detecting and classifying surface defects in cold rolling Steel Strip.
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DBTA - Surface Inspection System of Steel Strip Based on Machine Vision
2009 First International Workshop on Database Technology and Applications, 2009Co-Authors: Bo Tang, Jian-yi Kong, Xing-dong Wang, Li ChenAbstract:The traditional surface quality inspection of Steel Strip is carried by human inspectors, which is far from satisfactory because of its low productivity, low reliability and poor economy. It is a promising way to inspect surface quality of Steel Strip based on machine-vision technology. In this paper, the structure of the surface automated inspection system is described. The software and image processing of Steel Strip surface inspection is presented and the algorithms of detect surface defects of Steel Strip is discussed. The system is capable of both detecting and classifying surface defects in cold rolling Steel Strip.
Jiangyun Li - One of the best experts on this subject based on the ideXlab platform.
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Real-time Detection of Steel Strip Surface Defects Based on Improved YOLO Detection Network
IFAC-PapersOnLine, 2018Co-Authors: Jiangyun Li, Zhenfeng Su, Jiahui GengAbstract:Abstract The surface defects of Steel Strip have diverse and complex features, and surface defects caused by different production lines tend to have different characteristics. Therefore, the detection algorithms for the surface defects of Steel Strip should have good generalization performance. Aiming at detecting surface defects of Steel Strip, we established a dataset of six types of surface defects on cold-rolled Steel Strip and augmented it in order to reduce over-fitting. We improved the You Only Look Once (YOLO) network and made it all convolutional. Our improved network, which consists of 27 convolution layers, provides an end-to-end solution for the surface defects detection of Steel Strip. We evaluated the six types of defects with our network and reached performance of 97.55% mAP and 95.86% recall rate. Besides, our network achieves 99% detection rate with speed of 83 FPS, which provides methodological support for real-time surface defects detection of Steel Strip. It can also predict the location and size information of defect regions, which is of great significance for evaluating the quality of an entire Steel Strip production line.
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Real-Time Classification of Steel Strip Surface Defects Based on Deep CNNs
Proceedings of 2018 Chinese Intelligent Systems Conference, 2018Co-Authors: Jiahui Geng, Zhenfeng Su, Weicun Zhang, Jiangyun LiAbstract:Steel Strip surface defects recognition is very important to Steel Strip production and quality control, in which correct classification of these surface defects is crucial. The surface defects of Steel Strips are classified according to various features, but it is hard for traditional methods to extract all these features and use them effectively. In this paper, we propose a method to deal with the problem of defect classification based on deep convolutional neural networks (CNNs). We adopt GoogLeNet, as our base model and add an identity mapping to it, which obtains improvement to some extent. At the same time, we establish a dataset of cold-rolled Steel Strip surface defects of six types and augment it in order to reduce over-fitting. Then we detect defects of six types with our network and reach an accuracy of 98.57%. Besides, our network achieves a speed of 125 FPS, which fully meets the real-time requirement of the actual Steel Strip production lines.
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Slighter Faster R-CNN for real-time detection of Steel Strip surface defects
2018 Chinese Automation Congress (CAC), 2018Co-Authors: Jiahui Geng, Jiangyun LiAbstract:Effective surface defect detection methods are of great significance for the production of high quality Steel Strip. Aiming at real-time detection of Steel Strip surface defect, this paper constructed a slighter Faster R-CNN. Firstly, convolutional layers for feature extraction in Faster R-CNN were replaced by depthwise separable convolutions so that the speed of the network increased three to four times. Then, center loss was added to the original loss function to improve the network's ability to distinguish different types of defects. Finally, a surface defect dataset containing 4655 images of 6 classes was established, and the proposed networks were trained on it. The proposed networks achieved 98.32 % accuracy with an average speed of 0.05s per image. Experimental results show that the slighter Faster R-CNN outperforms other Steel Strip surface defect detection methods in both accuracy and speed.
Bo Tang - One of the best experts on this subject based on the ideXlab platform.
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Surface Inspection System of Steel Strip Based on Machine Vision
2009 First International Workshop on Database Technology and Applications, 2009Co-Authors: Bo Tang, Jian-yi Kong, Xing-dong Wang, Li ChenAbstract:The traditional surface quality inspection of Steel Strip is carried by human inspectors, which is far from satisfactory because of its low productivity, low reliability and poor economy. It is a promising way to inspect surface quality of Steel Strip based on machine-vision technology. In this paper, the structure of the surface automated inspection system is described. The software and image processing of Steel Strip surface inspection is presented and the algorithms of detect surface defects of Steel Strip is discussed. The system is capable of both detecting and classifying surface defects in cold rolling Steel Strip.
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DBTA - Surface Inspection System of Steel Strip Based on Machine Vision
2009 First International Workshop on Database Technology and Applications, 2009Co-Authors: Bo Tang, Jian-yi Kong, Xing-dong Wang, Li ChenAbstract:The traditional surface quality inspection of Steel Strip is carried by human inspectors, which is far from satisfactory because of its low productivity, low reliability and poor economy. It is a promising way to inspect surface quality of Steel Strip based on machine-vision technology. In this paper, the structure of the surface automated inspection system is described. The software and image processing of Steel Strip surface inspection is presented and the algorithms of detect surface defects of Steel Strip is discussed. The system is capable of both detecting and classifying surface defects in cold rolling Steel Strip.
Jiahui Geng - One of the best experts on this subject based on the ideXlab platform.
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Real-time Detection of Steel Strip Surface Defects Based on Improved YOLO Detection Network
IFAC-PapersOnLine, 2018Co-Authors: Jiangyun Li, Zhenfeng Su, Jiahui GengAbstract:Abstract The surface defects of Steel Strip have diverse and complex features, and surface defects caused by different production lines tend to have different characteristics. Therefore, the detection algorithms for the surface defects of Steel Strip should have good generalization performance. Aiming at detecting surface defects of Steel Strip, we established a dataset of six types of surface defects on cold-rolled Steel Strip and augmented it in order to reduce over-fitting. We improved the You Only Look Once (YOLO) network and made it all convolutional. Our improved network, which consists of 27 convolution layers, provides an end-to-end solution for the surface defects detection of Steel Strip. We evaluated the six types of defects with our network and reached performance of 97.55% mAP and 95.86% recall rate. Besides, our network achieves 99% detection rate with speed of 83 FPS, which provides methodological support for real-time surface defects detection of Steel Strip. It can also predict the location and size information of defect regions, which is of great significance for evaluating the quality of an entire Steel Strip production line.
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Real-Time Classification of Steel Strip Surface Defects Based on Deep CNNs
Proceedings of 2018 Chinese Intelligent Systems Conference, 2018Co-Authors: Jiahui Geng, Zhenfeng Su, Weicun Zhang, Jiangyun LiAbstract:Steel Strip surface defects recognition is very important to Steel Strip production and quality control, in which correct classification of these surface defects is crucial. The surface defects of Steel Strips are classified according to various features, but it is hard for traditional methods to extract all these features and use them effectively. In this paper, we propose a method to deal with the problem of defect classification based on deep convolutional neural networks (CNNs). We adopt GoogLeNet, as our base model and add an identity mapping to it, which obtains improvement to some extent. At the same time, we establish a dataset of cold-rolled Steel Strip surface defects of six types and augment it in order to reduce over-fitting. Then we detect defects of six types with our network and reach an accuracy of 98.57%. Besides, our network achieves a speed of 125 FPS, which fully meets the real-time requirement of the actual Steel Strip production lines.
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Slighter Faster R-CNN for real-time detection of Steel Strip surface defects
2018 Chinese Automation Congress (CAC), 2018Co-Authors: Jiahui Geng, Jiangyun LiAbstract:Effective surface defect detection methods are of great significance for the production of high quality Steel Strip. Aiming at real-time detection of Steel Strip surface defect, this paper constructed a slighter Faster R-CNN. Firstly, convolutional layers for feature extraction in Faster R-CNN were replaced by depthwise separable convolutions so that the speed of the network increased three to four times. Then, center loss was added to the original loss function to improve the network's ability to distinguish different types of defects. Finally, a surface defect dataset containing 4655 images of 6 classes was established, and the proposed networks were trained on it. The proposed networks achieved 98.32 % accuracy with an average speed of 0.05s per image. Experimental results show that the slighter Faster R-CNN outperforms other Steel Strip surface defect detection methods in both accuracy and speed.
