2020 IEEE 18th International Conference on Industrial Informatics (INDIN)最新文献

Improving Wireless Charging Efficiency with Machine Vision and Communication for Industrial Wireless Rechargeable Sensor Networks 利用机器视觉和通信技术提高工业无线充电传感器网络的无线充电效率

2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/INDIN45582.2020.9442213

Yaxiang Chen, Jingjing Yang, Anguo Liu, Ming-Chia Lai, Zhezhuang Xu, Jingao Hu

引用次数: 1

Grain Surface Simulator to Averiguate the Overlapping and Noise Problems on Computer Vision Granullometry of Fertilizers 利用颗粒表面模拟器解决计算机视觉粒度测量中的重叠和噪声问题

2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/INDIN45582.2020.9442238

Douglas A. Goulart, N. D. F. Traversi, J. C. O. Mendonça, R. N. Rodrigues, E. Estrada, Paulo L. J. Drews-Jr, Vinícius M. Oliveira, S. Botelho

{"title":"Grain Surface Simulator to Averiguate the Overlapping and Noise Problems on Computer Vision Granullometry of Fertilizers","authors":"Douglas A. Goulart, N. D. F. Traversi, J. C. O. Mendonça, R. N. Rodrigues, E. Estrada, Paulo L. J. Drews-Jr, Vinícius M. Oliveira, S. Botelho","doi":"10.1109/INDIN45582.2020.9442238","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442238","url":null,"abstract":"The production of food for all the population in the world became the biggest concern. The population continues to grow and the number of farmable lands has been decreasing. To make the lands more productive, fertilizers are used on a larger scale. To guarantee the quality of the product, particle size analysis are made by mechanical sieving. With the time, the wear-out of the sieving in the fertilizer industry the results of the particle size analysis will be erroneous. So the computer vision appears as an alternative that is non-invasive and less time-consuming. In this context, this paper has the objective to develop a grain surface simulator capable of generating virtual images with overlapping grains, since there is a difficulty to obtain annotated data of images of fertilizers. In order to validate the proposed simulator using a DIP algorithm, noises are added in the virtual images to compare with the reality in the industry, to show how well the particle size analysis with computer vision were handled towards adversities. The results of the overlapping analysis show that when the virtual image has a fewer number of grains, the DIP algorithm can identify the majority of grains, consequently with less error in the particle size analysis. Different noises, at different intensities, have their effects analyzed on the algorithm. As the analyzes in this study match with the reality showing the consequences, tendencies, and errors of the overlapping of grains and noises in the images, the simulator developed here matches with reality and is extremely useful to facilitate the study of complex cases of application of visual computing and digital image processing in particle size analysis of fertilizers.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125861019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Adversarial multi-domain adaptation for machine fault diagnosis with variable working conditions 基于多域自适应的变工况机械故障诊断

2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/INDIN45582.2020.9442084

Qi Li, Shuangjie Liu, Bingru Yang, Yiyun Xu, Liang Chen, Changqing Shen

{"title":"Adversarial multi-domain adaptation for machine fault diagnosis with variable working conditions","authors":"Qi Li, Shuangjie Liu, Bingru Yang, Yiyun Xu, Liang Chen, Changqing Shen","doi":"10.1109/INDIN45582.2020.9442084","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442084","url":null,"abstract":"Due to the complexity of industrial intelligent diagnosis, transfer learning-based fault diagnosis has become an evolving focus of the research field. Transfer learning uses knowledge of the source domain to identify faults in the target domain, which is a powerful tool to solve the problem of fault signal domain shift. However, existing methods have a limitation on multiple target domains. In other words, for different domains, respective transfer tasks are necessary. To seek a breakthrough, a adversarial multi-domain adaptation (AMDA) fault diagnosis method is proposed, realizing the fault diagnosis of multiple target domains by using the knowledge of a single source domain. AMDA is divided into three parts, namely, feature extractor, fault classifier and domain classifier. Through multi-domain adversarial learning, feature extractor and domain classifier mine the knowledge shared by multiple domains, and fault classifier can identify fault features distributed in different domains. The proposed AMDA method can surpass some traditional transfer learning fault diagnosis methods. Furthermore, as feature visualization result revealed, AMDA has significant advantages in multi-domain and broad research prospects.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123782941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Intelligent health evaluation of rolling bearings based on subspace meta-learning 基于子空间元学习的滚动轴承健康智能评估

2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/INDIN45582.2020.9442139

Peng Ding, M. Jia

{"title":"Intelligent health evaluation of rolling bearings based on subspace meta-learning","authors":"Peng Ding, M. Jia","doi":"10.1109/INDIN45582.2020.9442139","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442139","url":null,"abstract":"Health evaluation is attracting more and more attention in the domain of machinery prognostic and health management (PHM). Meanwhile, few studies have been devoted to health evaluation under variable working conditions and few shots learning, which are common situations under industrial sites. Thus, this shortcoming becomes the motivation of our study. We propose subspace meta-learning (SML) that integrates the strengths of knowledge transfer, constructing the statistically relevant latent subspace, and meta learning, realizing few shots prognostics. To be specifically, time-frequency images are first extracted with sliding windows along with the vibration signals across different life experiments of rolling bearings. Then, two-dimensional domain adaptation based on high order statistical properties is utilized to construct latent subspace and generate meta degradation knowledge. Finally, the convolutional layer based meta learning under model-agnostic learning mode is set up based on the time-frequency degradation knowledge. For a transparent test of our proposed SML health evaluation methodologies, public FEMTO-ST bearing datasets are employed for verifications, and comparisons are also conducted between existing prediction methods. Prediction performances reveal that the superiority of SML under few-shot prognostics.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126952806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Intelligent Fault Diagnosis under Small Sample Condition via A Signals Augmented Semi-supervised Learning Framework 基于信号增强半监督学习框架的小样本智能故障诊断

2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/INDIN45582.2020.9442224

Tianci Zhang, Jinglong Chen, Tongyang Pan, Zitong Zhou

引用次数: 1

Predictive Fast Charging of Lithium-ion Battery with Electro-thermal Constraints 基于电热约束的锂离子电池预测快速充电研究

2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/INDIN45582.2020.9442248

Hao Zhong, Zhongbao Wei, Hongwen He

引用次数: 0

Observer-based event-triggered cloud predictive control for heterogeneous MASs with DoS attacks and delays 针对具有DoS攻击和延迟的异构质量的基于观察器的事件触发云预测控制

2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/INDIN45582.2020.9442241

Xiuxia Yin, Zhiwei Gao, Yichuan Fu

引用次数: 1

System and Software Engineering, Runtime Intelligence 系统与软件工程，运行时智能

2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/indin45582.2020.9442217

引用次数: 0

Bidirectional Multiple-Port Three-Level DC-DC Converter for HESS in DC Microgrids 用于直流微电网HESS的双向多端口三电平DC-DC变换器

2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/INDIN45582.2020.9442127

B. Wang, W. Peng, G. Feng, X. Zhang, Y. Wang, U. Manandhar

{"title":"Bidirectional Multiple-Port Three-Level DC-DC Converter for HESS in DC Microgrids","authors":"B. Wang, W. Peng, G. Feng, X. Zhang, Y. Wang, U. Manandhar","doi":"10.1109/INDIN45582.2020.9442127","DOIUrl":"https://doi.org/10.1109/INDIN45582.2020.9442127","url":null,"abstract":"This paper proposes a bidirectional multiple-port three-level (BMPTL) DC-DC converter with two-stage structure for the hybrid energy storage system (HESS). The advantages of the proposed converter over the conventional converters for HESSs including reduced component size, superior extension capability and better control flexibility. These advantages results from the two-stage structure, the availability of three voltage levels, and the multiple-port design. A new control method based on deadbeat control strategy has been developed to regulate the BMPTL DC-DC converter. The proposed deadbeat-based method can mitigate the power imbalanced in DC microgrid and allocate proper power assignment for the battery and supercapacitor according to their characteristics simultaneously. To conduct the verification, a DC microgrid simulation model including the PV, load and HESS interfaced by the proposed BMPTL DC-DC converter is built. The simulation results are discussed in detail to demonstrate the effectiveness of the proposed BMPTL DC-DC converter with the deadbeat-based method.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126294070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Service-based Architecture for the Interaction of Control and MES Systems in Industry 4.0 Environment 工业4.0环境下基于服务的控制与MES系统交互体系结构

2020 IEEE 18th International Conference on Industrial Informatics (INDIN) Pub Date : 2020-07-20 DOI: 10.1109/INDIN45582.2020.9442083

Mahyar Azarmipour, Haitham Elfaham, Caspar Gries, T. Kleinert, U. Epple

引用次数: 4