2023 IEEE 21st International Conference on Industrial Informatics (INDIN)最新文献_第2页

Low-Threshold Retrofit Strategy for CNC Machines: A New Process Data Acquisition Approach 数控机床低阈值改造策略:一种新的过程数据采集方法

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218263

Bastian Schulte, Harry Fast, Holger Flatt, Chris Kleinhans, R. Schulte

{"title":"Low-Threshold Retrofit Strategy for CNC Machines: A New Process Data Acquisition Approach","authors":"Bastian Schulte, Harry Fast, Holger Flatt, Chris Kleinhans, R. Schulte","doi":"10.1109/INDIN51400.2023.10218263","DOIUrl":"https://doi.org/10.1109/INDIN51400.2023.10218263","url":null,"abstract":"The retrieval of process data from older CNC machines is hampered by the lack of machine control interfaces and sensors required for data acquisition. To address this problem, this paper proposes a low-threshold retrofit strategy for chipping machines (> 20 years of age) that enables the assessment of the current machining process and the quality of the manufactured components. Using commercially available sensor technology, the machining condition can be assessed during the machining process. In addition, temperature sensors are used to monitor the inside of the machine. This work shows that a change in the internal temperature of ~3°c can be correlated with qualitative dimensional variations of about 0.014 mm. This has implications for process capability and component quality in the $mu$m range. This retrofit approach provides data on the machining process with little effort and leads to an improved assessment of productivity and quality. With this approach, process data can be collected from older machines and used to calculate the overall equipment effectiveness (OEE). As a result, unnecessary costs in production can be identified and eliminated, leading to a reduction in production costs and a competitive advantage in the global marketplace.","PeriodicalId":174443,"journal":{"name":"2023 IEEE 21st International Conference on Industrial Informatics (INDIN)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114712230","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 method for planning the trajectory of mobile hydraulic crane booms with a focus on energy efficiency 一种以节能为重点的移动式液压起重机臂架轨迹规划方法

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218284

V. Zhidchenko, Timofei D. Komarov, Antoine Williot, Nathan Bauer, H. Handroos

引用次数: 0

Key Indicators for the Discrimination of Wines by Electronic Noses 电子鼻鉴别葡萄酒的关键指标

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10217912

Julius Wötner, Helene Dörksen, M. Pein-Hackelbusch

{"title":"Key Indicators for the Discrimination of Wines by Electronic Noses","authors":"Julius Wötner, Helene Dörksen, M. Pein-Hackelbusch","doi":"10.1109/INDIN51400.2023.10217912","DOIUrl":"https://doi.org/10.1109/INDIN51400.2023.10217912","url":null,"abstract":"In the food industry, and especially in wines as products thereof, ethanol and sulfur dioxide play an equally important role. Both substances are important wine quality characteristics as they influence the taste and odor. As both substances comprise volatile matter, electronic noses should be applicable to discriminate the different qualities of wines. Our study investigates the influence of alcohol and sulfur dioxide on the discrimination ability of wines (especially those of the same grape variety) using two different electronic nose systems. One system is equipped with metal oxide sensors and the other with quartz crystal microbalance sensors. Contrary to indications in literature, where the alcohol content is discussed to have a large influence on e-nose results, it was shown that a difference of 1 % ethanol was not sufficient to allow accurate discrimination using Linear Discriminant Analysis by any system. On the positive side, the analyzed concentrations of ethanol (about 12 %) did not superimpose other volatile information. So difference in sulfur dioxide content gave an accuracy for sample discrimination of up to 90.6 % with MOS nose. Thus, we are so far partially able to discriminate wines with electronic noses based on their volatile imprint.","PeriodicalId":174443,"journal":{"name":"2023 IEEE 21st International Conference on Industrial Informatics (INDIN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131969081","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

Determining the Target Security Level for Automated Security Risk Assessments 确定自动化安全风险评估的目标安全级别

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10217902

Marco Ehrlich, Andre Bröring, C. Diedrich, J. Jasperneite, W. Kastner, H. Trsek

引用次数: 0

A Comparison of Statistical and Machine Learning Approaches for Time Series Forecasting in a Demand Management Scenario 需求管理场景中时间序列预测的统计和机器学习方法比较

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218206

Anton Pfeifer, Hendrik Brand, V. Lohweg

引用次数: 0

Edge Intelligence for Detecting Deviations in Batch-based Industrial Processes 基于批处理的工业过程偏差检测的边缘智能

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10217845

Alexander Keusch, Thomas Hiessl, M. Joksch, Axel Sündermann, Daniel Schall, Stefan Schulte

引用次数: 0

E-VarifocalNet: A Lightweight Model to Detect Insulators and Their Defects under Power Grid Surveillance E-VarifocalNet:电网监控下绝缘子及其缺陷检测的轻量化模型

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10217966

Chao Ouyang, Haijun Zhang, Xiangyu Mu, Zhou Wu, Wei Dai

{"title":"E-VarifocalNet: A Lightweight Model to Detect Insulators and Their Defects under Power Grid Surveillance","authors":"Chao Ouyang, Haijun Zhang, Xiangyu Mu, Zhou Wu, Wei Dai","doi":"10.1109/INDIN51400.2023.10217966","DOIUrl":"https://doi.org/10.1109/INDIN51400.2023.10217966","url":null,"abstract":"Detecting insulators and their defects is a key task in real-time power grid surveillance with the rapid development of national smart grid. Traditional surveillance usually relies on maintenance personnel, leading to the issues of inefficiency and unsafety. Thus, with the prosperity of deep learning, we proposed a detection algorithm, named E-VarifocalNet, which is an enhanced version of the basic VarifocalNet method. The proposed E-VarifocalNet is specifically designed for detecting insulators and their defects. We developed a classification loss based on varifocal loss and the number of samples to solve the imbalance problem in object detection. Furthermore, a regression loss based on GIoU loss and Wasserstein distance is designed to gain higher flexibility in the representation of bounding boxes. Additionally, we applied a feature pyramid network based on dilated convolution and heatmap to build global and local semantic relations among pixels so as to enhance the detection accuracy on salient areas. Our dataset containing 2,100 images and 5,217 object instances was collected through real-time drones and an open data platform. Our E-VarifocalNet gets the highest mAP and a low model complexity on our dataset among state-of the-art object detectors, indicating the potential of our algorithm in real-time power grid surveillance applications.","PeriodicalId":174443,"journal":{"name":"2023 IEEE 21st International Conference on Industrial Informatics (INDIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130830462","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

Deep Reinforcement Learning for Energy-Efficient Task Offloading in Cooperative Vehicular Edge Networks 基于深度强化学习的协同车辆边缘网络节能任务卸载

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218113

Paul Agbaje, E. Nwafor, Habeeb Olufowobi

{"title":"Deep Reinforcement Learning for Energy-Efficient Task Offloading in Cooperative Vehicular Edge Networks","authors":"Paul Agbaje, E. Nwafor, Habeeb Olufowobi","doi":"10.1109/INDIN51400.2023.10218113","DOIUrl":"https://doi.org/10.1109/INDIN51400.2023.10218113","url":null,"abstract":"In the Internet of Vehicle ecosystem, multi-access edge computing (MEC) enables mobile nodes to improve their communication and computation capabilities by executing transactions in near real-time. However, the limited energy and computation capabilities of MEC servers limit the efficiency of task computation. Moreover, the use of static edge servers in dense vehicular networks may lead to an influx of service requests that negatively impact the quality of service (QoS) of the edge network. To enhance the QoS and optimize network resources, minimizing offloading computation costs in terms of reduced latency and energy consumption is crucial. In this paper, we propose a cooperative offloading scheme for vehicular nodes, using vehicles as mobile edge servers, which minimizes energy consumption and network delay. In addition, an optimization problem is presented, which is formulated as a Markov Decision Process (MDP). The solution proposed is a deep reinforcement-based Twin Delayed Deep Deterministic policy gradient (TD3), ensuring an optimal balance between task computation time delay and the energy consumption of the system.","PeriodicalId":174443,"journal":{"name":"2023 IEEE 21st International Conference on Industrial Informatics (INDIN)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133621762","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

Communication of energy data in modular production 模块化生产中能源数据的通信

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218273

Leif-Thore Reiche, A. Fay

引用次数: 0

Domain Transfer for Surface Defect Detection using Few-Shot Learning on Scarce Data 基于稀缺数据的少弹学习表面缺陷检测领域转移

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10217859

Felix Gerschner, Jonas Paul, Lukas Schmid, Nico Barthel, Victor Gouromichos, Florian Schmid, Martin Atzmueller, Andreas Theissler

{"title":"Domain Transfer for Surface Defect Detection using Few-Shot Learning on Scarce Data","authors":"Felix Gerschner, Jonas Paul, Lukas Schmid, Nico Barthel, Victor Gouromichos, Florian Schmid, Martin Atzmueller, Andreas Theissler","doi":"10.1109/INDIN51400.2023.10217859","DOIUrl":"https://doi.org/10.1109/INDIN51400.2023.10217859","url":null,"abstract":"This study evaluates the effectiveness of transfer learning models in industrial surface defect detection using few-shot learning. Surface defect detection is a critical task in various industrial applications, where accurately detecting and classifying defects can improve product quality and increase manufacturing efficiency. However, data scarcity is a considerable challenge: obtaining and labelling defect samples is a costly, time-consuming process and difficult due to their infrequent occurrence. Few-Shot learning aims to effectively train models using only a limited number of labelled samples, thus mitigating the impact of data scarcity. This study compares the performance of transfer learning models pre-trained on three different data sets for few-shot learning in the context of surface defect detection. On the one hand, transfer learning models pre-trained on the ImageNet data set yield the best overall results in terms of accuracy. On the other hand, our results indicate that the DAGM data set, an industrial optical inspection data set which is close to the target domain, is particularly effective for training models to clearly detect surface defects in a few-shot learning scenario.","PeriodicalId":174443,"journal":{"name":"2023 IEEE 21st International Conference on Industrial Informatics (INDIN)","volume":"1213 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132771845","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