{"title":"用于电解精炼短路检测的集合多标签分类法","authors":"","doi":"10.1016/j.aei.2024.102919","DOIUrl":null,"url":null,"abstract":"<div><div>Short-circuits occurring in the electrolytic refining process of non-ferrous smelting are a main factor that consumes extra energy and affects the metal quality. This paper proposes an ensembled multilabel classification method for short-circuit detection based on infrared images and makes up for the defect of previous methods using object-detection neural networks being hard to directly apply in industrial sites. Different from the object-detection methods, the multilabel classification method does not output the imaging positions but directly obtains the realistic positions, i.e. plate numbers, of the faulty plates. By introducing a new convolutional neural network named FlatNet, no extra work is required to get the realistic positions of the faulty plates. To address the data imbalance inherent to multilabel classification, dynamic weights that pay more attention both to the minority class and difficult samples are presented, forming a bilateral constraint on the missed and the false detections. At the end of the method, we design a greedy ensemble approach driven by validation F1-scores for the promotion of detection performance and stability. Experiments conducted with real-world data verify the effectiveness of the proposed fault detection method.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":null,"pages":null},"PeriodicalIF":8.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An ensembled multilabel classification method for the short-circuit detection of electrolytic refining\",\"authors\":\"\",\"doi\":\"10.1016/j.aei.2024.102919\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Short-circuits occurring in the electrolytic refining process of non-ferrous smelting are a main factor that consumes extra energy and affects the metal quality. This paper proposes an ensembled multilabel classification method for short-circuit detection based on infrared images and makes up for the defect of previous methods using object-detection neural networks being hard to directly apply in industrial sites. Different from the object-detection methods, the multilabel classification method does not output the imaging positions but directly obtains the realistic positions, i.e. plate numbers, of the faulty plates. By introducing a new convolutional neural network named FlatNet, no extra work is required to get the realistic positions of the faulty plates. To address the data imbalance inherent to multilabel classification, dynamic weights that pay more attention both to the minority class and difficult samples are presented, forming a bilateral constraint on the missed and the false detections. At the end of the method, we design a greedy ensemble approach driven by validation F1-scores for the promotion of detection performance and stability. Experiments conducted with real-world data verify the effectiveness of the proposed fault detection method.</div></div>\",\"PeriodicalId\":50941,\"journal\":{\"name\":\"Advanced Engineering Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.0000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced Engineering Informatics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1474034624005706\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Engineering Informatics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1474034624005706","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
摘要
有色金属冶炼的电解精炼过程中发生的短路是消耗额外能源和影响金属质量的主要因素。本文提出了一种基于红外图像的短路检测集合多标签分类方法,弥补了以往使用对象检测神经网络的方法难以直接应用于工业现场的缺陷。与物体检测方法不同的是,多标签分类方法不输出成像位置,而是直接获取故障车牌的实际位置,即车牌号码。通过引入名为 FlatNet 的新卷积神经网络,无需额外工作即可获得故障车牌的实际位置。为了解决多标签分类中固有的数据不平衡问题,我们提出了同时关注少数类和困难样本的动态权重,从而对漏检和误检形成了双边约束。在方法的最后,我们设计了一种由验证 F1 分数驱动的贪婪集合方法,以提高检测性能和稳定性。利用真实世界数据进行的实验验证了所提出的故障检测方法的有效性。
An ensembled multilabel classification method for the short-circuit detection of electrolytic refining
Short-circuits occurring in the electrolytic refining process of non-ferrous smelting are a main factor that consumes extra energy and affects the metal quality. This paper proposes an ensembled multilabel classification method for short-circuit detection based on infrared images and makes up for the defect of previous methods using object-detection neural networks being hard to directly apply in industrial sites. Different from the object-detection methods, the multilabel classification method does not output the imaging positions but directly obtains the realistic positions, i.e. plate numbers, of the faulty plates. By introducing a new convolutional neural network named FlatNet, no extra work is required to get the realistic positions of the faulty plates. To address the data imbalance inherent to multilabel classification, dynamic weights that pay more attention both to the minority class and difficult samples are presented, forming a bilateral constraint on the missed and the false detections. At the end of the method, we design a greedy ensemble approach driven by validation F1-scores for the promotion of detection performance and stability. Experiments conducted with real-world data verify the effectiveness of the proposed fault detection method.
期刊介绍:
Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.