利用基于拼图的数据扩展克服数据失衡和缺陷的异常检测

IF 2.1 3区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Machines Pub Date : 2023-11-20 DOI:10.3390/machines11111034
Eunkyeong Kim, Seunghwan Jung, Minseok Kim, Jinyong Kim, Baekcheon Kim, Jonggeun Kim, Sungshin Kim
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引用次数: 0

摘要

机床用途广泛,可以灵活地制造工件。此外,机床还需要维护;总体成本包括维护成本(占很大一部分)和确保产品质量的成本。因此,需要对工具状况进行异常检测,因为这些工具是必不可少的工业要素。然而,与工具条件相关的数据存在一些挑战:数据不平衡和缺陷。数据不平衡和缺陷会影响异常检测模型的性能。使用不平衡和缺陷数据训练的模型可能会误判异常数据为正常数据,从而导致错误。为了克服这些问题,我们设计了一种利用小波变换、色彩空间转换、色彩提取、基于拼图的数据增强和双重迁移学习的方法。所提出的方法从时间序列数据中生成图像数据,有效提取特征,并利用基于拼图的数据增强生成新的图像数据。对颜色信息进行处理以突出特征,并在处理过程中应用所提出的基于拼图的数据增强技术来增加数据量,从而提高异常检测模型的性能。实验结果表明,所提出的方法能更准确地对正常数据和异常数据进行分类。其中,异常数据分类的准确率从 25.00% 提高到 91.67%。这表明所提出的方法是有效的,可以克服数据不平衡和缺陷。
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Anomaly Detection Using Puzzle-Based Data Augmentation to Overcome Data Imbalances and Deficiencies
Machine tools are used in a wide range of applications, and they can manufacture workpieces flexibly. Furthermore, they require maintenance; the overall costs include maintenance costs, which constitute a significant portion, and the costs involved in ensuring product quality. Therefore, anomaly detection in tool conditions is required, because these tools are essential industrial elements. However, the data related to tool conditions present some challenges: data imbalances and deficiencies. Data imbalances and deficiencies can affect the performance of anomaly detection models. A model trained using data with imbalances and deficiencies may miscalculate that abnormal data are normal data, leasing to errors. To overcome these problems, the proposed method has been designed using the wavelet transform, color space conversion, color extraction, puzzle-based data augmentation, and double transfer learning. The proposed method generated image data from time-series data, effectively extracted features, and generated new image data using puzzle-based data augmentation. The color information was processed to highlight features, and the proposed puzzle-based data augmentation was applied during processing to increase the amount of data to improve the performance of the anomaly detection model. The experimental results showed that the proposed method can classify normal and abnormal data with greater accuracy. In particular, the accuracy of abnormal data classification increased from 25.00% to 91.67%. This demonstrates that the proposed method is effective and can overcome data imbalances and deficiencies.
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来源期刊
Machines
Machines Multiple-
CiteScore
3.00
自引率
26.90%
发文量
1012
审稿时长
11 weeks
期刊介绍: Machines (ISSN 2075-1702) is an international, peer-reviewed journal on machinery and engineering. It publishes research articles, reviews, short communications and letters. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. Full experimental and/or methodical details must be provided. There are, in addition, unique features of this journal: *manuscripts regarding research proposals and research ideas will be particularly welcomed *electronic files or software regarding the full details of the calculation and experimental procedure - if unable to be published in a normal way - can be deposited as supplementary material Subject Areas: applications of automation, systems and control engineering, electronic engineering, mechanical engineering, computer engineering, mechatronics, robotics, industrial design, human-machine-interfaces, mechanical systems, machines and related components, machine vision, history of technology and industrial revolution, turbo machinery, machine diagnostics and prognostics (condition monitoring), machine design.
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