A Machine Vision Anomaly Detection System to Industry 4.0 Based on Variational Fuzzy Autoencoder.
A Machine Vision Anomaly Detection System to Industry 4.0 Based on Variational Fuzzy Autoencoder.
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DOI：
10.1155/2022/1945507
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发表时间：
2022
影响因子：
--
通讯作者：
Jiang W
中科院分区：
文献类型：
--
作者：
Jiang W
从技术的角度来看，Industry 4.0在一个智能环境中发展和运行，在这个环境中，现实世界和虚拟世界通过智能的网络物理系统结合在一起。这些相互控制的设备自动激活创新功能，增强生产过程。然而，集成了最现代数字自动化和信息技术的工业环境是大规模定向网络攻击的理想目标。在工业4.0生态系统中实施集成而有效的安全战略，前提是定期进行垂直检查流程，以应对整个生产线中的任何新威胁和漏洞。在提出这一观点的同时，所有利益攸关方都坚信，现代工业基础设施的所有系统都是网络攻击的潜在目标，机电系统稍有调整就可能导致广泛的损失。因此，鉴于在设计充分确保有关基础设施的安全战略方面没有灵丹妙药，应采用先进的高级别解决方案，在不直接依赖人力资源的情况下有效地实施安全边界。在Industry 4.0中，主动网络安全最重要的方法之一是检测异常，即识别不符合流程预期模式的对象、观察、事件或行为。这项工作的主题是利用先进的机器视觉方法识别因网络攻击而导致的生产线缺陷。提出了一种新颖的变分模糊自动编码(VFA)方法。利用模糊熵和欧几里得模糊相似性度量，最大限度地提高了通过确定性函数进行非线性变换的可能性，从而创建了完全逼真的视觉系统。最后的结论是，所提出的系统可以在高度复杂的环境中对异常进行评估和分类，并具有很高的准确性。
From a technological point of view, Industry 4.0 evolves and operates in a smart environment in which the real and virtual worlds come together through smart cyber-physical systems. These devices that control each other autonomously activate innovative functions that enhance the production process. However, the industrial environment in which the most modern digital automation and information technologies are integrated is an ideal target for large-scale targeted cyberattacks. Implementing an integrated and effective security strategy in the Industrial 4.0 ecosystem presupposes a vertical inspection process at regular intervals to address any new threats and vulnerabilities throughout the production line. This view should be accompanied by the deep conviction of all stakeholders that all systems of modern industrial infrastructure are a potential target of cyberattacks and that the slightest rearrangement of mechatronic systems can lead to generalized losses. Accordingly, given that there is no panacea in designing a security strategy that fully ensures the infrastructure in question, advanced high-level solutions should be adopted, effectively implementing security perimeters without direct dependence on human resources. One of the most important methods of active cybersecurity in Industry 4.0 is the detection of anomalies, i.e., the identification of objects, observations, events, or behaviors that do not conform to the expected pattern of a process. The theme of this work is the identification of defects in the production line resulting from cyberattacks with advanced machine vision methods. An original variational fuzzy autoencoder (VFA) methodology is proposed. Using fuzzy entropy and Euclidean fuzzy similarity measurement maximizes the possibility of using nonlinear transformation through deterministic functions, thus creating an entirely realistic vision system. The final finding is that the proposed system can evaluate and categorize anomalies in a highly complex environment with significant accuracy.
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影响因子：
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Potter S