Graph Neural Networks for Anomaly Detection in Multivariate time-series datasets

用于多元时间序列数据集中异常检测的图神经网络

• 批准号: 2892581
• 金额: --
• 依托单位: Queen's University Belfast
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2892581
• 关键词: Graph; Neural; Networks; Anomaly; Detection

Anomalies are integral part of almost every system including manufacturing process. It can cause system fault, delay the manufacturing process, waste of resources and time. One of the key challenges in anomaly detection include defining the precise boundaries between normal/abnormal data points multivariate time series data. The state-of-the-art methods do not explicitly learn the structure of existing relationships, which limits the applicability in many real-world applications. In recent years, deep learning-based graph neural networks have emerged as a successful approach for modelling complex patterns in graph-structured data. The goal of this research is to design and developed the graph neural networks for anomalies detection in manufacturing process. The developed model will be able to identify potential anomalies within the manufacturing data when looking at the graph view of the wafer timelines. The following objectives are set to be achieved during the Ph.D. duration.1. A novel graph neural networks to detect anomalies for multivariate time-series data of manufacturing process.2. Evaluate the proposed approach on the provided commercial graph datasets and comparison with the existing state-of-the-art methods.3. Visualization of the output in a human understandable format.The developed approach will be applied to the area of nano-manufacturing to identify potential issues and defects within the manufacturing process based on a knowledge-graph. It will consider the generated event stream to capture the processes and events created during the construction of a semiconductor wafer. Using these techniques in production will reduce the amount of time that process engineers spend collecting and analysing data for potential issues with wafers as well as being able to provide advanced notification to anomalies within the manufacturing processes which are not easily identified by process engineers looking at single wafer timelines or process steps. The graph neural networks are expected to outperform to solve anomaly detection problem because of its inherent nature of structural relationship association with the data. The outcome of such research can be applied into (a) food-supply chain to detect the anomalies for food security, (b) transportation, and (c) energy consumption patterns in commercial/residential buildings to reduce the energy wastage and contribute to support the mission of climate change.

异常是几乎每个系统的组成部分，包括制造过程。它会导致系统故障，延迟制造过程，浪费资源和时间。异常检测中的一个关键挑战是定义多变量时间序列数据中正常/异常数据点之间的精确边界。最先进的方法没有明确地学习现有关系的结构，这限制了在许多现实世界的应用程序中的适用性。近年来，基于深度学习的图神经网络已经成为一种成功的方法，用于对图结构数据中的复杂模式进行建模。本研究的目的是设计与发展图类神经网路，以应用于制造过程的异常侦测。当查看晶圆时间线的图形视图时，开发的模型将能够识别制造数据中的潜在异常。以下目标将在博士期间实现。持续时间。提出了一种新的图神经网络用于制造过程多变量时间序列数据的异常检测.在提供的商业图数据集上评估所提出的方法，并与现有的最先进的方法进行比较。可视化的输出在一个人可以理解的格式。开发的方法将被应用到纳米制造领域，以确定潜在的问题和缺陷的基础上的知识图的制造过程中。它将考虑生成的事件流来捕获在半导体晶片的构造期间创建的过程和事件。在生产中使用这些技术将减少工艺工程师收集和分析晶圆潜在问题数据的时间，并能够对制造过程中的异常情况提供提前通知，这些异常情况不容易被工艺工程师通过查看单晶片时间表或工艺步骤识别。图神经网络由于其与数据的结构关联的固有性质，有望在解决异常检测问题方面表现出色。这些研究的结果可以应用于（a）食品供应链，以检测食品安全的异常，（B）运输，以及（c）商业/住宅建筑的能源消耗模式，以减少能源浪费，并有助于支持气候变化的使命。