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

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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.