A graph embedding based fault detection framework for process systems with multi-variate time-series datasets

Abstract

Due to the enormous potential of modelling, graph-based approaches have been used for various applications in the process industries. In this study, we propose a fault detection framework through graphs by utilising its attributes in the form of node embeddings. Shallow embedding methods are deployed to generate node embedding vectors. Shallow embedding methods are broadly classified into matrix factorisation and skip-gram-based methods. Node2vec and Deepwalk fall under skip-gram models, while GraphRep and HOPE constitute the Matrix factorisation methods. Node embedding values generated from these methods are then fed to the variational auto-encoder, which ranks the nodes in reconstruction loss value. The node embedding reconstruction loss values exceeding a particular threshold are considered outliers. The proposed work has been validated on NPCIL power-flux data and the benchmark Tennessee Eastman data. The results indicate that skip-gram models, especially Node2vec-VAE, outperformed the matrix factorisation methods for both the above-mentioned datasets.

Keywords

• Graph machine learning
• Fault detection
• Embeddings
• Variational auto-encoder
• Reconstruction loss