Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data

• URL: http://arxiv.org/abs/2210.11164v1
• Date: Thu, 20 Oct 2022 11:03:21 GMT
• Title: Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data
• Authors: Alexander Kovalenko, Vitaliy Pozdnyakov, Ilya Makarov
• Abstract summary: It is necessary to consider the behavior of the signals in each sensor separately, to take into account their correlation and hidden relationships with each other. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance.
• Score: 69.25738064847175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Timely detected anomalies in the chemical technological processes, as well as the earliest detection of the cause of the fault, significantly reduce the production cost in the industrial factories. Data on the state of the technological process and the operation of production equipment are received by a large number of different sensors. To better predict the behavior of the process and equipment, it is necessary not only to consider the behavior of the signals in each sensor separately, but also to take into account their correlation and hidden relationships with each other. Graph-based data representation helps with this. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. In this work, the possibility of applying graph neural networks to the problem of fault diagnosis in a chemical process is studied. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance. In this work, several methods for obtaining adjacency matrices were considered, as well as their quality was studied. It has also been proposed to use multiple adjacency matrices in one model. We showed state-of-the-art performance on the fault diagnosis task with the Tennessee Eastman Process dataset. The proposed graph neural networks outperformed the results of recurrent neural networks.

Related papers

• Data-Driven Fire Modeling: Learning First Arrival Times and Model Parameters with Neural Networks [12.416949154231714]
  We investigate the ability of neural networks to parameterize dynamics in fire science. In particular, we investigate neural networks that map five key parameters in fire spread to the first arrival time. For the inverse problem, we quantify the network's sensitivity in estimating each of the key parameters.
  arXiv  Detail & Related papers  (2024-08-16T19:54:41Z)
• Data Augmentation for Supervised Graph Outlier Detection with Latent Diffusion Models [39.33024157496401]
  We introduce GODM, a novel data augmentation for mitigating class imbalance in supervised graph outlier detection with latent Diffusion Models. Our proposed method consists of three key components: (1) Variantioanl maps the heterogeneous information inherent within the graph data into a unified latent space, (2) Graph Generator synthesizes graph data that are statistically similar to real outliers from latent space, and (3) Latent Diffusion Model learns the latent space distribution of real organic data by iterative denoising.
  arXiv  Detail & Related papers  (2023-12-29T16:50:40Z)
• Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs [122.06927400759021]
  We study the relationship between a graph neural network (GNN) and a manifold neural network (MNN) when the graph is constructed from a set of points sampled from the manifold. We prove non-asymptotic error bounds showing that convolutional filters and neural networks on these graphs converge to convolutional filters and neural networks on the continuous manifold.
  arXiv  Detail & Related papers  (2023-05-29T08:27:17Z)
• Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices) [10.616061367794385]
  We propose a two-way interaction whereby neural-network-empowered machines can expose the underpinning learnt causal graphs. We show that our method improves predictive performance up to 2.4x while producing parsimonious networks, up to 7x smaller in the input layer.
  arXiv  Detail & Related papers  (2022-05-19T18:21:12Z)
• Learning Graph Structure from Convolutional Mixtures [119.45320143101381]
  We propose a graph convolutional relationship between the observed and latent graphs, and formulate the graph learning task as a network inverse (deconvolution) problem. In lieu of eigendecomposition-based spectral methods, we unroll and truncate proximal gradient iterations to arrive at a parameterized neural network architecture that we call a Graph Deconvolution Network (GDN) GDNs can learn a distribution of graphs in a supervised fashion, perform link prediction or edge-weight regression tasks by adapting the loss function, and they are inherently inductive.
  arXiv  Detail & Related papers  (2022-05-19T14:08:15Z)
• Graph Neural Network-based Early Bearing Fault Detection [0.18275108630751835]
  A novel graph neural network-based fault detection method is proposed. It builds a bridge between AI and real-world running mechanical systems. We find that the proposed method can successfully detect faulty objects that are mixed in the normal object region.
  arXiv  Detail & Related papers  (2022-04-24T08:54:55Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• A complex network approach to time series analysis with application in diagnosis of neuromuscular disorders [1.9659095632676098]
  This paper proposes a new approach to network development named GraphTS to overcome the limited accuracy of existing methods. For this purpose, EMG signals are pre-processed and mapped to a complex network by a standard visibility graph algorithm. The resulting networks can differentiate between healthy and patient samples.
  arXiv  Detail & Related papers  (2021-08-16T06:44:48Z)
• An Introduction to Robust Graph Convolutional Networks [71.68610791161355]
  We propose a novel Robust Graph Convolutional Neural Networks for possible erroneous single-view or multi-view data. By incorporating an extra layers via Autoencoders into traditional graph convolutional networks, we characterize and handle typical error models explicitly.
  arXiv  Detail & Related papers  (2021-03-27T04:47:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.