Bearing Fault Diagnosis using Graph Sampling and Aggregation Network

• URL: http://arxiv.org/abs/2408.07099v1
• Date: Mon, 12 Aug 2024 12:32:03 GMT
• Title: Bearing Fault Diagnosis using Graph Sampling and Aggregation Network
• Authors: Jiaying Chen, Xusheng Du, Yurong Qian, Gwanggil Jeon,
• Abstract summary: Timely and accurate detection of bearing faults plays an important role in preventing catastrophic accidents and ensuring product quality. Traditional signal analysis techniques and deep learning-based fault detection algorithms do not take into account the intricate correlation between signals. We propose GraphSAGE-based Bearing fault Diagnosis (GSABFD) algorithm.
• Score: 7.9129510166723085
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Bearing fault diagnosis technology has a wide range of practical applications in industrial production, energy and other fields. Timely and accurate detection of bearing faults plays an important role in preventing catastrophic accidents and ensuring product quality. Traditional signal analysis techniques and deep learning-based fault detection algorithms do not take into account the intricate correlation between signals, making it difficult to further improve detection accuracy. To address this problem, we introduced Graph Sampling and Aggregation (GraphSAGE) network and proposed GraphSAGE-based Bearing fault Diagnosis (GSABFD) algorithm. The original vibration signal is firstly sliced through a fixed size non-overlapping sliding window, and the sliced data is feature transformed using signal analysis methods; then correlations are constructed for the transformed vibration signal and further transformed into vertices in the graph; then the GraphSAGE network is used for training; finally the fault level of the object is calculated in the output layer of the network. The proposed algorithm is compared with five advanced algorithms in a real-world public dataset for experiments, and the results show that the GSABFD algorithm improves the AUC value by 5% compared with the next best algorithm.

Related papers

• Rolling bearing fault diagnosis method based on generative adversarial enhanced multi-scale convolutional neural network model [7.600902237804825]
  A rolling bearing fault diagnosis method based on generative adversarial enhanced multi-scale convolutional neural network model is proposed. Compared with ResNet method, the experimental results show that the proposed method has better generalization performance and anti-noise performance.
  arXiv  Detail & Related papers  (2024-03-21T06:42:35Z)
• An Explainable Deep Learning-Based Method For Schizophrenia Diagnosis Using Generative Data-Augmentation [0.3222802562733786]
  We leverage a deep learning-based method for the automatic diagnosis of schizophrenia using EEG brain recordings. This approach utilizes generative data augmentation, a powerful technique that enhances the accuracy of the diagnosis.
  arXiv  Detail & Related papers  (2023-10-25T12:55:16Z)
• Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data [69.25738064847175]
  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.
  arXiv  Detail & Related papers  (2022-10-20T11:03:21Z)
• 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)
• 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)
• SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network [79.04274563889548]
  We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
  arXiv  Detail & Related papers  (2021-06-10T04:21:20Z)
• A Hierarchical Graph Signal Processing Approach to Inference from Spatiotemporal Signals [14.416786768268233]
  Motivated by the emerging area of graph signal processing (GSP), we introduce a novel method to draw inference from signals. In this paper we leverage techniques to develop a hierarchical feature extraction approach. We test our approach on the intracranial EEG (iEEG) data set of the K aggle seizure detection contest.
  arXiv  Detail & Related papers  (2020-10-25T17:08:13Z)
• FaultNet: A Deep Convolutional Neural Network for bearing fault classification [7.148679715851955]
  We analyze vibration signal data of mechanical systems with bearings by combining different signal processing methods. We propose a convolutional neural network FaultNet which can effectively determine the type of bearing fault with a high degree of accuracy.
  arXiv  Detail & Related papers  (2020-10-05T16:50:08Z)
• Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning [64.98816284854067]
  Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. A novel GCN-based SSL algorithm is presented in this paper to enrich the supervision signals by utilizing both data similarities and graph structure.
  arXiv  Detail & Related papers  (2020-09-15T13:59:28Z)
• Offline detection of change-points in the mean for stationary graph signals [55.98760097296213]
  We propose an offline method that relies on the concept of graph signal stationarity. Our detector comes with a proof of a non-asymptotic inequality oracle.
  arXiv  Detail & Related papers  (2020-06-18T15:51:38Z)
• Data-Driven Factor Graphs for Deep Symbol Detection [107.63351413549992]
  We propose to implement factor graph methods in a data-driven manner. In particular, we propose to use machine learning (ML) tools to learn the factor graph. We demonstrate that the proposed system, referred to as BCJRNet, learns to implement the BCJR algorithm from a small training set.
  arXiv  Detail & Related papers  (2020-01-31T09:23:52Z)

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.