Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration Data

• URL: http://arxiv.org/abs/2108.03288v1
• Date: Fri, 6 Aug 2021 20:04:29 GMT
• Title: Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration Data
• Authors: Atik Faysal, Ngui Wai Keng, M. H. Lim
• Abstract summary: Time-series data are one of the fundamental types of raw data representation used in data-driven techniques. Deep Neural Networks (DNNs) require huge labeled training samples to reach their optimum performance. In this study, a data augmentation technique named ensemble augmentation is proposed to overcome this limitation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time-series data are one of the fundamental types of raw data representation used in data-driven techniques. In machine condition monitoring, time-series vibration data are overly used in data mining for deep neural networks. Typically, vibration data is converted into images for classification using Deep Neural Networks (DNNs), and scalograms are the most effective form of image representation. However, the DNN classifiers require huge labeled training samples to reach their optimum performance. So, many forms of data augmentation techniques are applied to the classifiers to compensate for the lack of training samples. However, the scalograms are graphical representations where the existing augmentation techniques suffer because they either change the graphical meaning or have too much noise in the samples that change the physical meaning. In this study, a data augmentation technique named ensemble augmentation is proposed to overcome this limitation. This augmentation method uses the power of white noise added in ensembles to the original samples to generate real-like samples. After averaging the signal with ensembles, a new signal is obtained that contains the characteristics of the original signal. The parameters for the ensemble augmentation are validated using a simulated signal. The proposed method is evaluated using 10 class bearing vibration data using three state-of-the-art Transfer Learning (TL) models, namely, Inception-V3, MobileNet-V2, and ResNet50. Augmented samples are generated in two increments: the first increment generates the same number of fake samples as the training samples, and in the second increment, the number of samples is increased gradually. The outputs from the proposed method are compared with no augmentation, augmentations using deep convolution generative adversarial network (DCGAN), and several geometric transformation-based augmentations...

Related papers

• cDVGAN: One Flexible Model for Multi-class Gravitational Wave Signal and Glitch Generation [0.7853804618032806]
  We present a novel conditional model in the Generative Adrial Network framework for simulating multiple classes of time-domain observations. Our proposed cDVGAN outperforms 4 different baseline GAN models in replicating the features of the three classes. Our experiments show that training convolutional neural networks with our cDVGAN-generated data improves the detection of samples embedded in detector noise.
  arXiv  Detail & Related papers  (2024-01-29T17:59:26Z)
• Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy [55.014926694758195]
  Entropy and mutual information in neural networks provide rich information on the learning process. We leverage data geometry to access the underlying manifold and reliably compute these information-theoretic measures. We show that they form noise-resistant measures of intrinsic dimensionality and relationship strength in high-dimensional simulated data.
  arXiv  Detail & Related papers  (2023-12-04T01:32:42Z)
• LD-GAN: Low-Dimensional Generative Adversarial Network for Spectral Image Generation with Variance Regularization [72.4394510913927]
  Deep learning methods are state-of-the-art for spectral image (SI) computational tasks. GANs enable diverse augmentation by learning and sampling from the data distribution. GAN-based SI generation is challenging since the high-dimensionality nature of this kind of data hinders the convergence of the GAN training yielding to suboptimal generation. We propose a statistical regularization to control the low-dimensional representation variance for the autoencoder training and to achieve high diversity of samples generated with the GAN.
  arXiv  Detail & Related papers  (2023-04-29T00:25:02Z)
• ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data [93.06336507035486]
  Generative Adversarial Networks (GANs) typically suffer from overfitting when limited training data is available. We present ScoreMix, a novel and scalable data augmentation approach for various image synthesis tasks.
  arXiv  Detail & Related papers  (2022-10-27T02:55:15Z)
• Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise [51.76329821186873]
  We produce a model that can classify six different hand gestures with a limited number of samples that generalizes well to a wider audience. We appeal to a set of more elementary methods such as the use of random bounds on a signal, but desire to show the power these methods can carry in an online setting.
  arXiv  Detail & Related papers  (2022-06-29T23:22:18Z)
• ReSmooth: Detecting and Utilizing OOD Samples when Training with Data Augmentation [57.38418881020046]
  Recent DA techniques always meet the need for diversity in augmented training samples. An augmentation strategy that has a high diversity usually introduces out-of-distribution (OOD) augmented samples. We propose ReSmooth, a framework that firstly detects OOD samples in augmented samples and then leverages them.
  arXiv  Detail & Related papers  (2022-05-25T09:29:27Z)
• Feature transforms for image data augmentation [74.12025519234153]
  In image classification, many augmentation approaches utilize simple image manipulation algorithms. In this work, we build ensembles on the data level by adding images generated by combining fourteen augmentation approaches. Pretrained ResNet50 networks are finetuned on training sets that include images derived from each augmentation method.
  arXiv  Detail & Related papers  (2022-01-24T14:12:29Z)
• Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms [8.747840760772268]
  We show the impact of data augmentation on the binary classification task of surgical mask detection in samples of human voice. Results show that most of the baselines given by ComParE are outperformed.
  arXiv  Detail & Related papers  (2020-08-11T09:02:47Z)
• Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction [15.279153483132179]
  We use fully convolutional neural networks for semantic segmentation of eye tracking data. We also use these networks for reconstruction, and in conjunction with a variational auto-encoder to generate eye movement data.
  arXiv  Detail & Related papers  (2020-02-17T06:57:09Z)

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.