Journal Article10.1016/j.asoc.2022.108900
Multi-level features fusion network-based feature learning for machinery fault diagnosis
34
TL;DR: In this article , a multi-scale convolutional neural network (MLFNet) was proposed for feature learning of vibration signals for bearing defect detection. But the performance of the proposed model was not as good as the state-of-the-art methods.
read more
About: This article is published in Applied Soft Computing. The article was published on 01 Jun 2022. The article focuses on the topics: Concatenation (mathematics) & Computer science.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review
TL;DR: In this article , a summary of recent advances in deep learning techniques for industrial fault diagnosis and prognosis is given, which can greatly help to take appropriate actions for maintenance and avoid serious consequences in industrial systems.
Multi-scale and multi-layer perceptron hybrid method for bearings fault diagnosis
TL;DR: In this article , a multi-scale multi-layer perceptron (MSMLP) hybrid bearing fault diagnosis based on complementary ensemble empirical mode decomposition (CEEMD) is proposed, inspired by the successful application of deep networks in the field of computer vision.
39
M2FN: An end-to-end multi-task and multi-sensor fusion network for intelligent fault diagnosis
TL;DR: In this paper , a multi-task multi-sensor fusion network (M2FN) is proposed to improve fault diagnosis performance by using convolutional neural networks to extract and fuse features from raw vibration and current signals.
30
Development and research of triangle-filter convolution neural network for fuel reloading optimization of block-type HTGRs
TL;DR: In this article , a triangle-filter convolution neural network (TFCNN) was proposed to solve the problems of fuel reloading optimization for block-type HTGRs.
25
Online Fault Diagnosis of Industrial Robot Using IoRT and Hybrid Deep Learning Techniques: An Experimental Approach
Hazrat Bilal,Mohammad S. Obaidat,Muhammad Shamrooz Aslam,Jing Zhang,Baocai Yin,Khalid Mahmood +5 more
TL;DR: This study proposes an IoRT architecture using transfer learning and a hybrid 1D-MCNN-RNN technique for online fault diagnosis of industrial robots, achieving 99.03% accuracy in detecting joint faults under varying work conditions.
25
References
Deep Residual Learning for Image Recognition
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
- 07 Jun 2015
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Densely Connected Convolutional Networks
Gao Huang,Zhuang Liu,Laurens van der Maaten,Kilian Q. Weinberger +3 more
- 21 Jul 2017
TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
Fully convolutional networks for semantic segmentation
Jonathan Long,Evan Shelhamer,Trevor Darrell +2 more
- 07 Jun 2015
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
•Posted Content
Squeeze-and-Excitation Networks
TL;DR: Squeeze-and-excitation (SE) as mentioned in this paper adaptively recalibrates channel-wise feature responses by explicitly modeling interdependencies between channels, which can be stacked together to form SENet architectures.
18.9K