Journal Article10.1016/j.engappai.2024.108574
High-resolution cross-scale transformer: A deep learning model for bolt loosening detection based on monocular vision measurement
Tianyi Wu,Ke Shang,Wei Dai,Min Wang,Ruiting Liu,Junxian Zhou,Jun Liu +6 more
4
About: This article is published in Engineering Applications of Artificial Intelligence. The article was published on 01 Jul 2024.
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
Preload State Detection of Transmission Tower Bolt Joints Based on Sound Recognition
De-Hong Wang,Zixuan Zhang,Cong Zeng +2 more
Abstract: Timely mastering the preload state of transmission tower bolts is crucial for maintaining the structural performance of transmission towers and preventing the occurrence of tower collapse accidents. A preload state detection method for transmission tower bolt joints based on sound recognition is proposed. Based on the Mel spectrogram, the bolt sound signal features are extracted, and the mapping relationship between the sound signal and the preload state is established after feature fusion; extreme gradient boosting (XGBoost) is introduced as an independent predictor in the deep forest model, and an improved deep forest model is obtained; finally, experiments are conducted on single‐bolt and multibolt joints. The results show that the fusion feature proposed in this paper can effectively support multiple machine learning models. The improved deep forest model has an accuracy of 97.6% and 96.9% in identifying the preload state of single‐bolt and multiple‐bolt joints, respectively, and has a fast detection speed. The model can still achieve accurate classification even in the case of sample imbalance. The recognition accuracy of the method remains above 95% at −2, 0, and 2 dB signal‐to‐noise ratio (SNR) levels, demonstrating excellent noise immunity.
Integrating Virtual Sensor Data Augmentation Into Machine Learning for Damage Quantification of Bolted Structures Under Assembly Uncertainty
J. S. Coelho,M. R. Machado,M. Dutkiewicz +2 more
TL;DR: This study integrates virtual sensor data augmentation into machine learning for damage quantification of bolted structures, enhancing model performance and accuracy in estimating torque loosening, while accounting for assembly uncertainty and providing reliable condition assessment.
Distributed Acoustic Sensing: A Promising Tool for Finger-Band Anomaly Detection
TL;DR: Distributed Acoustic Sensing (DAS) effectively detects finger-band anomalies in straddle-type monorails by analyzing track vibration signals, identifying operating status, pinpointing track structures, and detecting bolt looseness through increased vibration energy.
Identifying bolt-loosening in offshore wind turbine structures utilizing data-fused EMD-PCA approach
Changzi Wang,Dongbo Luo,Zepeng Zheng,Junfeng Du,Yuanzhi Guo,Yufeng Jiang +5 more
References
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
- 06 Sep 2014
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
•Posted Content
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy,Lucas Beyer,Alexander Kolesnikov,Dirk Weissenborn,Xiaohua Zhai,Thomas Unterthiner,Mostafa Dehghani,Matthias Minderer,Georg Heigold,Sylvain Gelly,Jakob Uszkoreit,Neil Houlsby +11 more
TL;DR: Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.
Feature Pyramid Networks for Object Detection
Tsung-Yi Lin,Piotr Dollár,Ross Girshick,Kaiming He,Bharath Hariharan,Serge Belongie +5 more
- 21 Jul 2017
TL;DR: This paper exploits the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost and achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles.
Focal Loss for Dense Object Detection
Tsung-Yi Lin,Priya Goyal,Ross Girshick,Kaiming He,Piotr Dollár +4 more
- 07 Aug 2017
TL;DR: This paper proposes to address the extreme foreground-background class imbalance encountered during training of dense detectors by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples, and develops a novel Focal Loss, which focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Liang-Chieh Chen,Yukun Zhu,George Papandreou,Florian Schroff,Hartwig Adam +4 more
- 08 Sep 2018
TL;DR: This work extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries and applies the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules, resulting in a faster and stronger encoder-decoder network.