Deep Learning Technique for Image Colorization

Pradhan, Nitesh; Dhaka, Vijaypal Singh; Thakur, Shivam; Bhakar, Suman

doi:10.1145/3484824.3484902

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…With the rapid iterative progress of data mining, deep learning, and other technologies, various data-driven algorithms to complete feature extraction from initial data have been removed from the subjective influence of empirical bearing fault diagnosis by human experts, greatly improving diagnostic accuracy, and becoming a research hotspot of domestic and foreign scholars [1][2][3][4][5][6][7][8][9][10] . The deep learning method of rolling bearing fault diagnosis usually includes three modules: original vibration signal acquisition and the processing module, fault feature extraction module, and fault diagnosis classification module [11][12][13][14][15][16] .…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing intelligent faults diagnosis based on deep convolutional neural network

Wang

Ding

et al. 2023

International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022)

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In the processing, manufacturing, and production of modern fields, rolling bearings, the most basic module of most mechanical equipment, have a key role that cannot be ignored. This paper proposes three fault diagnosis model architectures for rolling bearings based on the deep convolutional neural network. Three models were tested on the industry-common Case Western Reserve University Dataset (CWRU). The original vibration signal acquisition and processing module mainly uses the vibration signal window translation method to complete the segmentation of overlapping signals and uses the Inception network to efficiently complete one-dimensional signal preprocessing. Finally, we use t-distributed stochastic neighbor embedding (t-SNE) to reduce the dimension and visualize the learned fault data distribution.

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