東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Graph Neural Networks Based on Multi...

Zhu, Guanhua.

FindBook

Google Book

Amazon

博客來

Graph Neural Networks Based on Multi-Rate Signal Decomposition for Bearing Fault Diagnosis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Graph Neural Networks Based on Multi-Rate Signal Decomposition for Bearing Fault Diagnosis./
作者:	Zhu, Guanhua.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	50 p.
附註:	Source: Masters Abstracts International, Volume: 85-01.
Contained By:	Masters Abstracts International85-01.
標題:	Deep learning. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30540010
ISBN:	9798379881849

Graph Neural Networks Based on Multi-Rate Signal Decomposition for Bearing Fault Diagnosis.
Zhu, Guanhua.

Graph Neural Networks Based on Multi-Rate Signal Decomposition for Bearing Fault Diagnosis. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 50 p.

Source: Masters Abstracts International, Volume: 85-01.

Thesis (M.Sc.)--Purdue University, 2023.

Roller bearings are the common components used in the mechanical systems for mechanical processing and production. The running state of roller bearings often determines the machining accuracy and productivity on a manufacturing line. Roller bearing failure may lead to the shutdown of production lines, resulting in serious economic losses. Therefore, the research on roller bearing fault diagnosis has a great value. This thesis research first proposes a method of signal frequency spectral resampling to tackle the problem of bearing fault detection at different rotating speeds using a single speed dataset for training the network such as the one dimensional convolutional neural network (1D CNN). Second, this research work proposes a technique to connect the graph structures constructed from spectral components of the different bearing fault frequency bands into a sparse graph structure, so that the fault identification can be carried out effectively through a graph neural network in terms of the computation load and classification rate. Finally, the frequency spectral resampling method for feature extraction is validated using our self-collected datasets. The performance of the graph neural network with our proposed sparse graph structure is validated using the Case Western Reserve University (CWRU) dataset as well as our self-collected datasets. The results show that our proposed method achieves higher bearing fault classification accuracy than those recently proposed by other researchers using machine learning approaches and neural networks.

ISBN: 9798379881849Subjects--Topical Terms:

3554982
Deep learning.

Graph Neural Networks Based on Multi-Rate Signal Decomposition for Bearing Fault Diagnosis.
LDR:02633nmm a2200373 4500 001 2400812
005 20240930130039.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798379881849
035 $a (MiAaPQ)AAI30540010
035 $a (MiAaPQ)Purdue22806956
035 $a AAI30540010
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhu, Guanhua. $3 3770872
245 1 0 $a Graph Neural Networks Based on Multi-Rate Signal Decomposition for Bearing Fault Diagnosis.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 50 p.
500 $a Source: Masters Abstracts International, Volume: 85-01.
500 $a Advisor: Tan, Lizhe.
502 $a Thesis (M.Sc.)--Purdue University, 2023.
520 $a Roller bearings are the common components used in the mechanical systems for mechanical processing and production. The running state of roller bearings often determines the machining accuracy and productivity on a manufacturing line. Roller bearing failure may lead to the shutdown of production lines, resulting in serious economic losses. Therefore, the research on roller bearing fault diagnosis has a great value. This thesis research first proposes a method of signal frequency spectral resampling to tackle the problem of bearing fault detection at different rotating speeds using a single speed dataset for training the network such as the one dimensional convolutional neural network (1D CNN). Second, this research work proposes a technique to connect the graph structures constructed from spectral components of the different bearing fault frequency bands into a sparse graph structure, so that the fault identification can be carried out effectively through a graph neural network in terms of the computation load and classification rate. Finally, the frequency spectral resampling method for feature extraction is validated using our self-collected datasets. The performance of the graph neural network with our proposed sparse graph structure is validated using the Case Western Reserve University (CWRU) dataset as well as our self-collected datasets. The results show that our proposed method achieves higher bearing fault classification accuracy than those recently proposed by other researchers using machine learning approaches and neural networks.
590 $a School code: 0183.
650 4 $a Deep learning. $3 3554982
650 4 $a Frequency distribution. $3 3695360
650 4 $a Fault diagnosis. $3 3555094
650 4 $a Fourier transforms. $3 3545926
650 4 $a Bearings. $3 3696649
650 4 $a Contact angle. $3 3320098
650 4 $a Neural networks. $3 677449
650 4 $a Signal processing. $3 533904
650 4 $a Electrical engineering. $3 649834
650 4 $a Mathematics. $3 515831
650 4 $a Mechanical engineering. $3 649730
650 4 $a Statistics. $3 517247
690 $a 0800
690 $a 0544
690 $a 0338
690 $a 0405
690 $a 0548
690 $a 0463
710 2 $a Purdue University. $3 1017663
773 0 $t Masters Abstracts International $g 85-01.
790 $a 0183
791 $a M.Sc.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30540010