國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Graph Neural Networks Based on Multi-Rate Signal Decomposition for Bearing Fault Diagnosis./
作者:	Zhu, Guanhua.
面頁冊數:	1 online resource (50 pages)
附註:	Source: Masters Abstracts International, Volume: 85-01.
Contained By:	Masters Abstracts International85-01.
標題:	Statistics. -
電子資源:	click for full text (PQDT)
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. - 1 online resource (50 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379881849Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Graph Neural Networks Based on Multi-Rate Signal Decomposition for Bearing Fault Diagnosis.
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500 $a Advisor: Tan, Lizhe.
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504 $a Includes bibliographical references
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.
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