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A Jointed Signal Analysis and Convolutional Neural Network Method for Fault Diagnosis
China.
China.
China.
KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.ORCID iD: 0000-0001-8679-8049
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2018 (English)In: Procedia CIRP, Elsevier, 2018, p. 1084-1087Conference paper, Published paper (Refereed)
Abstract [en]

Fault diagnosis plays a vital role in the modern industry. In this research, a joint vibration signal analysis and deep learning method for fault diagnosis is proposed. The vibration signal analysis is a well-established technique for condition monitoring, and deep learning has shown its potential in fault diagnosis. In the proposed method, the time-frequency technique, named as S transform, is applied to transfer the vibration signals to images, and then an improved convolutional neural network (CNN) is applied to classify these images. The results show the proposed method has achieved the significant improvement.

Place, publisher, year, edition, pages
Elsevier, 2018. p. 1084-1087
Keywords [en]
convolutional neural network, Fault diagnosis, time-frequency technique, Condition monitoring, Convolution, Deep learning, Failure analysis, Image enhancement, Manufacture, Mathematical transformations, Neural networks, Signal analysis, Vibration analysis, Convolutional Neural Networks (CNN), Learning methods, S transforms, Time-frequency techniques, Vibration signal, Vibration signal analysis, Well-established techniques, Fault detection
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-236393DOI: 10.1016/j.procir.2018.03.117Scopus ID: 2-s2.0-85049604166OAI: oai:DiVA.org:kth-236393DiVA, id: diva2:1260208
Conference
51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018, 16 May 2018 through 18 May 2018
Note

QC 20181101

Available from: 2018-11-01 Created: 2018-11-01 Last updated: 2018-11-01Bibliographically approved

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Wang, Lihui

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  • de-DE
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  • Other locale
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Output format
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  • asciidoc
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