A Rotating Machinery Fault Diagnosis Method Based on Feature Learning of Thermal Images
Jia,Zhen1; Liu,Zhenbao1; Vong,Chi Man2; Pecht,Michael3,4
Source PublicationIEEE Access
AbstractThe rotating machinery plays a vital role in industrial systems, in which unexpected mechanical faults during operation can lead to severe consequences. For fault prevention, many fault diagnostic methods based on vibration signals are available in the literature. However, the vibration signals are obtained by using different types of sensors, which can cause sensor installation issues and damage the rotating machinery. In addition, this kind of data acquisition through vibration signal induces a large amount of signal noise during machine operation, which will challenge the later fault diagnosis. A recent fault detection method based on infrared thermography (IRT) for rotating machinery avoids these issues. However, the corresponding literature is limited by the fact that the characteristics of the manual design cannot characterize the fault completely so that the diagnostic accuracy cannot exceed the diagnostic method based on the vibration signals. This paper introduces a popular image feature extraction method into the fault diagnosis of rotating machinery based on IRT for the first time. First, capturing the IRT images of the rotating machinery in different states, and then two popular feature extraction methods for IRT images, bag-of-visual-word, and convolutional neural network, are tested in turn. Finally, the extracted features are classified to implement the automatic fault diagnosis. The developed method is applied to analyze the experimental IRT images collected from bearings, and the results demonstrate that the developed method is more effective than the traditional methods based on vibration signals.
Keywordbag-of-visual-words convolutional neural network Fault diagnosis feature recognition infrared thermography
URLView the original
Scopus ID2-s2.0-85061336548
Fulltext Access
Citation statistics
Cited Times [WOS]:20   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorLiu,Zhenbao
Affiliation1.School of Aeronautics,Northwestern Polytechnical University,Xi'an,710072,China
2.Department of Computer and Information Science,University of Macau,Macau,999078,China
3.Center for Advanced Life Cycle Engineering,University of Maryland,College Park,20742,United States
4.Prognostics and Health Management Center,City University of Hong Kong,Hong Kong
Recommended Citation
GB/T 7714
Jia,Zhen,Liu,Zhenbao,Vong,Chi Man,et al. A Rotating Machinery Fault Diagnosis Method Based on Feature Learning of Thermal Images[J]. IEEE Access,2019,7:12348-12359.
APA Jia,Zhen,Liu,Zhenbao,Vong,Chi Man,&Pecht,Michael.(2019).A Rotating Machinery Fault Diagnosis Method Based on Feature Learning of Thermal Images.IEEE Access,7,12348-12359.
MLA Jia,Zhen,et al."A Rotating Machinery Fault Diagnosis Method Based on Feature Learning of Thermal Images".IEEE Access 7(2019):12348-12359.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Jia,Zhen]'s Articles
[Liu,Zhenbao]'s Articles
[Vong,Chi Man]'s Articles
Baidu academic
Similar articles in Baidu academic
[Jia,Zhen]'s Articles
[Liu,Zhenbao]'s Articles
[Vong,Chi Man]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Jia,Zhen]'s Articles
[Liu,Zhenbao]'s Articles
[Vong,Chi Man]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.