Fault Diagnosis Based on Compressed Sensing of Multisource Data Article Swipe
Xianglong You
,
Zhongwei Deng
,
Kai Zhang
,
Jiacheng Li
,
Hang Yuan
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1344545/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1344545/v1
With the development of the internet of things (IoT) and the application of 5G communication, fault diagnosis of massive multisource sensor data becomes more and more important. In this study, a compressed multisource sensor data-based fault diagnosis scheme is proposed, and its advantages include high data compression and fusion efficiency, low computational cost, and a fast online training sample updating rate. The method includes reference matrix construction, reference matrix compression and fusion, sparse vectors calculation, testing sample reconstruction, and quality evaluation. First, a reference matrix is constructed with labeled multisource sensor data, and each column in the matrix is composed of data samples collected from different sources. Then, the reference matrix is compressed using a measurement matrix, meanwhile, the multisource data samples are fused based on weighted summation during the compression. Later, sparse representation based on batch matching pursuit algorithm is conducted, in this step, the compressed testing sample is represented by the compressed reference matrix, and the output of the sparse representation is a sparse vector. After that, elements in the sparse vector corresponding to different patterns are retained exclusively while other elements are set to zero, respectively, and estimated testing samples are reconstructed with the compressed reference matrix and the processed sparse vector. Finally, based on reconstruction quality evaluation, the pattern of the testing sample is determined. Two cases are employed to validate the effectiveness of the proposed approach, including landfill gas power generator maintenance pattern recognition and multiple redundancy aileron actuator fault diagnosis, and the detection accuracy is 96.13% and 96.67%, respectively.
Related Topics
Concepts
Compressed sensing
Computer science
Redundancy (engineering)
Sparse approximation
Matrix (chemical analysis)
Pattern recognition (psychology)
Sparse matrix
Data mining
Sample (material)
Compression ratio
Sensor fusion
Algorithm
Artificial intelligence
Engineering
Physics
Chromatography
Gaussian
Composite material
Quantum mechanics
Internal combustion engine
Chemistry
Automotive engineering
Operating system
Materials science
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-1344545/v1
- https://www.researchsquare.com/article/rs-1344545/latest.pdf
- OA Status
- green
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221019084
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4221019084Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-1344545/v1Digital Object Identifier
- Title
-
Fault Diagnosis Based on Compressed Sensing of Multisource DataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-03-07Full publication date if available
- Authors
-
Xianglong You, Zhongwei Deng, Kai Zhang, Jiacheng Li, Hang YuanList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-1344545/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-1344545/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-1344545/latest.pdfDirect OA link when available
- Concepts
-
Compressed sensing, Computer science, Redundancy (engineering), Sparse approximation, Matrix (chemical analysis), Pattern recognition (psychology), Sparse matrix, Data mining, Sample (material), Compression ratio, Sensor fusion, Algorithm, Artificial intelligence, Engineering, Physics, Chromatography, Gaussian, Composite material, Quantum mechanics, Internal combustion engine, Chemistry, Automotive engineering, Operating system, Materials scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_date | 2022-03-07 |
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