Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi-Information Fusion
Due to the complexity of the structure and process of large-scale petrochemical equipment, different fault characteristics are mixed and present multiple couplings and ambiguities, leading to the difficulty in identifying composite faults in rotating machinery. This paper proposes a composite faults...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/1982317 |
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| _version_ | 1832549822795612160 |
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| author | Naiquang Su Xiao Li Qinghua Zhang Zhiqiang Huo |
| author_facet | Naiquang Su Xiao Li Qinghua Zhang Zhiqiang Huo |
| author_sort | Naiquang Su |
| collection | DOAJ |
| description | Due to the complexity of the structure and process of large-scale petrochemical equipment, different fault characteristics are mixed and present multiple couplings and ambiguities, leading to the difficulty in identifying composite faults in rotating machinery. This paper proposes a composite faults diagnosis method for rotating machinery of the large unit based on evidence theory and multi-information fusion. The evidence theory and multi-information fusion method mainly deal with multisource information and conflict information, synthesize multiple uncertain information, and obtain synthetic information from multiple data sources. To detect faults in rotating machinery, the dimensionless index ranges of composite faults are first used to form a feature set as the reference. Then, a two-sample distribution test is applied to compare the known fault samples with the tested fault samples, and the maximum statistical distance is used. Finally, the multiple maximum statistical distances are fused by evidence theory and identifying fault types based on the fusion result. The proposed method was applied to the large petrochemical unit simulation experiment system, the results of which showed that our proposed method could accurately identify composite faults and provide maintenance guidance for composite fault diagnosis. |
| format | Article |
| id | doaj-art-c9cd4e6449ff4511af082fd515ed57ac |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-c9cd4e6449ff4511af082fd515ed57ac2025-02-03T06:08:34ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/19823171982317Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi-Information FusionNaiquang Su0Xiao Li1Qinghua Zhang2Zhiqiang Huo3School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaGuangdong Province Key Laboratory of Petrochemical Equipment Fault Diagnosis, Maoming 525000, ChinaSchool of Engineering, University of Lincoln, Lincoln LN6 7TS, UKDue to the complexity of the structure and process of large-scale petrochemical equipment, different fault characteristics are mixed and present multiple couplings and ambiguities, leading to the difficulty in identifying composite faults in rotating machinery. This paper proposes a composite faults diagnosis method for rotating machinery of the large unit based on evidence theory and multi-information fusion. The evidence theory and multi-information fusion method mainly deal with multisource information and conflict information, synthesize multiple uncertain information, and obtain synthetic information from multiple data sources. To detect faults in rotating machinery, the dimensionless index ranges of composite faults are first used to form a feature set as the reference. Then, a two-sample distribution test is applied to compare the known fault samples with the tested fault samples, and the maximum statistical distance is used. Finally, the multiple maximum statistical distances are fused by evidence theory and identifying fault types based on the fusion result. The proposed method was applied to the large petrochemical unit simulation experiment system, the results of which showed that our proposed method could accurately identify composite faults and provide maintenance guidance for composite fault diagnosis.http://dx.doi.org/10.1155/2019/1982317 |
| spellingShingle | Naiquang Su Xiao Li Qinghua Zhang Zhiqiang Huo Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi-Information Fusion Shock and Vibration |
| title | Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi-Information Fusion |
| title_full | Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi-Information Fusion |
| title_fullStr | Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi-Information Fusion |
| title_full_unstemmed | Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi-Information Fusion |
| title_short | Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi-Information Fusion |
| title_sort | composite fault diagnosis for rotating machinery of large units based on evidence theory and multi information fusion |
| url | http://dx.doi.org/10.1155/2019/1982317 |
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