ENGINE FAULT DIAGNOSIS BASED ON DEEP BELIEF NETWORK IMPROVED BY ADAPTIVE CROW SEARCH ALGORITHM (MT)
Based on the crow search algorithm（CSA）, by the adaptive crow search algorithm（ACSA） the adaptive value strategy of sensing probability and flight distance, which effectively enhanced the performance of the algorithm is designed. In view of the fact that the selection of deep belief network（DBN） mod...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2023-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.02.004 |
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| _version_ | 1841534417564073984 |
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| author | WANG Liang TANG MingWei |
| author_facet | WANG Liang TANG MingWei |
| author_sort | WANG Liang |
| collection | DOAJ |
| description | Based on the crow search algorithm（CSA）, by the adaptive crow search algorithm（ACSA） the adaptive value strategy of sensing probability and flight distance, which effectively enhanced the performance of the algorithm is designed. In view of the fact that the selection of deep belief network（DBN） model parameters has great influence on the engine fault diagnosis results, ACSA is used to optimize the selection of its model parameters, and an engine fault diagnosis method based on DBN improved by ACSA is proposed. The Engine fault diagnosis example results show that the ACSA algorithm can obtain better DBN model parameters, and obtain higher engine fault diagnosis accuracy in less time than other methods. |
| format | Article |
| id | doaj-art-83f383bf52cf432e96a9084b68cdd13d |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2023-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-83f383bf52cf432e96a9084b68cdd13d2025-01-15T02:40:02ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692023-01-0127828336350156ENGINE FAULT DIAGNOSIS BASED ON DEEP BELIEF NETWORK IMPROVED BY ADAPTIVE CROW SEARCH ALGORITHM (MT)WANG LiangTANG MingWeiBased on the crow search algorithm（CSA）, by the adaptive crow search algorithm（ACSA） the adaptive value strategy of sensing probability and flight distance, which effectively enhanced the performance of the algorithm is designed. In view of the fact that the selection of deep belief network（DBN） model parameters has great influence on the engine fault diagnosis results, ACSA is used to optimize the selection of its model parameters, and an engine fault diagnosis method based on DBN improved by ACSA is proposed. The Engine fault diagnosis example results show that the ACSA algorithm can obtain better DBN model parameters, and obtain higher engine fault diagnosis accuracy in less time than other methods.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.02.004Deep belief networkCrow search algorithmAdaptive parameterEngineFault diagnosis |
| spellingShingle | WANG Liang TANG MingWei ENGINE FAULT DIAGNOSIS BASED ON DEEP BELIEF NETWORK IMPROVED BY ADAPTIVE CROW SEARCH ALGORITHM (MT) Jixie qiangdu Deep belief network Crow search algorithm Adaptive parameter Engine Fault diagnosis |
| title | ENGINE FAULT DIAGNOSIS BASED ON DEEP BELIEF NETWORK IMPROVED BY ADAPTIVE CROW SEARCH ALGORITHM (MT) |
| title_full | ENGINE FAULT DIAGNOSIS BASED ON DEEP BELIEF NETWORK IMPROVED BY ADAPTIVE CROW SEARCH ALGORITHM (MT) |
| title_fullStr | ENGINE FAULT DIAGNOSIS BASED ON DEEP BELIEF NETWORK IMPROVED BY ADAPTIVE CROW SEARCH ALGORITHM (MT) |
| title_full_unstemmed | ENGINE FAULT DIAGNOSIS BASED ON DEEP BELIEF NETWORK IMPROVED BY ADAPTIVE CROW SEARCH ALGORITHM (MT) |
| title_short | ENGINE FAULT DIAGNOSIS BASED ON DEEP BELIEF NETWORK IMPROVED BY ADAPTIVE CROW SEARCH ALGORITHM (MT) |
| title_sort | engine fault diagnosis based on deep belief network improved by adaptive crow search algorithm mt |
| topic | Deep belief network Crow search algorithm Adaptive parameter Engine Fault diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.02.004 |
| work_keys_str_mv | AT wangliang enginefaultdiagnosisbasedondeepbeliefnetworkimprovedbyadaptivecrowsearchalgorithmmt AT tangmingwei enginefaultdiagnosisbasedondeepbeliefnetworkimprovedbyadaptivecrowsearchalgorithmmt |