THE BIONIC INTELLIGENT FAULT DIAGNOSIS BASED ON COMPREHENSIVE DIFFERENCES MINING FOR HYDRO TURBINE
For the faults of hydropower unit with strong coupling and uncertainty,the traditional fault expression method based on the uniform feature vector is easy to submerge the effect of fault expression and limit the fault diagnosis accuracy and timeliness directly.Given this,we presented a bionic fault...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2020-01-01
|
| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.06.001 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841535830454173696 |
|---|---|
| author | CHEN XiaoYue GE Dang HUANG JiangPing LIU Xiong ZHANG XiaoYan |
| author_facet | CHEN XiaoYue GE Dang HUANG JiangPing LIU Xiong ZHANG XiaoYan |
| author_sort | CHEN XiaoYue |
| collection | DOAJ |
| description | For the faults of hydropower unit with strong coupling and uncertainty,the traditional fault expression method based on the uniform feature vector is easy to submerge the effect of fault expression and limit the fault diagnosis accuracy and timeliness directly.Given this,we presented a bionic fault diagnosis method,which imitates the human brain thinking and proposes the concept of dependent feature vector（DFV）,which logical structure is described by feature selection tree and extracted by feature extraction tree.DFV deeply excavates the intricate relationship between faults by using its nested structure,and realizes the comprehensive mining of the quantity and quality differences between faults of hydroelectric units.It deeply reveals the intrinsic relationship between different faults and faults and features,effectively extracts the essential differences of different faults,and improves the validity of fault expression.Furthermore,the intelligent classification method is realized by combining DFV and probabilistic neural network,the experimental results show that the method can complete the fault diagnosis of hydropower units and achieve excellent diagnostic validity and timeliness. |
| format | Article |
| id | doaj-art-069d4683767e4e8c98ceb1a50b09fb76 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2020-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-069d4683767e4e8c98ceb1a50b09fb762025-01-15T02:26:48ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692020-01-01421271127630609252THE BIONIC INTELLIGENT FAULT DIAGNOSIS BASED ON COMPREHENSIVE DIFFERENCES MINING FOR HYDRO TURBINECHEN XiaoYueGE DangHUANG JiangPingLIU XiongZHANG XiaoYanFor the faults of hydropower unit with strong coupling and uncertainty,the traditional fault expression method based on the uniform feature vector is easy to submerge the effect of fault expression and limit the fault diagnosis accuracy and timeliness directly.Given this,we presented a bionic fault diagnosis method,which imitates the human brain thinking and proposes the concept of dependent feature vector（DFV）,which logical structure is described by feature selection tree and extracted by feature extraction tree.DFV deeply excavates the intricate relationship between faults by using its nested structure,and realizes the comprehensive mining of the quantity and quality differences between faults of hydroelectric units.It deeply reveals the intrinsic relationship between different faults and faults and features,effectively extracts the essential differences of different faults,and improves the validity of fault expression.Furthermore,the intelligent classification method is realized by combining DFV and probabilistic neural network,the experimental results show that the method can complete the fault diagnosis of hydropower units and achieve excellent diagnostic validity and timeliness.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.06.001Hydroelectric generating setDependent feature vectorComprehensive differences miningBionic fault diagnosisFeature selection |
| spellingShingle | CHEN XiaoYue GE Dang HUANG JiangPing LIU Xiong ZHANG XiaoYan THE BIONIC INTELLIGENT FAULT DIAGNOSIS BASED ON COMPREHENSIVE DIFFERENCES MINING FOR HYDRO TURBINE Jixie qiangdu Hydroelectric generating set Dependent feature vector Comprehensive differences mining Bionic fault diagnosis Feature selection |
| title | THE BIONIC INTELLIGENT FAULT DIAGNOSIS BASED ON COMPREHENSIVE DIFFERENCES MINING FOR HYDRO TURBINE |
| title_full | THE BIONIC INTELLIGENT FAULT DIAGNOSIS BASED ON COMPREHENSIVE DIFFERENCES MINING FOR HYDRO TURBINE |
| title_fullStr | THE BIONIC INTELLIGENT FAULT DIAGNOSIS BASED ON COMPREHENSIVE DIFFERENCES MINING FOR HYDRO TURBINE |
| title_full_unstemmed | THE BIONIC INTELLIGENT FAULT DIAGNOSIS BASED ON COMPREHENSIVE DIFFERENCES MINING FOR HYDRO TURBINE |
| title_short | THE BIONIC INTELLIGENT FAULT DIAGNOSIS BASED ON COMPREHENSIVE DIFFERENCES MINING FOR HYDRO TURBINE |
| title_sort | bionic intelligent fault diagnosis based on comprehensive differences mining for hydro turbine |
| topic | Hydroelectric generating set Dependent feature vector Comprehensive differences mining Bionic fault diagnosis Feature selection |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.06.001 |
| work_keys_str_mv | AT chenxiaoyue thebionicintelligentfaultdiagnosisbasedoncomprehensivedifferencesminingforhydroturbine AT gedang thebionicintelligentfaultdiagnosisbasedoncomprehensivedifferencesminingforhydroturbine AT huangjiangping thebionicintelligentfaultdiagnosisbasedoncomprehensivedifferencesminingforhydroturbine AT liuxiong thebionicintelligentfaultdiagnosisbasedoncomprehensivedifferencesminingforhydroturbine AT zhangxiaoyan thebionicintelligentfaultdiagnosisbasedoncomprehensivedifferencesminingforhydroturbine AT chenxiaoyue bionicintelligentfaultdiagnosisbasedoncomprehensivedifferencesminingforhydroturbine AT gedang bionicintelligentfaultdiagnosisbasedoncomprehensivedifferencesminingforhydroturbine AT huangjiangping bionicintelligentfaultdiagnosisbasedoncomprehensivedifferencesminingforhydroturbine AT liuxiong bionicintelligentfaultdiagnosisbasedoncomprehensivedifferencesminingforhydroturbine AT zhangxiaoyan bionicintelligentfaultdiagnosisbasedoncomprehensivedifferencesminingforhydroturbine |