ROTATING MACHINERY DEGRADATION STATUS IDENTIFICATION BASED ON BI-OBJECTIVE OPTIMIZATION GENETIC ALGORITHM AND SVM
Extracting degradation features is an important part of Monitoring the health status of machinery. The performance of degradation features fluctuates or even declines with the continuous operation of the rotating machinery for a long time, which makes it difficult to extract and select degradation f...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2021-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.06.002 |
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| _version_ | 1841535950117666816 |
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| author | PEI MoChao ZHANG JianJun LI HongRu YU He |
| author_facet | PEI MoChao ZHANG JianJun LI HongRu YU He |
| author_sort | PEI MoChao |
| collection | DOAJ |
| description | Extracting degradation features is an important part of Monitoring the health status of machinery. The performance of degradation features fluctuates or even declines with the continuous operation of the rotating machinery for a long time, which makes it difficult to extract and select degradation features. First, a feature mapping Algorithm library was used to extract features from the vibration signals and the original feature set was filtered based on Kolmogorov-smirnov（KS） test and Benjamini-Yekutieli process. Then, the optimal feature subset was searched in the supervised environment by combining BOGA with SVC. The accuracy of SVC and the dimension of subset were two objective functions of BOGA, the former was maximized, the latter was minimized. The performance of proposed method was verified by the experiment on the data set of hydraulic pump degradation state and the comparison with FRESH<sub>P</sub>CAa, ReliefF and JMIM on the case western reserve university bearing data set. |
| format | Article |
| id | doaj-art-8075b8c4216b4c438f2943c6da27f112 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-8075b8c4216b4c438f2943c6da27f1122025-01-15T02:25:00ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692021-01-01431280128830612155ROTATING MACHINERY DEGRADATION STATUS IDENTIFICATION BASED ON BI-OBJECTIVE OPTIMIZATION GENETIC ALGORITHM AND SVMPEI MoChaoZHANG JianJunLI HongRuYU HeExtracting degradation features is an important part of Monitoring the health status of machinery. The performance of degradation features fluctuates or even declines with the continuous operation of the rotating machinery for a long time, which makes it difficult to extract and select degradation features. First, a feature mapping Algorithm library was used to extract features from the vibration signals and the original feature set was filtered based on Kolmogorov-smirnov（KS） test and Benjamini-Yekutieli process. Then, the optimal feature subset was searched in the supervised environment by combining BOGA with SVC. The accuracy of SVC and the dimension of subset were two objective functions of BOGA, the former was maximized, the latter was minimized. The performance of proposed method was verified by the experiment on the data set of hydraulic pump degradation state and the comparison with FRESH<sub>P</sub>CAa, ReliefF and JMIM on the case western reserve university bearing data set.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.06.002Rotating machineryDegradation state identificationBi-objective optimization genetic algorithm（BOGA）Support vector classifier（SVC） |
| spellingShingle | PEI MoChao ZHANG JianJun LI HongRu YU He ROTATING MACHINERY DEGRADATION STATUS IDENTIFICATION BASED ON BI-OBJECTIVE OPTIMIZATION GENETIC ALGORITHM AND SVM Jixie qiangdu Rotating machinery Degradation state identification Bi-objective optimization genetic algorithm（BOGA） Support vector classifier（SVC） |
| title | ROTATING MACHINERY DEGRADATION STATUS IDENTIFICATION BASED ON BI-OBJECTIVE OPTIMIZATION GENETIC ALGORITHM AND SVM |
| title_full | ROTATING MACHINERY DEGRADATION STATUS IDENTIFICATION BASED ON BI-OBJECTIVE OPTIMIZATION GENETIC ALGORITHM AND SVM |
| title_fullStr | ROTATING MACHINERY DEGRADATION STATUS IDENTIFICATION BASED ON BI-OBJECTIVE OPTIMIZATION GENETIC ALGORITHM AND SVM |
| title_full_unstemmed | ROTATING MACHINERY DEGRADATION STATUS IDENTIFICATION BASED ON BI-OBJECTIVE OPTIMIZATION GENETIC ALGORITHM AND SVM |
| title_short | ROTATING MACHINERY DEGRADATION STATUS IDENTIFICATION BASED ON BI-OBJECTIVE OPTIMIZATION GENETIC ALGORITHM AND SVM |
| title_sort | rotating machinery degradation status identification based on bi objective optimization genetic algorithm and svm |
| topic | Rotating machinery Degradation state identification Bi-objective optimization genetic algorithm（BOGA） Support vector classifier（SVC） |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.06.002 |
| work_keys_str_mv | AT peimochao rotatingmachinerydegradationstatusidentificationbasedonbiobjectiveoptimizationgeneticalgorithmandsvm AT zhangjianjun rotatingmachinerydegradationstatusidentificationbasedonbiobjectiveoptimizationgeneticalgorithmandsvm AT lihongru rotatingmachinerydegradationstatusidentificationbasedonbiobjectiveoptimizationgeneticalgorithmandsvm AT yuhe rotatingmachinerydegradationstatusidentificationbasedonbiobjectiveoptimizationgeneticalgorithmandsvm |