Gearbox Fault Diagnosis Based on Dynamic Weighted Feature Fusion with Maximum Information Coefficient
With the refinement and complexity of mechanical equipment, the number and types of sensors used to monitor their operating status are increasing. In order to effectively fuse multi-sensor information, complete the information in time and space, and improve the reliability of sensor information, a g...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2022-12-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2022.12.022 |
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| author | Nie Yongjun Liu Zhijun Tang Zhenyu Liu Zhihua Zhou Qiang |
| author_facet | Nie Yongjun Liu Zhijun Tang Zhenyu Liu Zhihua Zhou Qiang |
| author_sort | Nie Yongjun |
| collection | DOAJ |
| description | With the refinement and complexity of mechanical equipment, the number and types of sensors used to monitor their operating status are increasing. In order to effectively fuse multi-sensor information, complete the information in time and space, and improve the reliability of sensor information, a gear fault diagnosis method based on dynamic weighted feature fusion with maximum information coefficient is proposed. The wavelet packet transform is used to decompose the vibration signals collected by multi-sensor into time-frequency domain; the time and frequency domain features are calculated, the weight of each sensor is calculated by the maximum information coefficient, and the features are fused in parallel; the fused features are input into the support vector machine model for fault classification. Experiments show that the fusion features have better aggregation and are more conducive to classification; under the two speed conditions, the accuracy of fault diagnosis after fusion is 87.72% and 99.16% respectively; the experiment also proves that the diagnosis effect of dynamic weighted fusion is better than that of fixed weight fusion. |
| format | Article |
| id | doaj-art-82accca631194f77be67d8d2cd14d3ee |
| institution | OA Journals |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2022-12-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-82accca631194f77be67d8d2cd14d3ee2025-08-20T02:37:29ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392022-12-014614214733108495Gearbox Fault Diagnosis Based on Dynamic Weighted Feature Fusion with Maximum Information CoefficientNie YongjunLiu ZhijunTang ZhenyuLiu ZhihuaZhou QiangWith the refinement and complexity of mechanical equipment, the number and types of sensors used to monitor their operating status are increasing. In order to effectively fuse multi-sensor information, complete the information in time and space, and improve the reliability of sensor information, a gear fault diagnosis method based on dynamic weighted feature fusion with maximum information coefficient is proposed. The wavelet packet transform is used to decompose the vibration signals collected by multi-sensor into time-frequency domain; the time and frequency domain features are calculated, the weight of each sensor is calculated by the maximum information coefficient, and the features are fused in parallel; the fused features are input into the support vector machine model for fault classification. Experiments show that the fusion features have better aggregation and are more conducive to classification; under the two speed conditions, the accuracy of fault diagnosis after fusion is 87.72% and 99.16% respectively; the experiment also proves that the diagnosis effect of dynamic weighted fusion is better than that of fixed weight fusion.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2022.12.022Maximum information coefficientDynamic weightingFeature fusionFault diagnosisSupport vector machine |
| spellingShingle | Nie Yongjun Liu Zhijun Tang Zhenyu Liu Zhihua Zhou Qiang Gearbox Fault Diagnosis Based on Dynamic Weighted Feature Fusion with Maximum Information Coefficient Jixie chuandong Maximum information coefficient Dynamic weighting Feature fusion Fault diagnosis Support vector machine |
| title | Gearbox Fault Diagnosis Based on Dynamic Weighted Feature Fusion with Maximum Information Coefficient |
| title_full | Gearbox Fault Diagnosis Based on Dynamic Weighted Feature Fusion with Maximum Information Coefficient |
| title_fullStr | Gearbox Fault Diagnosis Based on Dynamic Weighted Feature Fusion with Maximum Information Coefficient |
| title_full_unstemmed | Gearbox Fault Diagnosis Based on Dynamic Weighted Feature Fusion with Maximum Information Coefficient |
| title_short | Gearbox Fault Diagnosis Based on Dynamic Weighted Feature Fusion with Maximum Information Coefficient |
| title_sort | gearbox fault diagnosis based on dynamic weighted feature fusion with maximum information coefficient |
| topic | Maximum information coefficient Dynamic weighting Feature fusion Fault diagnosis Support vector machine |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2022.12.022 |
| work_keys_str_mv | AT nieyongjun gearboxfaultdiagnosisbasedondynamicweightedfeaturefusionwithmaximuminformationcoefficient AT liuzhijun gearboxfaultdiagnosisbasedondynamicweightedfeaturefusionwithmaximuminformationcoefficient AT tangzhenyu gearboxfaultdiagnosisbasedondynamicweightedfeaturefusionwithmaximuminformationcoefficient AT liuzhihua gearboxfaultdiagnosisbasedondynamicweightedfeaturefusionwithmaximuminformationcoefficient AT zhouqiang gearboxfaultdiagnosisbasedondynamicweightedfeaturefusionwithmaximuminformationcoefficient |