Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection
The key to fault diagnosis of rotating machinery is to extract fault features effectively and select the appropriate classification algorithm. As a common signal decomposition method, the effect of wavelet packet decomposition (WPD) largely depends on the applicability of the wavelet basis function...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/8899188 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832552539674902528 |
|---|---|
| author | Zong Yuan Taotao Zhou Jie Liu Changhe Zhang Yong Liu |
| author_facet | Zong Yuan Taotao Zhou Jie Liu Changhe Zhang Yong Liu |
| author_sort | Zong Yuan |
| collection | DOAJ |
| description | The key to fault diagnosis of rotating machinery is to extract fault features effectively and select the appropriate classification algorithm. As a common signal decomposition method, the effect of wavelet packet decomposition (WPD) largely depends on the applicability of the wavelet basis function (WBF). In this paper, a novel fault diagnosis approach for rotating machinery based on feature importance ranking and selection is proposed. Firstly, a two-step principle is proposed to select the most suitable WBF for the vibration signal, based on which an optimized WPD (OWPD) method is proposed to decompose the vibration signal and extract the fault information in the frequency domain. Secondly, FE is utilized to extract fault features of the decomposed subsignals of OWPD. Thirdly, the categorical boosting (CatBoost) algorithm is introduced to rank the fault features by a certain strategy, and the optimal feature set is further utilized to identify and diagnose the fault types. A hybrid dataset of bearing and rotor faults and an actual dataset of the one-stage reduction gearbox are utilized for experimental verification. Experimental results indicate that the proposed approach can achieve higher fault diagnosis accuracy using fewer features under complex working conditions. |
| format | Article |
| id | doaj-art-f5395e7b232245d68020683d93ba6f59 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-f5395e7b232245d68020683d93ba6f592025-02-03T05:58:30ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/88991888899188Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and SelectionZong Yuan0Taotao Zhou1Jie Liu2Changhe Zhang3Yong Liu4School of Transportation, Wuhan University of Technology, Wuhan 430070, ChinaChina Ship Development and Design Center, Wuhan 430063, ChinaSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, ChinaChina Ship Development and Design Center, Wuhan 430063, ChinaThe key to fault diagnosis of rotating machinery is to extract fault features effectively and select the appropriate classification algorithm. As a common signal decomposition method, the effect of wavelet packet decomposition (WPD) largely depends on the applicability of the wavelet basis function (WBF). In this paper, a novel fault diagnosis approach for rotating machinery based on feature importance ranking and selection is proposed. Firstly, a two-step principle is proposed to select the most suitable WBF for the vibration signal, based on which an optimized WPD (OWPD) method is proposed to decompose the vibration signal and extract the fault information in the frequency domain. Secondly, FE is utilized to extract fault features of the decomposed subsignals of OWPD. Thirdly, the categorical boosting (CatBoost) algorithm is introduced to rank the fault features by a certain strategy, and the optimal feature set is further utilized to identify and diagnose the fault types. A hybrid dataset of bearing and rotor faults and an actual dataset of the one-stage reduction gearbox are utilized for experimental verification. Experimental results indicate that the proposed approach can achieve higher fault diagnosis accuracy using fewer features under complex working conditions.http://dx.doi.org/10.1155/2021/8899188 |
| spellingShingle | Zong Yuan Taotao Zhou Jie Liu Changhe Zhang Yong Liu Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection Shock and Vibration |
| title | Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection |
| title_full | Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection |
| title_fullStr | Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection |
| title_full_unstemmed | Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection |
| title_short | Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection |
| title_sort | fault diagnosis approach for rotating machinery based on feature importance ranking and selection |
| url | http://dx.doi.org/10.1155/2021/8899188 |
| work_keys_str_mv | AT zongyuan faultdiagnosisapproachforrotatingmachinerybasedonfeatureimportancerankingandselection AT taotaozhou faultdiagnosisapproachforrotatingmachinerybasedonfeatureimportancerankingandselection AT jieliu faultdiagnosisapproachforrotatingmachinerybasedonfeatureimportancerankingandselection AT changhezhang faultdiagnosisapproachforrotatingmachinerybasedonfeatureimportancerankingandselection AT yongliu faultdiagnosisapproachforrotatingmachinerybasedonfeatureimportancerankingandselection |