Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis
Using an adaptive noise canceling technique (ANCT) and distance ratio principal component analysis (DRPCA), this paper proposes a new fault diagnostic model for multi-degree tooth-cut failures (MTCF) in a gearbox operating at inconsistent speeds. To account for background and disturbance noise in th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/11/4091 |
| _version_ | 1797491707353235456 |
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| author | Cong Dai Nguyen Cheol Hong Kim Jong-Myon Kim |
| author_facet | Cong Dai Nguyen Cheol Hong Kim Jong-Myon Kim |
| author_sort | Cong Dai Nguyen |
| collection | DOAJ |
| description | Using an adaptive noise canceling technique (ANCT) and distance ratio principal component analysis (DRPCA), this paper proposes a new fault diagnostic model for multi-degree tooth-cut failures (MTCF) in a gearbox operating at inconsistent speeds. To account for background and disturbance noise in the vibration characteristics of gear failures, the proposed approach employs ANCT in the first stage to optimize vibration signals. The ANCT applies an adaptive denoising technique to each basic frequency segment in the whole frequency response of vibrations. Following that, a novel DRPCA is used to extract the discriminating low-dimensional features. The DRPCA initially determines each feature’s relative proximity to fault categories by computing the average Euclidian distance ratio between similar and dissimilar classes. The most discriminatory features with the lowest dimensions are selected, as determined by principal component analysis (PCA). The new DRPCA is created by combining distance ratio–based feature inspection with PCA. The optimal feature set containing the most discriminative features is then fed to the support vector machine classifier to identify multiple failure categories. The experimental results indicate that the proposed model outperforms the state-of-art approaches and offers the highest identification accuracy. |
| first_indexed | 2024-03-10T00:53:12Z |
| format | Article |
| id | doaj.art-b6dce3f74b794cf2b72e35d20dd87df5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:53:12Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b6dce3f74b794cf2b72e35d20dd87df52023-11-23T14:48:27ZengMDPI AGSensors1424-82202022-05-012211409110.3390/s22114091Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component AnalysisCong Dai Nguyen0Cheol Hong Kim1Jong-Myon Kim2Faculty of Radio-Electronic Engineering, Le Quy Don Technical University, Hanoi 10000, VietnamSchool of Computer Science and Engineering, Soongsil University, Seoul 06978, KoreaDepartment of Electrical, Electronics, and Computer Engineering, University of Ulsan, Ulsan 44610, KoreaUsing an adaptive noise canceling technique (ANCT) and distance ratio principal component analysis (DRPCA), this paper proposes a new fault diagnostic model for multi-degree tooth-cut failures (MTCF) in a gearbox operating at inconsistent speeds. To account for background and disturbance noise in the vibration characteristics of gear failures, the proposed approach employs ANCT in the first stage to optimize vibration signals. The ANCT applies an adaptive denoising technique to each basic frequency segment in the whole frequency response of vibrations. Following that, a novel DRPCA is used to extract the discriminating low-dimensional features. The DRPCA initially determines each feature’s relative proximity to fault categories by computing the average Euclidian distance ratio between similar and dissimilar classes. The most discriminatory features with the lowest dimensions are selected, as determined by principal component analysis (PCA). The new DRPCA is created by combining distance ratio–based feature inspection with PCA. The optimal feature set containing the most discriminative features is then fed to the support vector machine classifier to identify multiple failure categories. The experimental results indicate that the proposed model outperforms the state-of-art approaches and offers the highest identification accuracy.https://www.mdpi.com/1424-8220/22/11/4091fault diagnosisfeature extractiongearbox fault identificationadaptive noise canceling techniqueprincipal component analysissupport vector machine |
| spellingShingle | Cong Dai Nguyen Cheol Hong Kim Jong-Myon Kim Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis Sensors fault diagnosis feature extraction gearbox fault identification adaptive noise canceling technique principal component analysis support vector machine |
| title | Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis |
| title_full | Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis |
| title_fullStr | Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis |
| title_full_unstemmed | Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis |
| title_short | Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis |
| title_sort | gearbox fault identification model using an adaptive noise canceling technique heterogeneous feature extraction and distance ratio principal component analysis |
| topic | fault diagnosis feature extraction gearbox fault identification adaptive noise canceling technique principal component analysis support vector machine |
| url | https://www.mdpi.com/1424-8220/22/11/4091 |
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