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 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:22 |
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| Enthalten in: |
Sensors (Basel, Switzerland) - 22(2022), 11 vom: 27. Mai |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Nguyen, Cong Dai [VerfasserIn] |
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| Links: |
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| Themen: |
Adaptive noise canceling technique |
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| Anmerkungen: |
Date Revised 16.07.2022 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3390/s22114091 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM342064061 |
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| 100 | 1 | |a Nguyen, Cong Dai |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Gearbox Fault Identification Model Using an Adaptive Noise Canceling Technique, Heterogeneous Feature Extraction, and Distance Ratio Principal Component Analysis |
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| 500 | |a Date Revised 16.07.2022 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a adaptive noise canceling technique | |
| 650 | 4 | |a fault diagnosis | |
| 650 | 4 | |a feature extraction | |
| 650 | 4 | |a gearbox fault identification | |
| 650 | 4 | |a principal component analysis | |
| 650 | 4 | |a support vector machine | |
| 700 | 1 | |a Kim, Cheol Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Jong-Myon |e verfasserin |4 aut | |
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