Domain adaptation with label-aligned sampling (DALAS) for cross-domain fault diagnosis of rotating machinery under class imbalance
Though existing cross-domain fault-diagnosis methods have shown promising results under domain shift conditions, existing approaches are only valid for class-balanced data. However, situations of class imbalance are inevitable in industrial fields, due to the difficulty in acquiring fault data from...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lee, Jinwook [verfasserIn] Ko, Jin Uk [verfasserIn] Kim, Taehun [verfasserIn] Kim, Yong Chae [verfasserIn] Jung, Joon Ha [verfasserIn] Youn, Byeng D. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 243 |
|---|---|
| Übergeordnetes Werk: |
volume:243 |
| DOI / URN: |
10.1016/j.eswa.2023.122910 |
|---|
| Katalog-ID: |
ELV067138969 |
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| 245 | 1 | 0 | |a Domain adaptation with label-aligned sampling (DALAS) for cross-domain fault diagnosis of rotating machinery under class imbalance |
| 264 | 1 | |c 2023 | |
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| 520 | |a Though existing cross-domain fault-diagnosis methods have shown promising results under domain shift conditions, existing approaches are only valid for class-balanced data. However, situations of class imbalance are inevitable in industrial fields, due to the difficulty in acquiring fault data from in-use machinery. Thus, if existing cross-domain fault diagnosis approaches are directly applied when domain shift and class imbalance coexist, performance degradation can occur. Thus, this research develops a domain adversarial learning network for class-imbalanced data to address situations where class imbalance and domain shift coexist; this situation is referred to as the problem of class imbalance domain adaptation (CIDA). In the proposed method, domain adversarial training is implemented for learning domain-invariant features by reducing the domain shift, and a label-aligned sampling strategy is utilized to deal with the class imbalance. In addition, for further performance enhancement of label-aligned sampling by increasing the accuracy of pseudo labels, metric learning is introduced to enhance the feature distinctiveness by expanding the distance of samples from different classes while decreasing the distance of samples from the same class. The efficiency of the proposed method is validated by applying it to various circumstances using two bearing datasets. The proposed method demonstrates superior performance compared to conventional algorithms in addressing the CIDA problem, according to quantitative and qualitative evaluations. | ||
| 650 | 4 | |a Fault diagnosis | |
| 650 | 4 | |a Unsupervised domain adaptation | |
| 650 | 4 | |a Class imbalance | |
| 650 | 4 | |a Rotating machinery | |
| 700 | 1 | |a Ko, Jin Uk |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Taehun |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Yong Chae |e verfasserin |4 aut | |
| 700 | 1 | |a Jung, Joon Ha |e verfasserin |4 aut | |
| 700 | 1 | |a Youn, Byeng D. |e verfasserin |4 aut | |
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10.1016/j.eswa.2023.122910 doi (DE-627)ELV067138969 (ELSEVIER)S0957-4174(23)03412-7 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Lee, Jinwook verfasserin aut Domain adaptation with label-aligned sampling (DALAS) for cross-domain fault diagnosis of rotating machinery under class imbalance 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Though existing cross-domain fault-diagnosis methods have shown promising results under domain shift conditions, existing approaches are only valid for class-balanced data. However, situations of class imbalance are inevitable in industrial fields, due to the difficulty in acquiring fault data from in-use machinery. Thus, if existing cross-domain fault diagnosis approaches are directly applied when domain shift and class imbalance coexist, performance degradation can occur. Thus, this research develops a domain adversarial learning network for class-imbalanced data to address situations where class imbalance and domain shift coexist; this situation is referred to as the problem of class imbalance domain adaptation (CIDA). In the proposed method, domain adversarial training is implemented for learning domain-invariant features by reducing the domain shift, and a label-aligned sampling strategy is utilized to deal with the class imbalance. In addition, for further performance enhancement of label-aligned sampling by increasing the accuracy of pseudo labels, metric learning is introduced to enhance the feature distinctiveness by expanding the distance of samples from different classes while decreasing the distance of samples from the same class. The efficiency of the proposed method is validated by applying it to various circumstances using two bearing datasets. The proposed method demonstrates superior performance compared to conventional algorithms in addressing the CIDA problem, according to quantitative and qualitative evaluations. Fault diagnosis Unsupervised domain adaptation Class imbalance Rotating machinery Ko, Jin Uk verfasserin aut Kim, Taehun verfasserin aut Kim, Yong Chae verfasserin aut Jung, Joon Ha verfasserin aut Youn, Byeng D. verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 243 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:243 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 243 |
| spelling |
10.1016/j.eswa.2023.122910 doi (DE-627)ELV067138969 (ELSEVIER)S0957-4174(23)03412-7 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Lee, Jinwook verfasserin aut Domain adaptation with label-aligned sampling (DALAS) for cross-domain fault diagnosis of rotating machinery under class imbalance 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Though existing cross-domain fault-diagnosis methods have shown promising results under domain shift conditions, existing approaches are only valid for class-balanced data. However, situations of class imbalance are inevitable in industrial fields, due to the difficulty in acquiring fault data from in-use machinery. Thus, if existing cross-domain fault diagnosis approaches are directly applied when domain shift and class imbalance coexist, performance degradation can occur. Thus, this research develops a domain adversarial learning network for class-imbalanced data to address situations where class imbalance and domain shift coexist; this situation is referred to as the problem of class imbalance domain adaptation (CIDA). In the proposed method, domain adversarial training is implemented for learning domain-invariant features by reducing the domain shift, and a label-aligned sampling strategy is utilized to deal with the class imbalance. In addition, for further performance enhancement of label-aligned sampling by increasing the accuracy of pseudo labels, metric learning is introduced to enhance the feature distinctiveness by expanding the distance of samples from different classes while decreasing the distance of samples from the same class. The efficiency of the proposed method is validated by applying it to various circumstances using two bearing datasets. The proposed method demonstrates superior performance compared to conventional algorithms in addressing the CIDA problem, according to quantitative and qualitative evaluations. Fault diagnosis Unsupervised domain adaptation Class imbalance Rotating machinery Ko, Jin Uk verfasserin aut Kim, Taehun verfasserin aut Kim, Yong Chae verfasserin aut Jung, Joon Ha verfasserin aut Youn, Byeng D. verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 243 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:243 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 243 |
| allfields_unstemmed |
10.1016/j.eswa.2023.122910 doi (DE-627)ELV067138969 (ELSEVIER)S0957-4174(23)03412-7 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Lee, Jinwook verfasserin aut Domain adaptation with label-aligned sampling (DALAS) for cross-domain fault diagnosis of rotating machinery under class imbalance 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Though existing cross-domain fault-diagnosis methods have shown promising results under domain shift conditions, existing approaches are only valid for class-balanced data. However, situations of class imbalance are inevitable in industrial fields, due to the difficulty in acquiring fault data from in-use machinery. Thus, if existing cross-domain fault diagnosis approaches are directly applied when domain shift and class imbalance coexist, performance degradation can occur. Thus, this research develops a domain adversarial learning network for class-imbalanced data to address situations where class imbalance and domain shift coexist; this situation is referred to as the problem of class imbalance domain adaptation (CIDA). In the proposed method, domain adversarial training is implemented for learning domain-invariant features by reducing the domain shift, and a label-aligned sampling strategy is utilized to deal with the class imbalance. In addition, for further performance enhancement of label-aligned sampling by increasing the accuracy of pseudo labels, metric learning is introduced to enhance the feature distinctiveness by expanding the distance of samples from different classes while decreasing the distance of samples from the same class. The efficiency of the proposed method is validated by applying it to various circumstances using two bearing datasets. The proposed method demonstrates superior performance compared to conventional algorithms in addressing the CIDA problem, according to quantitative and qualitative evaluations. Fault diagnosis Unsupervised domain adaptation Class imbalance Rotating machinery Ko, Jin Uk verfasserin aut Kim, Taehun verfasserin aut Kim, Yong Chae verfasserin aut Jung, Joon Ha verfasserin aut Youn, Byeng D. verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 243 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:243 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 243 |
| allfieldsGer |
10.1016/j.eswa.2023.122910 doi (DE-627)ELV067138969 (ELSEVIER)S0957-4174(23)03412-7 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Lee, Jinwook verfasserin aut Domain adaptation with label-aligned sampling (DALAS) for cross-domain fault diagnosis of rotating machinery under class imbalance 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Though existing cross-domain fault-diagnosis methods have shown promising results under domain shift conditions, existing approaches are only valid for class-balanced data. However, situations of class imbalance are inevitable in industrial fields, due to the difficulty in acquiring fault data from in-use machinery. Thus, if existing cross-domain fault diagnosis approaches are directly applied when domain shift and class imbalance coexist, performance degradation can occur. Thus, this research develops a domain adversarial learning network for class-imbalanced data to address situations where class imbalance and domain shift coexist; this situation is referred to as the problem of class imbalance domain adaptation (CIDA). In the proposed method, domain adversarial training is implemented for learning domain-invariant features by reducing the domain shift, and a label-aligned sampling strategy is utilized to deal with the class imbalance. In addition, for further performance enhancement of label-aligned sampling by increasing the accuracy of pseudo labels, metric learning is introduced to enhance the feature distinctiveness by expanding the distance of samples from different classes while decreasing the distance of samples from the same class. The efficiency of the proposed method is validated by applying it to various circumstances using two bearing datasets. The proposed method demonstrates superior performance compared to conventional algorithms in addressing the CIDA problem, according to quantitative and qualitative evaluations. Fault diagnosis Unsupervised domain adaptation Class imbalance Rotating machinery Ko, Jin Uk verfasserin aut Kim, Taehun verfasserin aut Kim, Yong Chae verfasserin aut Jung, Joon Ha verfasserin aut Youn, Byeng D. verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 243 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:243 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 243 |
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10.1016/j.eswa.2023.122910 doi (DE-627)ELV067138969 (ELSEVIER)S0957-4174(23)03412-7 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Lee, Jinwook verfasserin aut Domain adaptation with label-aligned sampling (DALAS) for cross-domain fault diagnosis of rotating machinery under class imbalance 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Though existing cross-domain fault-diagnosis methods have shown promising results under domain shift conditions, existing approaches are only valid for class-balanced data. However, situations of class imbalance are inevitable in industrial fields, due to the difficulty in acquiring fault data from in-use machinery. Thus, if existing cross-domain fault diagnosis approaches are directly applied when domain shift and class imbalance coexist, performance degradation can occur. Thus, this research develops a domain adversarial learning network for class-imbalanced data to address situations where class imbalance and domain shift coexist; this situation is referred to as the problem of class imbalance domain adaptation (CIDA). In the proposed method, domain adversarial training is implemented for learning domain-invariant features by reducing the domain shift, and a label-aligned sampling strategy is utilized to deal with the class imbalance. In addition, for further performance enhancement of label-aligned sampling by increasing the accuracy of pseudo labels, metric learning is introduced to enhance the feature distinctiveness by expanding the distance of samples from different classes while decreasing the distance of samples from the same class. The efficiency of the proposed method is validated by applying it to various circumstances using two bearing datasets. The proposed method demonstrates superior performance compared to conventional algorithms in addressing the CIDA problem, according to quantitative and qualitative evaluations. Fault diagnosis Unsupervised domain adaptation Class imbalance Rotating machinery Ko, Jin Uk verfasserin aut Kim, Taehun verfasserin aut Kim, Yong Chae verfasserin aut Jung, Joon Ha verfasserin aut Youn, Byeng D. verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 243 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:243 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 243 |
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004 VZ 54.72 bkl Domain adaptation with label-aligned sampling (DALAS) for cross-domain fault diagnosis of rotating machinery under class imbalance Fault diagnosis Unsupervised domain adaptation Class imbalance Rotating machinery |
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Domain adaptation with label-aligned sampling (DALAS) for cross-domain fault diagnosis of rotating machinery under class imbalance |
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Domain adaptation with label-aligned sampling (DALAS) for cross-domain fault diagnosis of rotating machinery under class imbalance |
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Lee, Jinwook |
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Lee, Jinwook Ko, Jin Uk Kim, Taehun Kim, Yong Chae Jung, Joon Ha Youn, Byeng D. |
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domain adaptation with label-aligned sampling (dalas) for cross-domain fault diagnosis of rotating machinery under class imbalance |
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Domain adaptation with label-aligned sampling (DALAS) for cross-domain fault diagnosis of rotating machinery under class imbalance |
| abstract |
Though existing cross-domain fault-diagnosis methods have shown promising results under domain shift conditions, existing approaches are only valid for class-balanced data. However, situations of class imbalance are inevitable in industrial fields, due to the difficulty in acquiring fault data from in-use machinery. Thus, if existing cross-domain fault diagnosis approaches are directly applied when domain shift and class imbalance coexist, performance degradation can occur. Thus, this research develops a domain adversarial learning network for class-imbalanced data to address situations where class imbalance and domain shift coexist; this situation is referred to as the problem of class imbalance domain adaptation (CIDA). In the proposed method, domain adversarial training is implemented for learning domain-invariant features by reducing the domain shift, and a label-aligned sampling strategy is utilized to deal with the class imbalance. In addition, for further performance enhancement of label-aligned sampling by increasing the accuracy of pseudo labels, metric learning is introduced to enhance the feature distinctiveness by expanding the distance of samples from different classes while decreasing the distance of samples from the same class. The efficiency of the proposed method is validated by applying it to various circumstances using two bearing datasets. The proposed method demonstrates superior performance compared to conventional algorithms in addressing the CIDA problem, according to quantitative and qualitative evaluations. |
| abstractGer |
Though existing cross-domain fault-diagnosis methods have shown promising results under domain shift conditions, existing approaches are only valid for class-balanced data. However, situations of class imbalance are inevitable in industrial fields, due to the difficulty in acquiring fault data from in-use machinery. Thus, if existing cross-domain fault diagnosis approaches are directly applied when domain shift and class imbalance coexist, performance degradation can occur. Thus, this research develops a domain adversarial learning network for class-imbalanced data to address situations where class imbalance and domain shift coexist; this situation is referred to as the problem of class imbalance domain adaptation (CIDA). In the proposed method, domain adversarial training is implemented for learning domain-invariant features by reducing the domain shift, and a label-aligned sampling strategy is utilized to deal with the class imbalance. In addition, for further performance enhancement of label-aligned sampling by increasing the accuracy of pseudo labels, metric learning is introduced to enhance the feature distinctiveness by expanding the distance of samples from different classes while decreasing the distance of samples from the same class. The efficiency of the proposed method is validated by applying it to various circumstances using two bearing datasets. The proposed method demonstrates superior performance compared to conventional algorithms in addressing the CIDA problem, according to quantitative and qualitative evaluations. |
| abstract_unstemmed |
Though existing cross-domain fault-diagnosis methods have shown promising results under domain shift conditions, existing approaches are only valid for class-balanced data. However, situations of class imbalance are inevitable in industrial fields, due to the difficulty in acquiring fault data from in-use machinery. Thus, if existing cross-domain fault diagnosis approaches are directly applied when domain shift and class imbalance coexist, performance degradation can occur. Thus, this research develops a domain adversarial learning network for class-imbalanced data to address situations where class imbalance and domain shift coexist; this situation is referred to as the problem of class imbalance domain adaptation (CIDA). In the proposed method, domain adversarial training is implemented for learning domain-invariant features by reducing the domain shift, and a label-aligned sampling strategy is utilized to deal with the class imbalance. In addition, for further performance enhancement of label-aligned sampling by increasing the accuracy of pseudo labels, metric learning is introduced to enhance the feature distinctiveness by expanding the distance of samples from different classes while decreasing the distance of samples from the same class. The efficiency of the proposed method is validated by applying it to various circumstances using two bearing datasets. The proposed method demonstrates superior performance compared to conventional algorithms in addressing the CIDA problem, according to quantitative and qualitative evaluations. |
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Domain adaptation with label-aligned sampling (DALAS) for cross-domain fault diagnosis of rotating machinery under class imbalance |
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