A Combined Nonstationary Kriging and Support Vector Machine Method for Stochastic Eigenvalue Analysis of Brake Systems
This paper presents a new metamodel approach based on nonstationary kriging and a support vector machine to efficiently predict the stochastic eigenvalue of brake systems. One of the difficulties in the mode-coupling instability induced by friction is that stochastic eigenvalues represent heterogene...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/1/245 |
| _version_ | 1818011362406694912 |
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| author | Gil-Yong Lee Yong-Hwa Park |
| author_facet | Gil-Yong Lee Yong-Hwa Park |
| author_sort | Gil-Yong Lee |
| collection | DOAJ |
| description | This paper presents a new metamodel approach based on nonstationary kriging and a support vector machine to efficiently predict the stochastic eigenvalue of brake systems. One of the difficulties in the mode-coupling instability induced by friction is that stochastic eigenvalues represent heterogeneous behavior due to the bifurcation phenomenon. Therefore, the stationarity assumption in kriging, where the response is correlated over the entire random input space, may not remain valid. In this paper, to address this issue, Gibb’s nonstationary kernel with step-wise hyperparameters was adopted to reflect the heterogeneity of the stochastic eigenvalues. In predicting the response for unsampled input, the support vector machine-based classification is utilized. To validate the performance, a simplified finite element model of the brake system is considered. Under various types of uncertainties, including different friction coefficients and material properties, stochastic eigenvalue problems are investigated. Through numerical studies, it is seen that the proposed method improves accuracy and robustness compared to conventional stationary kriging. |
| first_indexed | 2024-04-14T06:06:44Z |
| format | Article |
| id | doaj.art-8a2f448a99ab47dfb1caf14eb4f0d693 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-14T06:06:44Z |
| publishDate | 2019-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8a2f448a99ab47dfb1caf14eb4f0d6932022-12-22T02:08:30ZengMDPI AGApplied Sciences2076-34172019-12-0110124510.3390/app10010245app10010245A Combined Nonstationary Kriging and Support Vector Machine Method for Stochastic Eigenvalue Analysis of Brake SystemsGil-Yong Lee0Yong-Hwa Park1Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, KoreaDepartment of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, KoreaThis paper presents a new metamodel approach based on nonstationary kriging and a support vector machine to efficiently predict the stochastic eigenvalue of brake systems. One of the difficulties in the mode-coupling instability induced by friction is that stochastic eigenvalues represent heterogeneous behavior due to the bifurcation phenomenon. Therefore, the stationarity assumption in kriging, where the response is correlated over the entire random input space, may not remain valid. In this paper, to address this issue, Gibb’s nonstationary kernel with step-wise hyperparameters was adopted to reflect the heterogeneity of the stochastic eigenvalues. In predicting the response for unsampled input, the support vector machine-based classification is utilized. To validate the performance, a simplified finite element model of the brake system is considered. Under various types of uncertainties, including different friction coefficients and material properties, stochastic eigenvalue problems are investigated. Through numerical studies, it is seen that the proposed method improves accuracy and robustness compared to conventional stationary kriging.https://www.mdpi.com/2076-3417/10/1/245brake systemsstochastic complex eigenvalue analysisnonstationary krigingsupport vector machine |
| spellingShingle | Gil-Yong Lee Yong-Hwa Park A Combined Nonstationary Kriging and Support Vector Machine Method for Stochastic Eigenvalue Analysis of Brake Systems Applied Sciences brake systems stochastic complex eigenvalue analysis nonstationary kriging support vector machine |
| title | A Combined Nonstationary Kriging and Support Vector Machine Method for Stochastic Eigenvalue Analysis of Brake Systems |
| title_full | A Combined Nonstationary Kriging and Support Vector Machine Method for Stochastic Eigenvalue Analysis of Brake Systems |
| title_fullStr | A Combined Nonstationary Kriging and Support Vector Machine Method for Stochastic Eigenvalue Analysis of Brake Systems |
| title_full_unstemmed | A Combined Nonstationary Kriging and Support Vector Machine Method for Stochastic Eigenvalue Analysis of Brake Systems |
| title_short | A Combined Nonstationary Kriging and Support Vector Machine Method for Stochastic Eigenvalue Analysis of Brake Systems |
| title_sort | combined nonstationary kriging and support vector machine method for stochastic eigenvalue analysis of brake systems |
| topic | brake systems stochastic complex eigenvalue analysis nonstationary kriging support vector machine |
| url | https://www.mdpi.com/2076-3417/10/1/245 |
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