Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method
This research aims to evaluate the calculation accuracy and efficiency of the artificial neural network-based important sampling method (ANN-IS) on reliability of structures such as drum brakes. The finite element analysis (FEA) result is used to establish the ANN sample in ANN-based reliability ana...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5517634 |
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| _version_ | 1832565976870158336 |
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| author | Zhou Yang Unsong Pak Cholu Kwon |
| author_facet | Zhou Yang Unsong Pak Cholu Kwon |
| author_sort | Zhou Yang |
| collection | DOAJ |
| description | This research aims to evaluate the calculation accuracy and efficiency of the artificial neural network-based important sampling method (ANN-IS) on reliability of structures such as drum brakes. The finite element analysis (FEA) result is used to establish the ANN sample in ANN-based reliability analysis methods. Because the process of FEA is time-consuming, the ANN sample size has a very important influence on the calculation efficiency. Two types of ANNs used in this study are the radial basis function neural network (RBF) and back propagation neural network (BP). RBF-IS and BP-IS methods are used to conduct reliability analysis on training samples of three different sizes, and the results are compared with several reliability analysis methods based on ANNs. The results show that the probability of failure of the RBF-IS method is closer to that of the Monte-Carlo simulation method (MCS) than those of other methods (including BP-IS). In addition, the RBF-IS method has better calculation efficiency than the other methods considered in this study. This research demonstrates that the RBF-IS method is well suited to structure reliability problems. |
| format | Article |
| id | doaj-art-223fff073e16411eac8bc363e7a9aed4 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-223fff073e16411eac8bc363e7a9aed42025-02-03T01:05:26ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55176345517634Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling MethodZhou Yang0Unsong Pak1Cholu Kwon2School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, ChinaSchool of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, ChinaSchool of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, ChinaThis research aims to evaluate the calculation accuracy and efficiency of the artificial neural network-based important sampling method (ANN-IS) on reliability of structures such as drum brakes. The finite element analysis (FEA) result is used to establish the ANN sample in ANN-based reliability analysis methods. Because the process of FEA is time-consuming, the ANN sample size has a very important influence on the calculation efficiency. Two types of ANNs used in this study are the radial basis function neural network (RBF) and back propagation neural network (BP). RBF-IS and BP-IS methods are used to conduct reliability analysis on training samples of three different sizes, and the results are compared with several reliability analysis methods based on ANNs. The results show that the probability of failure of the RBF-IS method is closer to that of the Monte-Carlo simulation method (MCS) than those of other methods (including BP-IS). In addition, the RBF-IS method has better calculation efficiency than the other methods considered in this study. This research demonstrates that the RBF-IS method is well suited to structure reliability problems.http://dx.doi.org/10.1155/2021/5517634 |
| spellingShingle | Zhou Yang Unsong Pak Cholu Kwon Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method Complexity |
| title | Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method |
| title_full | Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method |
| title_fullStr | Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method |
| title_full_unstemmed | Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method |
| title_short | Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method |
| title_sort | vibration reliability analysis of drum brake using the artificial neural network and important sampling method |
| url | http://dx.doi.org/10.1155/2021/5517634 |
| work_keys_str_mv | AT zhouyang vibrationreliabilityanalysisofdrumbrakeusingtheartificialneuralnetworkandimportantsamplingmethod AT unsongpak vibrationreliabilityanalysisofdrumbrakeusingtheartificialneuralnetworkandimportantsamplingmethod AT cholukwon vibrationreliabilityanalysisofdrumbrakeusingtheartificialneuralnetworkandimportantsamplingmethod |