Intelligence model-driven multi-stress adaptive reliability enhancement testing technology
This paper proposes an Intelligence Model-Driven Multi-Stress Adaptive Reliability Enhancement Testing (IMD-MSARET) technology, which aims to address the problems of rigid test design, low efficiency, and high consumption of test specimens in traditional reliability enhancement testing under multi-s...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-06-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0277547 |
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| _version_ | 1850108042019864576 |
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| author | Shouqing Huang Beichen He Jing Wang Xiaoyang Li Rui Kang Fangyong Li |
| author_facet | Shouqing Huang Beichen He Jing Wang Xiaoyang Li Rui Kang Fangyong Li |
| author_sort | Shouqing Huang |
| collection | DOAJ |
| description | This paper proposes an Intelligence Model-Driven Multi-Stress Adaptive Reliability Enhancement Testing (IMD-MSARET) technology, which aims to address the problems of rigid test design, low efficiency, and high consumption of test specimens in traditional reliability enhancement testing under multi-stress scenarios through efficient multi-factor test design and data analysis. The IMD-MSARET technology combines sequential test design and artificial intelligence techniques to dynamically construct and update the mapping relationship model between multiple stresses and failure characteristic during testing. In terms of mathematical models, we propose a Tuna Swarm Optimization–Gaussian Process Regression (TSO-GPR) model, which combines the global search capability of the tuna swarm optimization algorithm and the accurate prediction capability of Gaussian process regression, effectively handling the complex nonlinear relationships between multiple stresses and failure characteristic. In addition, we propose a three-factor step-by-step screening algorithm and scoring model to determine the optimal sequential test points. Case study verification shows that IMD-MSARET outperforms traditional methods, such as simple random testing and orthogonal testing, in terms of test efficiency, prediction accuracy, and test item consumption. The TSO-GPR model-driven IMD-MSARET is superior to GPR, TSO-SVM, support vector machine and Tuna Swarm Optimization–Backpropagation Neural Network (TSO-BPNN) in terms of accuracy, efficiency, and test item cost for constructing multi-stress limit envelopes. |
| format | Article |
| id | doaj-art-e0591964a993432ba65118682e878892 |
| institution | OA Journals |
| issn | 2158-3226 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-e0591964a993432ba65118682e8788922025-08-20T02:38:28ZengAIP Publishing LLCAIP Advances2158-32262025-06-01156065205065205-1810.1063/5.0277547Intelligence model-driven multi-stress adaptive reliability enhancement testing technologyShouqing Huang0Beichen He1Jing Wang2Xiaoyang Li3Rui Kang4Fangyong Li5National Key Laboratory of Science and Technology on Reliability and Environmental Engineering, Beijing 100094, ChinaNational Key Laboratory of Science and Technology on Reliability and Environmental Engineering, Beijing 100094, ChinaNational Key Laboratory of Science and Technology on Reliability and Environmental Engineering, Beijing 100094, ChinaNational Key Laboratory of Science and Technology on Reliability and Environmental Engineering, Beijing 100094, ChinaNational Key Laboratory of Science and Technology on Reliability and Environmental Engineering, Beijing 100094, ChinaNational Key Laboratory of Science and Technology on Reliability and Environmental Engineering, Beijing 100094, ChinaThis paper proposes an Intelligence Model-Driven Multi-Stress Adaptive Reliability Enhancement Testing (IMD-MSARET) technology, which aims to address the problems of rigid test design, low efficiency, and high consumption of test specimens in traditional reliability enhancement testing under multi-stress scenarios through efficient multi-factor test design and data analysis. The IMD-MSARET technology combines sequential test design and artificial intelligence techniques to dynamically construct and update the mapping relationship model between multiple stresses and failure characteristic during testing. In terms of mathematical models, we propose a Tuna Swarm Optimization–Gaussian Process Regression (TSO-GPR) model, which combines the global search capability of the tuna swarm optimization algorithm and the accurate prediction capability of Gaussian process regression, effectively handling the complex nonlinear relationships between multiple stresses and failure characteristic. In addition, we propose a three-factor step-by-step screening algorithm and scoring model to determine the optimal sequential test points. Case study verification shows that IMD-MSARET outperforms traditional methods, such as simple random testing and orthogonal testing, in terms of test efficiency, prediction accuracy, and test item consumption. The TSO-GPR model-driven IMD-MSARET is superior to GPR, TSO-SVM, support vector machine and Tuna Swarm Optimization–Backpropagation Neural Network (TSO-BPNN) in terms of accuracy, efficiency, and test item cost for constructing multi-stress limit envelopes.http://dx.doi.org/10.1063/5.0277547 |
| spellingShingle | Shouqing Huang Beichen He Jing Wang Xiaoyang Li Rui Kang Fangyong Li Intelligence model-driven multi-stress adaptive reliability enhancement testing technology AIP Advances |
| title | Intelligence model-driven multi-stress adaptive reliability enhancement testing technology |
| title_full | Intelligence model-driven multi-stress adaptive reliability enhancement testing technology |
| title_fullStr | Intelligence model-driven multi-stress adaptive reliability enhancement testing technology |
| title_full_unstemmed | Intelligence model-driven multi-stress adaptive reliability enhancement testing technology |
| title_short | Intelligence model-driven multi-stress adaptive reliability enhancement testing technology |
| title_sort | intelligence model driven multi stress adaptive reliability enhancement testing technology |
| url | http://dx.doi.org/10.1063/5.0277547 |
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