Optimal burn-in strategy for high reliable products using convolutional neural network
Burn-in test is widely used to improve the product reliability from the customer's perspective by identifying and screening out defective individuals before they are marketed. For those high reliable products whose failures are caused by gradual degradation, burn-in test not only could pick out...
| Main Authors: | , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/145919 |
| _version_ | 1811681535224446976 |
|---|---|
| author | Lyu, Yi Gao, Junyan Chen, Ci Jiang, Yijie Li, Huachuan Chen, Kairui Zhang, Yun |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Lyu, Yi Gao, Junyan Chen, Ci Jiang, Yijie Li, Huachuan Chen, Kairui Zhang, Yun |
| author_sort | Lyu, Yi |
| collection | NTU |
| description | Burn-in test is widely used to improve the product reliability from the customer's perspective by identifying and screening out defective individuals before they are marketed. For those high reliable products whose failures are caused by gradual degradation, burn-in test not only could pick out weak units, but also increases the degradation of normal units, and hence the test duration is regarded as one key factor in the test policy optimization. In this paper, a new burn-in framework is proposed, which combines a sliding window strategy with one-dimensional convolutional neural network, completes the off-line training for classification model, and then obtains the optimal burn-in time with a group-accuracy strategy. And an online optimization algorithm is constructed to reduce the burn-in time as much as possible without deteriorating the screening effect, thereby to reduce the unnecessary lifetime loss of normal units involved in the test. The effectiveness of the presented framework is validated by the experiment. Compared to conventional strategies based on degradation models, the proposed method has better performance and robustness. |
| first_indexed | 2024-10-01T03:42:29Z |
| format | Journal Article |
| id | ntu-10356/145919 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:42:29Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1459192021-01-14T05:50:59Z Optimal burn-in strategy for high reliable products using convolutional neural network Lyu, Yi Gao, Junyan Chen, Ci Jiang, Yijie Li, Huachuan Chen, Kairui Zhang, Yun School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Burn-in Deep Learning Burn-in test is widely used to improve the product reliability from the customer's perspective by identifying and screening out defective individuals before they are marketed. For those high reliable products whose failures are caused by gradual degradation, burn-in test not only could pick out weak units, but also increases the degradation of normal units, and hence the test duration is regarded as one key factor in the test policy optimization. In this paper, a new burn-in framework is proposed, which combines a sliding window strategy with one-dimensional convolutional neural network, completes the off-line training for classification model, and then obtains the optimal burn-in time with a group-accuracy strategy. And an online optimization algorithm is constructed to reduce the burn-in time as much as possible without deteriorating the screening effect, thereby to reduce the unnecessary lifetime loss of normal units involved in the test. The effectiveness of the presented framework is validated by the experiment. Compared to conventional strategies based on degradation models, the proposed method has better performance and robustness. Published version 2021-01-14T05:50:59Z 2021-01-14T05:50:59Z 2019 Journal Article Lyu, Y., Gao, J., Chen, C., Jiang, Y., Li, H., Chen, K., & Zhang, Y. (2019). Optimal burn-in strategy for high reliable products using convolutional neural network. IEEE Access, 7, 178511-178521. doi:10.1109/ACCESS.2019.2958570 2169-3536 0000-0002-6252-5502 0000-0002-8085-3454 0000-0003-0813-5543 0000-0001-5713-1229 https://hdl.handle.net/10356/145919 10.1109/ACCESS.2019.2958570 2-s2.0-85077235896 7 178511 178521 en IEEE Access © 2019 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given. application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Burn-in Deep Learning Lyu, Yi Gao, Junyan Chen, Ci Jiang, Yijie Li, Huachuan Chen, Kairui Zhang, Yun Optimal burn-in strategy for high reliable products using convolutional neural network |
| title | Optimal burn-in strategy for high reliable products using convolutional neural network |
| title_full | Optimal burn-in strategy for high reliable products using convolutional neural network |
| title_fullStr | Optimal burn-in strategy for high reliable products using convolutional neural network |
| title_full_unstemmed | Optimal burn-in strategy for high reliable products using convolutional neural network |
| title_short | Optimal burn-in strategy for high reliable products using convolutional neural network |
| title_sort | optimal burn in strategy for high reliable products using convolutional neural network |
| topic | Engineering::Electrical and electronic engineering Burn-in Deep Learning |
| url | https://hdl.handle.net/10356/145919 |
| work_keys_str_mv | AT lyuyi optimalburninstrategyforhighreliableproductsusingconvolutionalneuralnetwork AT gaojunyan optimalburninstrategyforhighreliableproductsusingconvolutionalneuralnetwork AT chenci optimalburninstrategyforhighreliableproductsusingconvolutionalneuralnetwork AT jiangyijie optimalburninstrategyforhighreliableproductsusingconvolutionalneuralnetwork AT lihuachuan optimalburninstrategyforhighreliableproductsusingconvolutionalneuralnetwork AT chenkairui optimalburninstrategyforhighreliableproductsusingconvolutionalneuralnetwork AT zhangyun optimalburninstrategyforhighreliableproductsusingconvolutionalneuralnetwork |