Optimal Burn-in Strategy for High Reliable Products Using Convolutional Neural Network

IEEE Access

Yijie Jiang¹  Yun Zhang¹  Kairui Chen¹  Huachuan Li²  Junyan Gao²  Yi Lyu³  Ci Chen⁴ 

[1] School of Automation, Guangdong University of Technology, Guangzhou, China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China;School of Computer, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan, China;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;

关键词: Burn-in;    deep learning;    degradation;    sliding window;    online optimization;   

DOI  :  10.1109/ACCESS.2019.2958570

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown