Abstract
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.
Highlights
Due to the improvement of manufacturing technology, the reliability of various products tends to be higher and their service life is longer, which means that even in the burn-in environment with special settings, the failure time of products is longer than that of general products
The difficulty lies in how to obtain reasonable burn-in time based on given degradation information and apply it to online screening tasks, and this is why deep learning (DL) has a lot of research and application in the field of fault diagnosis, but little development in the burn-in test
During the offline training phase, one-dimensional convolutional neural network(CNN1) is combined with the sliding window strategy to build the relationship between degradation trend and measured data, and here, a well-trained CNN1 will be obtained to conduct the screening task
Summary
Due to the improvement of manufacturing technology, the reliability of various products tends to be higher and their service life is longer, which means that even in the burn-in environment with special settings, the failure time of products is longer than that of general products. Ye et al [25] applied the Wiener process to model the measured degradation and discussed the optimal burn-in plan by jointly considering the burn-in test cost and maintenance expense. The difficulty lies in how to obtain reasonable burn-in time based on given degradation information and apply it to online screening tasks, and this is why DL has a lot of research and application in the field of fault diagnosis, but little development in the burn-in test. During the offline training phase, one-dimensional convolutional neural network(CNN1) is combined with the sliding window strategy to build the relationship between degradation trend and measured data, and here, a well-trained CNN1 will be obtained to conduct the screening task.
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