Artificial Intelligence Enhanced Reliability Assessment Methodology With Small Samples

Due to the high price of the product and the limitation of laboratory conditions, reliability tests often get a small number of failed samples. If the data are not handled properly, the reliability evaluation results will incur grave errors. In order to solve this problem, this work proposes an artificial intelligence (AI) enhanced reliability assessment methodology by combining Bayesian neural networks (BNNs) and differential evolution (DE) algorithms. First, a single hidden layer BNN model is constructed by fusing small samples and prior information to obtain the 95% confidence interval (CI) of the posterior distribution. Then, the DE algorithm is used to iteratively generate optimal virtual samples based on the 95% CI and small samples trends. A reliability assessment model is reconstructed based on double hidden layers BNN model by combining virtual samples and test samples in the last stage. In order to verify the effectiveness of the proposed method, an accelerated life test (ALT) of the subsurface electronic control unit (S-ECU) was carried out. The verification test results show that the proposed method can accurately evaluate the reliability life of a product. And compared with the two existing methods, the results show that this method can effectively improve the accuracy of the reliability assessment of a test product.

Automated summary

This paper proposes an enhanced artificial intelligence reliability assessment method by combining Bayesian neural networks and differential evolution algorithms. By fusing small samples and prior information, a reliability assessment model is constructed and reconstructed using virtual samples. The experimental results demonstrate that this method can accurately evaluate the reliability life of a product and improve the accuracy of the assessment.