A generic quality and accuracy driven uncertainty quantification framework for reliability analysis

In practical engineering, massive data are commonly utilized for data prediction and system reliability analysis. However, the presence of inevitable data noise can render reliability analysis results inaccurate, thereby necessitating the data uncertainty quantification. Moreover, current studies struggle to ensure both prediction accuracy and uncertainty quality simultaneously, which complicates the uncertainty quantification. To address these problems, this paper proposes a generic quality and accuracy driven uncertainty quantification framework based on deep learning methods. The proposed quality and accuracy driven sampling loss function builds the bridge between the significance level and quantile level, and constrains the mean prediction, upper and lower interval limits, significantly improving prediction accuracy and interval quality. The randomly sampled significance level is regarded as an input feature to derive the prediction interval at an aleatory confidence level, avoiding inaccuracies in reliability analysis. Additionally, the proposed framework is a universal one, applicable to any modeling method, and possesses high engineering practicability. Two numerical examples and one practical engineering case are adopted to verify the effectiveness. Results demonstrate our proposed method achieves higher prediction accuracy and uncertainty quality compared to other methods. This advancement offers credible information essential for interval reliability analysis and system health condition assessment.