Uncertainty utilization in fault detection using Bayesian deep learning

Up to now, extensive literature on the usage of deep learning in manufacturing can be found. Though, actual usage of deep learning in manufacturing sites is somehow restrained by the quality of the obtained data, especially for machine failure cases. This article proposes an approach for utilizing the prediction uncertainty information generated by Bayesian deep learning models to improve decision-making in fault detection. Inference is carried out using Automatic Differentiation Variational Inference (ADVI), and the resultant prediction uncertainty information is utilized to enhance fault detection. The proposed approach is applied to an open-source dataset and a real case study on Vertical Continuous Plating (VCP) of printed circuit boards. The experiments show that the performance of the proposed scheme is considerably beneficial compared to classical deep learning models.