Monitoring and diagnosis of multistage manufacturing processes using hierarchical Bayesian networks

In recent years, manufacturing systems have given rise to manufacturing big data due to the rapid developments in sensor and information technology and that has fueled data-driven research techniques towards addressing the issues in multistage quality control and diagnosis. In this paper, a unified framework with dual Hierarchical Bayesian Networks (HBNs) has been presented for simultaneous online process monitoring and fault diagnosis of a multistage manufacturing system. To achieve this, a novel AMDS (Absolute Mean Deviation of States) control chart has been developed for monitoring the unobserved inputs. The AMDS control chart is built on the AMDS statistic, which is calculated using the inferred states distribution generated utilizing the HBNs of the unobserved inputs. Discrete event simulation results of the two-stage process demonstrate that the methodology can successfully detect process changes and diagnose the root causes of the change. In addition, it can also identify the time at which the fault has occurred and the type (mean shift or variance change) and nature (step faults or slow drifts) of the change. The robustness of the proposed methodology is extensively tested against multiple randomly generated non-linear quadratic process models for two-stage systems.