LAUSR

Abstract In most manufacturing processes, identifying the faulty process variables that may lead to process changes is crucial for quality engineers and practitioners. There are several parametric procedures for identifying faulty variables with the assumption that they follow multivariate normal distributions. However, in practice, the normality assumption restricts the applicability of such procedures in identifying the faulty variables. In addition, conventional procedures for fault identification are often computationally expensive, especially in high-dimensional processes. Therefore, this article proposes a data-driven Bayesian approach for fault identification that addresses the limitations posed by the normality assumption. The proposed approach is computationally efficient for high-dimensional data compared with existing approaches. Experimental results with various simulation studies and real-life data sets demonstrate the effectiveness of the proposed procedure.