A new neural network-based control scheme for fault detection and fault diagnosis in fuzzy multivariate multinomial data

In some multivariate statistical control applications, the data of the process cannot be precise and defined linguistically in practice. Using multivariate control charts in such situations with non-precise data leads to misleading results. In this paper, a new neural network-based monitoring scheme is presented by considering fuzzy multivariate multinomial data. The proposed approach is also able to identify the attribute(s) that cause an out-of-control signal. An application example is provided to evaluate the performance of the proposed approach in detecting different shifts as well as diagnosing the out-of-control attribute quality characteristic(s). The results of applying the proposed approach in both fault detection and the fault diagnosis are satisfactory.