KPI-oriented process monitoring based on causal-weighted partial least squares

With the increasing demand for product quality, Key performance indicator (KPI)-oriented process monitoring plays an important role in modern industrial. Partial least squares (PLS)-based methods are widely adopted for KPI-oriented process monitoring, in which the process variable space is decomposed into a principal component subspace that has a strong correlation with KPIs and a residual subspace unrelated to KPIs, and the two spaces are monitored separately. However, if a KPI-unrelated fault occurs in a variable that has no causal relation with any KPI, but has a spurious correlation with some KPIs because of the existence of confounders, PLS based methods may mis-regard the KPI-unrelated fault as a KPI-related fault. This occurs because as a correlation analysis-based method, PLS cannot discriminate whether a variable is a real cause of KPIs or has a spurious correlation with KPIs. Motivated by this, this paper proposes two causal-weighted PLS methods for KPI-oriented process monitoring by combining the LiNGAM-based causal discovery with PLS, which first calculate causal weights based on the modified LiNGAM algorithm and bootstrap strategy, and then employ the causal weights to reweight the weight vectors of PLS to enhance the influence of causal variables in the KPI-related principal subspace and reduce the influence of spurious correlations. Case studies based on a simulated dataset, Tennessee Eastman Process dataset and field data from finishing rolling mill process show that the proposed methods can significantly reduce the false alarm rate for KPI-unrelated faults (i.e., the probability that a KPI-unrelated fault sample is mis-regarded as a KPI-related fault sample) caused by spurious correlation without significantly compromising the fault detection rate of KPI-related faults.