Fault Propagation Analysis for Manufacturing Process Monitoring via a Temporal Causal Modeling Algorithm

Manufacturing processes frequently experience fault propagation when an initial fault triggers widespread alarms through informational or physical relationships. Effective fault propagation analysis is, therefore, essential to guarantee production safety by accurately identifying fault propagation pathways, thereby preventing cascading failures in process monitoring systems. However, the majority of existing methodologies in the relevant literature provide unreliable fault propagation monitoring outcomes, primarily because they overlook the accuracy of causality estimators and fail to adequately consider the significance of causal variables. To address these limitations, this article introduces a nonlinear multivariate Lasso Granger (LG) approach specifically designed for fault propagation analysis. To reduce ambiguous causal pathways, a many-to-one neural network architecture, rather than conventional pairwise networks, is employed for extracting LG causality. Furthermore, an input attention mechanism is integrated into the network to adaptively identify and prioritize relevant driving variables. Subsequently, a Monte Carlo-based significance test is devised to determine an appropriate threshold for the LG causality estimator, effectively eliminating spurious causal paths. The effectiveness and practical applicability of the proposed fault propagation analysis approach are validated through numerical simulations and computer numerically controlled machining case studies. Finally, the obtained results demonstrate the practicality and superior performance of the developed methodology.