An integrated methodology for fault detection, root cause diagnosis, and propagation pathway analysis in chemical process systems

Although the current research on fault detection and diagnosis (FDD) has made significant developments, there exists some challenges such as earlier fault detection with lower false alarms, accurate fault diagnosis without prior fault information, and construction of fault propagation pathway. Many articles have contributed to address one or more aforementioned challenges. However, very few of them has attempted to capture these research gaps in a unified framework. Hence, the current article presents an integrated method for process FDD and propagation pathway identification. The methodology is developed combining the early fault detection capacity of the multivariate exponentially weighted moving average principal component analysis (MEMWA-PCA) and robust diagnosis and causality representability of the Bayesian network (BN). MEWMA-PCA can generally detect the fault earlier than PCA with a lower number of false alarms. Multiple likelihood evidence-based BN updating is utilized to ensure accurate diagnosis without in-depth fault information; this BN updating mechanism is found to be superior to the conventional hard evidence-based updating technique. Additionally, for taking corrective actions effectively, fault propagation pathway can be generated using BN. The proposed method is applied to a total of ten fault cases in the benchmark Tennessee Eastman chemical process. It is observed that the developed method is robust and comprehensive compared to the existing PCA and PCA-BN-based hybrid methods.