MFD-Mamba: A Mamba-Based Framework for Multiple-Fault Process Monitoring in Industrial Systems

Multiple faults in industrial processes may occur simultaneously or successively, leading to abnormal responses distributed across coupled process regions and evolving over time. This paper proposes a Mamba-based multiple-fault detection framework, termed MFD-Mamba, for dynamic industrial processes. A hybrid multiblock decomposition first combines lag-aware data relationships with prior process connections to construct soft variable memberships and a block-relation prior. The resulting blockwise sequences are then modeled by a shared modified Mamba encoder, while dynamic block relation modeling is used to describe interactions among process regions. Blockwise process predictions are further used to construct hybrid residuals. For fault detection, block residuals are converted into probabilistic evidence and fused within a Bayesian framework, where multiple residual-based monitoring statistics, persistence information, and cross-evidence terms are jointly incorporated to construct the final monitoring index. The proposed method is evaluated on the Tennessee Eastman process and the activated sludge process under overlapping and sequential multiple-fault conditions. On the TE process, MFD-Mamba achieves FDR/FAR values of 99.88%/0.13% and 99.81%/0.20% for the overlapping and sequential fault scenarios, respectively. On the ASP, the corresponding FDR/FAR values are 99.79%/0.78% and 99.58%/1.56%. These results demonstrate that MFD-Mamba provides accurate and reliable multiple-fault detection across different dynamic industrial processes.