Fault Diagnosis of Unseen Modes in Chemical Process via Fusing Invariance and Specificity

Data-driven fault diagnosis methods often assume that training and testing data follow identical distributions. However, in chemical processes, variations in operating conditions frequently lead to discrepancies between training and testing data, thereby reducing diagnostic accuracy. While domain-adaptive methods attempt to mitigate these discrepancies, they generally require prior knowledge of operating modes not encountered by the model, thus limiting their applicability. In contrast, domain generalization (DG) techniques have gained attention for their ability to generalize models to unseen modes those the model has never experienced without prior knowledge. Most existing DG methods aim to learn domain-invariant features that are unaffected by operating condition variations from historical data. However, this is challenging because process data typically contain both domain-specific information (e.g., steady-state operating points and production ratios) and domain-invariant information, and the former is often equally valuable for fault diagnosis. To address this challenge, we propose a novel method named domain disentanglement network with entropy-driven weight learning strategies (DDNEL) for diagnosing faults of unseen modes in chemical processes. The entropy optimization algorithm was employed to guide DDNEL in extracting domain-invariant features and domain-specific features, respectively. In the inference phase, an entropy-driven feature fusion mechanism is devised to fuse invariant and specific features for diagnosing the faults of unseen modes. Empirical results on the continuous stirred tank reactor (CSTR), the Tennessee Eastman process (TEP), and a real-world industrial case (Cranfield multiphase flow process) demonstrate the effectiveness of the proposed DDNEL approach.