OSF-JS: Orthonormal Slow Feature-Based Quality-Related Data Decomposition Network With Jensen–Shannon Divergence for Chemical Processes Incipient Fault Detection

In chemical processes, there are both nonlinearity and slow dynamics among process variables. Meanwhile, at the beginning of faults, slight variation may not prompt the significant parametric changes. Traditional fault detection methods, based on mean and variation shifts, often fail to achieve satisfactory performance. To improve the detection of incipient quality-related faults, a novel orthonormal slow feature-based quality-related data decomposition network with Jensen-Shannon divergence (OSF-JS) fault detection method is proposed. The orthonormal subspace analysis (OSA) is embedded with Siamese to extract orthonormal slow features (OSFs) from decomposed subspaces. Enhancing the detection of incipient faults, JS divergence combined with a sliding window algorithm is introduced to construct statistics. The effectiveness of OSF-JS is validated for a wastewater treatment process and a real-world chemical process. Results show that the proposed OSF-JS method not only improves the fault detection rate (FDR) and reduces false alarm rate (FAR) but also identifies whether faults occur in the quality-related component.