A Two-Layer Distributed Fault Diagnosis Method Based on Correlation Feature Transfer for Large-Scale Sequential Process Industries

Plant-wide process monitoring and fault diagnosis are key technologies to ensure the high-efficiency and safety of the production. This article pays close attention to the new characteristics in modern large-scale sequential industries, including long process, multiunit cooperation, and strong coupling among subblocks. A two-layer distributed fault diagnosis method based on feature transfer is proposed. By characterizing the coupling correlation between subblocks and imposing the feature heredity of adjacent subblocks into plant-wide process modeling, a distributed fault diagnosis framework at the subblock layer and the process layer is developed. First, the process decomposition is performed and we obtain several sequential subblocks. For each subblock, the local tangent space alignment (LTSA) algorithm is employed to perform the nonlinear dimension reduction. Then, canonical correlation analysis (CCA) is used to characterize the coupling relationships among subblocks and the internal unique features of each subblock and the external features related to other subblocks are extracted by CCA. In the feature extraction process, we use the external features as the input of the next adjacent subblock to model the chain feature transfer effect between subblocks. On this basis, the internal and external monitoring statistics for each subblock are constructed and the corresponding control limits are determined by kernel density estimation (KDE). In this way, the subblock layer monitoring model is developed, where the internal fluctuations and underlying propagation tendency of anomalies can be monitored in a fine-scale manner. The plant-wide process monitoring results are reached based on all the monitoring submodels via Bayesian fusion. Finally, the internal and external statistics-based contribution plots are designed for fault diagnosis and the fault-relevant variables can be located. The proposed framework is testified on a real hot strip mill process and the results show its effectiveness.