The batch process is a batch-repeated production process, which shows a multiple modal switching within the batch. This makes it difficult to use a single-mode analysis method to achieve accurate modeling and fault diagnosis. Therefore, a novel two-step phase partition idea is proposed based on improved affinity propagation (AP) clustering and sub-phase similarity diminishing scan (PSDS) method. In order to capture the dynamic characteristics of the modes switching, the improved AP clustering is used for phase preliminary partition, in which an effective method that is more suitable for complex batch process is proposed to calculate the similarity. For sub-phases generated by the phase preliminary partition, the internal process of each sub-phase also varies obviously with the development of duration, so an innovative method PSDS is proposed to implement phase fine partition. Then each sub-phase scanned by the PSDS method is identified and divided into stable parts and transition parts, which further reflects the change trend within the sub-phase. For the outliers and misclassification points that may arise during the process of phase partition, the solutions are put forward, respectively. Thus, the partition results with different characteristics are modeled and monitored separately by using the method of principal component analysis (PCA). A practical application on batch process, aircraft steering engine platform fault diagnosis experiment, is given to conform the feasibility and performance of the proposed method.
Phase Partition and Fault Diagnosis of Batch Process Based on KECA Angular Similarity
