Fault detection and diagnosis of chemical process using enhanced KECA

- A new MSPCA-KECA-based method is developed for fault detection and diagnosis.
- MSPCA is proposed to extract fault-symptom features for multi-scale problem.
- Each KECA classifier is dedicated to a specific fault.
- The Cauchy-Schwarz (CS) divergence is a measure of the similarity between two probability density functions.
- Results show that the proposed method outperforms KPCA, KICA and KECA.

As the main concerns of abnormal event management in process engineering, fault detection and diagnosis have attracted more and more attention recently. A new monitoring method based on kernel entropy component analysis(KECA) is proposed for nonlinear chemical process. Then, an angle-based statistic is designed to express the distinct angular structure that KECA reveals, which is able to measure the similarity between probability density functions. Likewise, each KECA classifier is dedicated to a specific fault, which provides an expendable framework for incorporating new faults identified in the process. As to the fault features are submerged because of multi-scale property of process data, an enhanced KECA method for fault detection and diagnosis is developed, by adding multi-scale principal component analysis(MSPCA) for features extraction to improve the classification effect of KECA. The effectiveness of the proposed approach is demonstrated by applying to Tennessee Eastman process. The MSPCA based method essentially captures the fault-symptom correlation, whereas KECA can be an effective method for process fault diagnosis.