Fault Detection and Diagnosis for Gas Density Monitoring using Multivariate Statistical Process Control

Abstract

Malfunction of plant equipment, instrumentation and degradation in process operation increase the operating cost of any chemical process industries. Thus, modern chemical industries need to operate as fault free as possible because faults that present in a process increase the operating cost due to the increase in waste generation and products with undesired specifications. Effective monitoring strategy for early fault detection and diagnosis is very important not only from a safety and cost viewpoint, but also for the maintenance of yield and the product quality in a process as well. Therefore, an efficient fault detection and diagnosis algorithm needs to be developed to detect faults that are present in a process and pinpoint the cause of these detected faults. Multivariate analysis technique i.e., Principal Component Analysis (PCA) and Partial Correlation analysis (PCorrA) are used to determine the correlation coefficients between the process variables and quality variables while control chart with the calculated correlation coefficients are used to facilitate the Fault Detection and Diagnosis (FDD) algorithm. A procedure for FDD has been described in this study and the proposed method is demonstrated on an Air Flow Pressure Temperature (AFPT) control system pilot plant. Results show that method based on PCA and PCorrA was able to detect the pre-designed faults successfully and identify variables which cause the faults.