A local and global statistics pattern analysis method and its application to process fault identification

Traditional principal component analysis (PCA) is a second-order method and lacks the ability to provide higher-order representations for data variables. Recently, a statistics pattern analysis (SPA) framework has been incorporated into PCA model to make full use of various statistics of data variables effectively. However, these methods omit the local information, which is also important for process monitoring and fault diagnosis. In this paper, a local and global statistics pattern analysis (LGSPA) method, which integrates SPA framework and locality preserving projections within the PCA, is proposed to utilize various statistics and preserve both local and global information in the observed data. For the purpose of fault detection, two monitoring indices are constructed based on the LGSPA model. In order to identify fault variables, an improved reconstruction based contribution (IRBC) plot based on LGSPA model is proposed to locate fault variables. The RBC of various statistics of original process variables to the monitoring indices is calculated with the proposed RBC method. Based on the calculated RBC of process variables' statistics, a new contribution of process variables is built to locate fault variables. The simulation results on a simple six-variable system and a continuous stirred tank reactor system demonstrate that the proposed fault diagnosis method can effectively detect fault and distinguish the fault variables from normal variables.

Fault F1 is the step change in feed flow rate in CSTR. After fault F1 is detected using the LGSPA-based method, the LGSPA-based RBC method is applied to identify the fault variable. The proposed RBC plots for Dp and Dr indices identify the root cause of the fault correctly with large difference between the fault variable and normal variables, which ensures clear identification with high contrast degree.Figure optionsDownload as PowerPoint slide