Process monitoring using causal map and multivariate statistics: fault detection and identification

Abstract

Data-driven techniques based on multivariate statistics (such as principal component analysis (PCA) and partial least squares (PLS)) have been applied widely to chemical processes and their effectiveness for fault detection is well recognized. There is an inherent limitation on the ability for purely data-driven techniques to identify and diagnose faults, especially when the abnormal situations are associated with unknown faults or multiple faults. The modified distance (DI) and modified causal dependency (CD) are proposed to incorporate the causal map with data-driven approach to improve the proficiency for identifying and diagnosing faults. The DI is based on the Kullback–Leibner information distance (KLID), the mean of the measured variables, and the range of the measured variable. The DI is used to measure the similarity of the measured variable between the current operating conditions and the historical operating conditions. When the DI is larger than the predefined threshold, the variable is identified as abnormal. The CD, derived based on the multivariate T 2 statistic, is used to measure the causal dependency of two variables. When CD is larger than the predefined threshold, the causal dependency of the two variables is broken. The proposed method requires a causal map and historical data associated with the normal operating conditions. A causal map containing the causal relationship between all of the measured variables can be derived based on knowledge from a plant engineer and the sample covariance matrix from the normal data. The DI/CD algorithm outperformed the purely data-driven techniques such as PCA for detecting and identifying known, unknown, and multiple faults using the data sets from the Tennessee Eastman process (TEP).