Two-Dimensional Contribution Map for Fault Identification [Focus on Education]

All control engineers should be able to detect and iden-tify faults (that is, abnormal conditions in a system) from the analysis of large heterogeneous time-series data sets. This "Focus on Education" column provides an introduction to multivariable data-based methods for fault detection and fault identification, with the latter being the determination of system variables that contribute the most to a detected fault. For fault identification in statistical process monitoring, the contribution plot is the most com-monly used tool for quickly identifying the most affected variables. Contribution calculations are revisited in the context of principal component analysis (PCA) and T2 statistics, and a two-dimensional (2-D) contribution map is illustrated for the examination of time-series data under faulty conditions. The 2-D contribution map is compared to the traditional one-dimensional (1-D) contribution plot using simulated data from a realistic chemical process. The 2-D contribution map demonstrates the potential to enable a greater understanding of the fault and how its effects are propagated through the system.