Comparative Performance Analysis of Alternative Approaches for Gross Error Detection and Identification

ALVAREZ MEDINA CARLOS RODRIGO; ADRIANA BRANDOLÍN; MABEL SÁNCHEZ

Salt Lake City- Utah- USA

Congreso; 2007 AIChE Annual Meeting; 2007

American Institute of Chemical Engineers

The D-statistic is widely used in Statistical Process Control to reliably detect the out of control status, but by itself it offers no assistance as fault identification tool. Different strategies have been proposed to calculate the contribution of each process variable to the inflated statistic. They work in the original or in the latent variable space. Mason et al. (1995, 1997) presented a strategy to decompose the D-statistic into the contributions of each variable, which is intended to identify the fault source. Due to the combinatorial nature of the formulation, a great number of decompositions are obtained, increasing the complexity of the identification procedure. A straightforward method to decompose the D statistic as a unique sum of each variable contribution was recently developed by Alvarez et al. (2006, 2007), which is called OSS (Original Space Strategy).  This decomposition provides a clear understanding about the physical meaning of the negative contributions and estimates a bound for them.      In this work, the detection and identification capabilities of the monitoring technique presented by Alvarez et. al (2007), are compared with those corresponding to the most commonly used gross error detection and identification techniques for some benchmarks.