Fault Detection of Biological Phenomena Modeled by S-systems

In this work we propose a novel fault detection (FD) technique in order to enhance monitoring of biological processes. To do that, a new statistical FD method, that is based on combining the advantages of the double exponentially weighted moving average (EWMA), called Max-DEWMA, with those of the particle filtering (PF), and multiscale representation is developed. The advantages of PF-based multiscale (MS) Max-DEWMA (M-DEWMA) are threefold: (i) the dynamical multiscale representation is proposed to extract accurate deterministic features and decorrelate autocorrelated measurements; (ii) PF is proposed to estimate the states of biological processes; (iii) MS-M-DEWMA chart is able to detect smaller fault shifts in the mean/variances and enhance the monitoring of biological processes. The FD performance is studied using Cad System in E. coli (CSEC) model. PF-based MS-M-DEWMA is used to enhance FD of the CSEC model through monitoring some of the key variables involved in this model such as enzymes, lysine and cadaverine. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.