Multiscale Kernel PLS-Based Exponentially Weighted-GLRT and Its Application to Fault Detection

In this paper, we develop an improved statistical technique in order to enhance monitoring of biological processes. To improve the performance of monitoring, a detection statistic that exploits the advantages of the generalized likelihood ratio test (GLRT) statistic with those of the exponentially weighted moving average filter, kernel partial least square (KPLS) model, and multiscale representation is developed. The advantages of multiscale (MS) KPLS-based exponentially weighted GLRT (EW-GLRT) are threefold: First, the developed EW-GLRT statistic takes into account the information given by the current and previous data by giving high importance to the more recent data; second, the dynamical multiscale representation is proposed to extract accurate deterministic features and decorrelate autocorrelated measurements; third, the MSKPLS model evaluates the KPLS of the wavelet coefficients at each scale. Due to its multiscale nature, MSKPLS is appropriate for modeling of data that contain contributions from events whose behavior changes over time and frequency. The detection performance is studied using Cad System in E. coli model and genomic copy number data for detecting small and moderate shifts. MSKPLS-based EW-GLRT is used to enhance fault detection of the Cad System in E. coli model through monitoring some of the key variables involved in this model, such as enzymes, lysine, and cadaverine. The proposed technique is also applied to detect diseases using genomic copy number data through better detection of aberrations in the genetic information of patients, which can help medical doctors make more accurate diagnosis of diseases.