Reliable Fault Detection and Diagnosis of Large-Scale Nonlinear Uncertain Systems Using Interval Reduced Kernel PLS

Abstract

Kernel partial least squares (KPLS) models are widely used as nonlinear data-driven methods for faults detection (FD) in industrial processes. However, KPLS models lead to irrelevant performance over long operation periods due to process parameters changes, errors and uncertainties associated with measurements. Therefore, in this paper, two different interval reduced KPLS (IRKPLS) models are developed for monitoring large scale nonlinear uncertain systems. The proposed IRKPLS models present an interval versions of the classical KPLS model. The two proposed IRKPLS models are based on the Euclidean distance between interval-valued observations as a dissimilarity metric to keep only the more relevant and informative samples. The first proposed IRKPLS technique uses the centers and ranges of intervals to estimate the interval model, while the second one is based on the upper and lower bounds of intervals for model identification. These obtained models are used to evaluate the monitored interval residuals. The aforementioned interval residuals are fed to the generalized likelihood ratio test (GLRT) chart to detect the faults. In addition to considering the uncertainties in the input-output systems, the new IRKPLS-based GLRT techniques aim to decrease the execution time when ensuring the fault detection performance. The developed IRKPLS-based GLRT approaches are evaluated across various faults of the well-known Tennessee Eastman (TE) process. The performance of the proposed IRKPLS-based GLRT methods is evaluated in terms of missed detection rate, false alarms rate, and execution time. The obtained results demonstrate the efficiency of the proposed approaches, compared with the classical interval KPLS. INDEX TERMS Kernel PLS (KPLS), interval KPLS (IKPLS), interval reduced KPLS (IRKPLS), fault detection (FD), uncertain systems.

SVM Based on Gaussian and Non-Gaussian Double Subspace for Fault Detection

“…The KS test method is used to analyze the normal distribution characteristics of the TE process variables. Variables 7,9,13,16,18,19,37,38,39,41 and 50 obey a non-Gaussian distribution, and the other variables obey a Gaussian distribution. A PCA model is established for the data that obey a Gaussian distribution, and an ICA model is established for the data that obey a non-Gaussian distribution.…”

“…This method improves the process monitoring capabilities of KPCA. To monitor large-scale nonlinear uncertain systems, Fezai et al [13] developed interval reduced kernel partial least squares (IRKPLS) models. The method considers the errors and imprecision of the measurement devices and uncertainties in the system.…”

An Enhanced Unsupervised Extreme Learning Machine Based Method for the Nonlinear Fault Detection

“…Fezai et al [7] discussed an online reduced kernel PCA based fault detection method to tackle the conventional kernel PCA's limitation of monitoring dynamic systems with large training dataset. In order to cope with the process parameters changes, measurements' errors and uncertainties over the long operation periods, two improved interval reduced kernel PLS models were proposed to monitor large scale nonlinear uncertain systems in the literature [8]. Nevertheless, during the dimensionality reduction procedure, these nonlinear extensions omit the detailed local adjacency similarity structure among neighboring samples, because they only focus on the diversity information (i.e., the intra-class variations) of the samples.…”