Nonlinear Detection and Isolation of Multiple Faults Using Residuals Modeling

Abstract

This paper proposes a model-based detection and isolation (FDI) system based on nonlinear state estimation that can be applied to nonlinear systems. The proposed FDI system uses an extended Kalman filter (EKF), in which conditions based on high filtering are defined to best serve the FDI objectives. A better understanding of the residual trends, calculated from the difference between measurements and the EKF estimates, can be obtained when a fault occurs by developing a model that is able to predict the behavior of the residuals. This model is utilized as the basis for detection and isolation of single and multiple faults. Comparisons with data driven techniques, specifically principal component analysis (PCA) and Kernel PCA, show superior isolation results, having the advantage of distinguishing single and multiple faults from a diverse array of possible faults, a common occurrence in complex processes. The proposed approach is validated using an experimental air heater.

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

“…This is because both approaches entail differentiating the statistical index, which is difficult if the chain involves a kernel function [86]. Nevertheless, many researchers have derived analytical expressions for either kernel contributions-based diagnosis [66,79,81,83,87,94,119,127,133,136,146,150,156,157,162,164,194,213,241,268,275,276,278,279,288,289,293] or kernel reconstructions-based diagnosis [86,117,140,155,161,163,176,217,236,254,265,285]. However, most derivations are applicable only when the kernel function is the RBF, Equation (5).…”

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

“…This is because both approaches entail differentiating the statistical index, which is difficult if the chain involves a kernel function [86]. Nevertheless, many researchers have derived analytical expressions for either kernel contributions-based diagnosis [66,79,81,83,87,94,119,127,133,136,146,150,156,157,162,164,194,213,241,268,275,276,278,279,288,289,293] or kernel reconstructions-based diagnosis [86,117,140,155,161,163,176,217,236,254,265,285]. However, most derivations are applicable only when the kernel function is the RBF, Equation (5).…”

kNN based on probability density for fault detection in multimodal processes

“…The methods of process monitoring and fault detection are divided into 3 categories: model‐based methods, knowledge‐based methods, and data‐based methods. Compared with the first 2 methods, the data‐based methods have no requirement of a priori process model and associated expert knowledge, which has made them attract more popular attention in recent years …”

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

“…This is because both approaches entail differentiating the statistical index, which is difficult if the chain involves a kernel function [86]. Nevertheless, many researchers have derived analytical expressions for either kernel contributions-based diagnosis [66,79,81,83,87,94,119,127,133,136,146,150,156,157,162,164,194,213,241,268,275,276,278,279,288,289,293] or kernel reconstructions-based diagnosis [86,117,140,155,161,163,176,217,236,254,265,285]. However, most derivations are applicable only when the kernel function is the RBF, Equation (5).…”

kNN based on probability density for fault detection in multimodal processes

“…The methods of process monitoring and fault detection are divided into 3 categories: model‐based methods, knowledge‐based methods, and data‐based methods. Compared with the first 2 methods, the data‐based methods have no requirement of a priori process model and associated expert knowledge, which has made them attract more popular attention in recent years …”

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

“…This is because both approaches entail differentiating the statistical index, which is difficult if the chain involves a kernel function [86]. Nevertheless, many researchers have derived analytical expressions for either kernel contributions-based diagnosis [66,79,81,83,87,94,119,127,133,136,146,150,156,157,162,164,194,213,241,268,275,276,278,279,288,289,293] or kernel reconstructions-based diagnosis [86,117,140,155,161,163,176,217,236,254,265,285]. However, most derivations are applicable only when the kernel function is the RBF, Equation (5).…”

kNN based on probability density for fault detection in multimodal processes

“…The methods of process monitoring and fault detection are divided into 3 categories: model‐based methods, knowledge‐based methods, and data‐based methods. Compared with the first 2 methods, the data‐based methods have no requirement of a priori process model and associated expert knowledge, which has made them attract more popular attention in recent years …”