In this paper, a modified independent component analysis (ICA) and its application to process monitoring are proposed. The basic idea of this approach is to use the modified ICA to extract some dominant independent components from normal operating process data and to combine them with statistical process monitoring techniques. The proposed monitoring method is applied to fault detection and ide...

The fact that modern Networked Industrial Control Systems (NICS) depend on Information and Communications Technologies (ICT) is well known. Although many studies have focused on the security of NICS, today we still lack a proper understanding of the impact that network parameters, e.g. network delays, packet losses, background traffic, and network design decisions, have on cyber attacks targeti...

This paper elucidates the methodology of employing agent-based technique for detecting and diagnosing faults in chemical processes. The proposed agent methodology has been shown to be able to effectively combine various fault diagnosis methodology and exploit the capability of parallel computing technology in the process plants. We also show that the integration and collaboration among heteroge...

The purpose of this article is to present a method for industrial process diagnosis with Bayesian network, and more particularly with Conditional Gaussian Network (CGN). The interest of the proposed method is to combine a discriminant analysis and a distance rejection in a CGN in order to detect new types of fault. The performances of this method are evaluated on the data of a benchmark example...

Statistics pattern analysis (SPA) is a new multivariate statistical monitoring framework proposed by the authors recently. It addresses some challenges that cannot be readily addressed by the commonly used multivariate statistical methods such as principal component analysis (PCA) in monitoring batch processes in the semiconductor industry. It was later extended to the monitoring of continuous ...

In this paper, a multivariate fault prognosis approach for continuous processes with hidden faults is proposed based on statistical process monitoring methods and multivariate time series prediction. It is assumed that the fault is a slowly time-varying autocorrelated process and can be completely reconstructed. Fault magnitude is estimated first via reconstruction, then predicted by a vector A...

The purpose of this article is to present a new procedure for industrial process diagnosis. This method is based on bayesian classifiers. A feature selection is done before the classification between the different faults of a process. The feature selection is based on a new result about mutual information that we demonstrate. The performances of this method are evaluated on the data of a benchm...

For using process operational data to realize process monitoring, kinds of improved Principal Components Analysis (PCA) have been applied to cope with complex industrial processes. In this paper, a novel nonlinear wavelet packet PCA (NLWPPCA) method, which combines input training network with wavelet packet PCA, is proposed. Wavelet packet PCA integrates ability of PCA to de-correlate the varia...