The purpose of this article is to present and evaluate the performance of a new procedure for industrial process diagnosis. This method is based on the use of a bayesian network as a classifier. But, as the classification performances are not very efficient in the space described by all variables of the process, an identification of important variables is made. This feature selection is made by...

Projection to latent structures (PLS)model has beenwidely used in quality-related processmonitoring, as it can establish amapping relationship between process variables and quality index variables. To enhance the adaptivity of PLS, kernel PLS (KPLS) as an advanced version has been proposed for nonlinear processes. In this paper, we discuss a new total kernel PLS (T-KPLS) for nonlinear quality-r...

In order to better utilize historical process data from faulty operations, supervised learning methods, such as Fisher discriminant analysis (FDA), have been adopted in process monitoring. However, such methods can only separate known faults from normal operations, and they have no means to deal with unknown faults. In addition, most of these methods are not designed for handling non-Gaussian d...

Many industrial processes are operated in multiple modes due to different manufacturing strategies. Multimodality of process data is often accompanied with nonlinear and non-Gaussian characteristics, which makes data-driven monitoring more complicated. In this paper, statistics pattern analysis (SPA) introduced extract low- high-order from raw data. Support vector description (SVDD), can deal p...

Chemical process variables are always driven by random noise and disturbances. The closed-loop control yields process measurements that are auto & cross correlated. The influence of auto & cross correlations on statistical process control (SPC) is investigated in detail. It is revealed both auto and cross correlations among the variables will cause unexpected false alarms. Dynamic PCA and ARMA-...

Fault detection and diagnosis (FDD) is a critical approach to ensure safe and efficient operation of manufacturing and chemical processing plants. Multivariate statistical process monitoring (MSPM) has received considerable attention for FDD since it does not require a mechanistic process model. The diagnosis of the source or cause of the detected process fault in MSPM largely relies on contrib...

Process monitoring based on neural networks is getting more and attention. Compared with classical networks, high-order have natural advantages in dealing heteroscedastic data. However, might bring the risk of overfitting, which learning both key information from original data noises or anomalies. Orthogonal constraints can greatly reduce correlations between extracted features, thereby reducin...

This paper proposes a parallel monitoring method for plant-wide processes by integrating mutual information and Bayesian inference into global-local preserving projections (GLPP)-based multi-block framework. Unlike traditional multivariate statistic process (MSPM) methods, the proposed MI-PGLPP transforms several sub-block monitoringtasks fully taking advantage of distributed First, original da...

Due to recent increase in deployment of Cyber-Physical Industrial Control Systems different critical infrastructures, addressing cyber-security challenges these systems is vital for assuring their reliability and secure operation presence malicious cyber attacks. Towards this end, developing a testbed generate real-time data-sets infrastructure that would be utilized validation attack detection...