The fact that modern Networked Industrial Control Systems (NICS) depend on Information and Communication 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 design choices have on the resilience of NICS, e.g., a network architecture using VLAN segmentation. In this paper we investigat...

A feedback-based model predictive control (MPC) approach to product quality improvement that incorporates multivariate statistical techniques has been developed [1]. The objective of the approach is to use existing process measurements to help reduce the variability of product quality when its online measurement is not feasible. The approach is model based and it uses principal component analys...

Performance monitoring, anomaly detection, and root-cause analysis in complex cyber–physical systems (CPSs) are often highly intractable due to widely diverse operational modes, disparate data types, fault propagation mechanisms. This paper presents a new data-driven framework for analysis, based on spatiotemporal graphical modeling approach built the concept of symbolic dynamics discovering re...

Root cause analysis is an important method for fault diagnosis when used with multivariate statistical process monitoring (MSPM). Conventional contribution analysis in MSPM can only isolate the effects of the fault by pinpointing inconsistent variables, but not the underlying cause. By integrating reconstruction-based multivariate contribution analysis (RBMCA) with fuzzy-signed directed graph (...

Identification of faulty variables is an important component of multivariate statistical process monitoring (MSPM); it provides crucial information for further analysis of the root cause of the detected fault. The main challenge is the large number of combinations of process variables under consideration, usually resulting in a combinatorial optimization problem. This paper develops a generic r...

Recently, data mining and machine learning techniques have been increasingly applied in process engineering. Various successful applications include fault detection and development of data driven models. While fault detection is useful for steady operation of the plant, data driven models can be employed for robust prediction of structure activity relationships. Many of these models require non...

Qualitative trend analysis (QTA) is a process-history-based data-driven technique that works by extracting important features (trends) from the measured signals and evaluating the trends. QTA has been widely used for process fault detection and diagnosis. Recently, Dash et al. (2001, 2003) presented an intervalhalving-based algorithm for off-line automatic trend extraction from a record of data...

• An unsupervised fault detection and diagnosis scheme based on orthogonal autoencoders is proposed for the monitoring of industrial chemical processes. The use integrated gradients allows exploration bottleneck autoencoder, providing candidate variables root cause analysis. method performs well compared to traditional methods in offering compelling interpretable results. analysis shows how dif...

Many classical multivariate statistical process monitoring (MSPM) techniques assume normal distribution of the data and independence of the samples. Very often, these assumptions do not hold for real industrial chemical processes, where multiple plant operating modes lead to multiple nominal operation regions. MSPM techniques that do not take account of this fact show increased false alarm and ...