Industrial control systems are a vital part of the critical infrastructure. The potentially large impact of a failure makes them attractive targets for adversaries. Unfortunately, simplistic approaches to intrusion detection using protocol analysis or näıve statistical estimation techniques are inadequate in the face of skilled adversaries who can hide their presence with the appearance of legi...

Dynamic trend analysis is an important technique for fault detection and diagnosis. Trend analysis involves hierarchical representation of signal trends, extraction of the trends, and their comparison (estimation of similarity) to infer the state of the process. In this paper, an overview of some of the existing methods for trend extraction and similarity estimation is presented. A novel interv...

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-...

Nonlinearity in industrial processes such as chemical and biological processes is still a significant problem. Kernel principal component analysis (KPCA) has recently proven to be a powerful tool for monitoring nonlinear processes with numerous mutually correlated measured variables. One of the drawbacks of original KPCA is that computation time increases with the number of samples. In this art...

Statistical data-driven process monitoring is critical for efficient operations of industrial processes. However, deviations from normal regions in the process data may or may not lead to poor quality of products. This paper proposes a new combined index for detecting output-relevant faults, which affect the output data, and studies the output-relevant fault detectability based on total project...

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...

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...

Recently, neural networks (NNs) have been proposed for the detection of cyber attacks targeting industrial control systems (ICSs). Such detectors are often retrained, using data collected during system operation, to cope with evolution monitored signals over time. However, by exploiting this mechanism, an attacker can fake provided corrupted sensors at training time and poison learning process ...

Traditional onefold data-driven methods for fault detection in complex process industrial systems with high-dimensional, linear, nonlinear, Gaussian, and non-Gaussian coexistence often have less than satisfactory monitoring performance because only a single distribution of variables is considered. To address this problem, hybrid model based on PCA-KPCA-ICA-KICA-BI (Bayesian inference) proposed,...

Principal component analysis (PCA) and Partial least square (PLS) are powerful multivariate statistical tools that have been successfully applied for process monitoring. They are efficient in dimension reduction and are suitable for processing large amount of data. Nevertheless, their application scope is restricted to static processes where the dynamics are ignored. In order to achieve improve...