In this article, a novel dynamic Bayesian network based networked process monitoring approach is proposed for fault detection, propagation pathway identification and root cause diagnosis. First, process network structure is designed according to the prior process knowledge including process flow sheets and used to characterize the causal relationships among different measurement variables. Then...

Attacks on industrial control systems (ICSs) can lead to significant physical damage. While off-line safety and security assessments provide insight into vulnerable system components, they may not account for stealthy attacks designed evade anomaly detectors during long operational transients. In this article, we propose a predictive online monitoring approach check the of under potential false...

Decentralized monitoring methods, which divide the process variables into several blocks and perform local for each sub-block, have been gaining increasing attention in large-scale plant-wide due to complexity of their processes. In such dynamic nature data is a relevant issue not usually managed. Here, new data-driven distributed scheme proposed deal with this issue, integrating regression aut...

To overcome the disadvantage of linear dissimilarity analysis (DISSIM) when monitoring nonlinear processes, a kernel dissimilarity analysis algorithm, termed KDISSIM here, is presented, which is the nonlinear version of DISSIM algorithm. A kernel dissimilarity index is introduced to quantitatively evaluate the differences between nonlinear data distribution structures, which can reflect the cha...

This thesis is about the multivariate process monitoring (detection and diagnosis) withbayesian networks. It allows to unify in a same tool (a bayesian network) some monitoring ded-icated methods like multivariate control charts, discriminant analysis and the MYT method.The first chapter gives some essential points of the process monitoring, with a study of differ-ent approa...

Compared to large process faults, the latent and small ones are difficult to be detected. However, the accumulation of these faults may even more harmful to the process. A novel fault detection and diagnosis method is proposed which is based on similarity factor and a variable moving window. The new method is based on the idea that a change of process can be reflected in the distribution of the...

Effective monitoring of industrial processes provides many benefits. However, for dynamic processes with strong nonlinearity many existing techniques still cannot give satisfactory monitoring performance. This is evidenced by the well known Tennessee Eastman (TE) benchmark process, where some faults, e.g. Faults 3 and 9, have not been comfortably detected by almost all data-driven approaches pu...

Contribution analysis in multivariate statistical process monitoring (MSPM) identifies the most responsible variables to the detected process fault. In multivariate contribution analysis, the main challenge of fault isolation is to determine the appropriate variables to be analysed and this usually results in a combinatorial optimisation problem. Reconstruction-based multivariate contribution a...

Cyberattacks against Industrial Control Systems (ICS) can have harmful physical impacts. Investigating such attacks be difficult, as evidence could lost to damage. This is especially true with stealthy ; i.e., that evade detection. In this paper, we aim engineer Forensic Readiness (FR) in safety-critical, geographically distributed ICS, by proactively collecting potential of attacks. The collec...