Offline changepoint detection (CPD) algorithms are used for signal segmentation in an optimal way. Generally, these based on the assumption that signal’s changed statistical properties known, and appropriate models (metrics, cost functions) used. Otherwise, process of proper model selection can become laborious time-consuming with uncertain results. Although ensemble approach is well known incr...

Partial least squares (PLS) and linear regression methods are widely utilized for quality-related fault detection in industrial processes. Standard PLS decomposes the process variables into principal residual parts. However, as part still contains many components unrelated to quality, if these were not removed it could cause false alarms. Besides, although do affect product they have a great im...

Virtual labs are valuable educational resources in control education, and widely used the process industry as tools for operator training decision aid. In these application domains, virtual typically rely on interactive simulation of large-scale hybrid-DAE models with components different engineering whose description can be greatly simplified by use Modelica language. Existing free commercial ...

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...

Causal relations among variables may change significantly due to different control strategies and fault types. Off line-based knowledge is not adequate for diagnosis, existing causal models obtained from data driven methods are mostly based on historical only. However, variable correlation would remain identical, could be very under certain industrial operation conditions. To deal with this pro...

Industrial processes are nonlinear and complicated, requiring accurate fault identification to minimize performance deterioration respond quickly emergencies. This work investigates industrial process defect isolation, which is analytically difficult owing their complexity. paper carefully analyzes four design methods for flaw isolation based on Principal Component Analysis (PCA), Fisher Discri...

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...

Although the unsupervised extreme learning machine (UELM) based methods have been widely used to diagnosis nonlinear process faults recently, UELM algorithm is only designed preserve local adjacency similarity of input dataset instead mining intra-class variations. Besides, determination optimal hidden nodes number a tough issue. In order deal with these two problems, novel enhanced (EUELM) sch...