The effective robust management of plant requires the implementation of industrial alarm
systems in a very significant capacity. The core objective of alarms is to warn the operator of …

Industrial operators often experience overwhelming situations during ongoing alarm floods
due to high alarm rates. In such situations, real-time assistance in the form of prediction of …

Digital technologies have been reshaping how the process industries operate. The
extensive use of physical and information digitalization called by ambitious revolution to …

Abstract The conventional Kernel Principal Component Analysis (KPCA)-based fault
detection technique requires more computation time and memory storage space to analyze …

Fault detection and diagnosis (FDD) systems play a crucial role in maintaining the adequate
execution of the monitored process. One of the widely used data-driven FDD methods is the …

Due to the abrupt nature of the chemical process, a large number of alarms are often
generated at the same time. As a result of the flood of alarms, it largely hinders the operator …

Process abnormalities can result in serious accidents that may lead to unexpected loss of
life and property. Early fault detection and diagnosis are essential to prevent these …

Abstract Principal Component Analysis (PCA)-based approach for fault detection is a simple
and accurate data-driven technique for feature extraction and selection. However, PCA …

For the alarm system of nonstationary industrial processes, the conventional alarm
thresholds configured for one single operational zone often result in frequent false and …

Current multivariate statistical process monitoring is mostly based on data-based models
with the principal aim of detecting faults promptly. To increase fault detection performance …