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 …

In modern industrial plants with a variety of interconnected devices and control loops, a fault
may propagate through the information and material flow pathways. Alarms are indicators of …

This paper presents a data-driven methodology for fault detection and diagnosis (FDD) by
integrating the principal component analysis (PCA) with the Bayesian network (BN). Though …

The content of free calcium oxide (f-CaO) in cement clinker is an important index affecting
the quality of cement clinker. Because f-CaO content in cement clinker cannot be measured …

In this era of Industry 4.0, there are continuing efforts to develop fault detection and
diagnosis methods that are fully autonomous; these methods are self-learning, with little or …

In chemical processes, Bayesian network (BN)-based approaches have been extensively
applied for process fault diagnosis. Generally, BN is learned using score and search …

In complex industrial facilities, a fault may generate in a unit and easily propagate to other
units, resulting in degraded performance of process operations and deteriorating the …

The propagation of the developing incipient fault embeds potential risks to the safety
management of industrial processes. The transfer entropy (TE) based causality analysis …

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 …

Alarm flooding is a serious safety problem in the chemical process industries. Bayesian
Networks are a set of powerful tools that can be used to trace the root-cause of alarms. For …