This paper presents an overview of graphical methods used for robust Fault Detection and
Isolation (FDI) that can be employed for monitorability and diagnosability analysis and/or …

Digital twins are proposed to manage the industrial production sites by mapping physical
space entities to virtual spaces. Existing digital twin modeling methods are mainly realized …

The chemical process industry has become the backbone of the global economy. The
complexities of chemical process systems have been increased in the last two decades due …

This work concerns robust sensor fault detection observer (SFDO) design for uncertain and
disturbed discrete‐time Takagi–Sugeno (T–S) systems using H−∕ H∞ criterion. The …

In recent decades, machine learning (ML) techniques have been effectively applied for
industrial process monitoring to assure safety and high-quality yield. Traditional process …

This study reviews the latest works on performance modelling and fault diagnosis of fuel cell
systems during the past few years. The fuel cell is a promising alternative power source for …

The complexity of industrial processes imposes a lot of challenges in building accurate and
representative causal models for abnormal events diagnosis, control and maintenance of …

In the field of alternative electrical power generation, the durability of the energy presents a
great challenge to science. Fuel Cells (FCs) are considered as one of the future promising …

This study presents a data-based methodology for fault detection and isolation in dynamic
systems based on fuzzy/Bayesian approach for change point detection associated with a …

The paper deals with the use of coupled bond graph as an integrated decision tool for health
monitoring of chemical reactors. A bond graph model based diagnostic strategy is adopted …