Data-Driven Design of Fault Diagnosis Systems

Nonlinear Multimode Processes
Erscheinungsjahr 2014
ISBN: 978-3-658-05806-7
Verlag: Springer

Buch, Englisch, 136 Seiten, Format (B × H): 148 mm x 210 mm, Gewicht: 2105 g

In many industrial applications early detection and diagnosis of abnormal behavior of the plant is of great importance. During the last decades, the complexity of process plants has been drastically increased, which imposes great challenges in development of model-based monitoring approaches and it sometimes becomes unrealistic for modern large-scale processes.

The main objective of Adel Haghani Abandan Sari is to study e?cient fault diagnosis techniques for complex industrial systems using process historical data and considering the nonlinear behavior of the process. To this end, di?erent methods are presented to solve the fault diagnosis problem based on the overall behavior of the process and its dynamics. Moreover, a novel technique is proposed for fault isolation and determination of the root-cause of the faults in the system, based on the fault impacts on the process measurements.

Zielgruppe

Research

Autoren/Hrsg.

Haghani Abandan Sari, Adel

Fachgebiete

• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik

Inhaltsverzeichnis

Introduction.- An overview of fault diagnosis techniques.- Fault detection in multimode nonlinear systems.- Fault detection in multimode nonlinear dynamic systems.- Fault diagnosis in multimode nonlinear processes.- Bayesian approach for fault treatment.- Application and benchmark study.- Summary.

Über Autor(innen)

Adel Haghani Abandan Sari is research assistant with Institute of Automation, university of Rostock. His research interests include data-driven process monitoring and fault-tolerant control with focus on large-scale industrial processes.