Haghani Abandan Sari, Adel.
Data-Driven Design of Fault Diagnosis Systems Nonlinear Multimode Processes / [electronic resource] : by Adel Haghani Abandan Sari. - XIX, 136 p. 39 illus. online resource.
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.
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 efficient fault diagnosis techniques for complex industrial systems using process historical data and considering the nonlinear behavior of the process. To this end, different 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. Contents Process monitoring Fault diagnosis and fault-tolerant control Data-driven approaches and decision making Target Groups Graduate students and scientists of automatic control and process engineering Engineers in field of process control and monitoring, mechatronic About the Author 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.
9783658058074
10.1007/978-3-658-05807-4 doi
Engineering.
Applied mathematics.
Engineering mathematics.
Control engineering.
Robotics.
Mechatronics.
Industrial engineering.
Production engineering.
Engineering.
Control, Robotics, Mechatronics.
Industrial and Production Engineering.
Appl.Mathematics/Computational Methods of Engineering.
TJ210.2-211.495 TJ163.12
629.8
Data-Driven Design of Fault Diagnosis Systems Nonlinear Multimode Processes / [electronic resource] : by Adel Haghani Abandan Sari. - XIX, 136 p. 39 illus. online resource.
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.
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 efficient fault diagnosis techniques for complex industrial systems using process historical data and considering the nonlinear behavior of the process. To this end, different 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. Contents Process monitoring Fault diagnosis and fault-tolerant control Data-driven approaches and decision making Target Groups Graduate students and scientists of automatic control and process engineering Engineers in field of process control and monitoring, mechatronic About the Author 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.
9783658058074
10.1007/978-3-658-05807-4 doi
Engineering.
Applied mathematics.
Engineering mathematics.
Control engineering.
Robotics.
Mechatronics.
Industrial engineering.
Production engineering.
Engineering.
Control, Robotics, Mechatronics.
Industrial and Production Engineering.
Appl.Mathematics/Computational Methods of Engineering.
TJ210.2-211.495 TJ163.12
629.8