Data-Driven Design of Fault Diagnosis Systems

1. Front Matter

   Pages I-XIX

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2. Introduction

   • Adel Haghani Abandan Sari

   Pages 1-10

3. An overview of fault diagnosis techniques

   • Adel Haghani Abandan Sari

   Pages 11-30

4. Fault detection in multimode nonlinear systems

   • Adel Haghani Abandan Sari

   Pages 31-46

5. Fault detection in multimode nonlinear dynamic systems

   • Adel Haghani Abandan Sari

   Pages 47-63

6. Fault diagnosis in multimode nonlinear processes

   • Adel Haghani Abandan Sari

   Pages 65-73

7. Bayesian approach for fault treatment

   • Adel Haghani Abandan Sari

   Pages 75-86

8. Application and benchmark study

   • Adel Haghani Abandan Sari

   Pages 87-108

9. Back Matter

   Pages 109-136

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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.

• Data-driven approaches
• Decision making
• Fault detection and diagnosis
• Process monitoring

• Inst. f. Informatik und Elektrotechnik, Universität Rostock, Rostock, Germany

  Adel Haghani Abandan Sari

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.

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