Application of randomized algorithms to the analysis and synthesis of model-based and data-driven fault diagnosis systems

• Applicant: Professor Dr.-Ing. Steven Xianchun Ding
• Subject Area: Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics
• Term: from 2014 to 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 242153335

This research project deals with the analysis and synthesis of observer-based and data-driven fault detection systems (FD-systems). For our purpose, the so-called randomized Algorithms (RA) serves as the mathematical and systematic fundamentals of the study and investigation. The first objective of the project is to sketch a RA-based framework for the analysis and synthesis of observer-based FD-systems. On the basis of this framework, methods and algorithms will be developed for the computation of false alarm rate (FAR) and missed detection rate (MDR). Moreover, development of statistical learning methods for the optimization of observer-based FD-Systems in the context of the FAR and MDR will be addressed. Considering practical situations that the available process data may not cover all faulty operation scenarios, data-driven FD-methods may fail in these situations for a successful fault isolation and identification. It is the second objective of this project to develop methods and algorithms, which are based on the RA and used for generating fault data and further for identifying applicable fault models. These methods will be applied to the multivariate analysis (MVA) methods like PCA (Principle Component Analysis) and PLS (Partial Least Squares) and further to the data-driven observer-based FD-systems. The last objective of the project consists in developing, on the basis of the RA methods, an evaluation platform for the data-driven and model-based FD-systems, which is used for a comparable evaluation of FD-performance like FAR, MDR and detection time. The achieved theoretical results will be tested and demonstrated on the benchmark process Tennessee Eastman and on the laboratory system of heating exchange control. Finally, a software tool in the MATLAB-programming environment will be developed for the RA-based analysis and synthesis of observer-based FD-systems, and the FD-evaluation platform will be established with a web-access.

• DFG Programme: Research Grants