Fault diagnosis of differential-algebraic systems

A large class of engineering systems are modeled by coupled differential and algebraic equations (DAE). Due to the singular nature of the algebraic equations, DAE systems do not satisfy the standard state-space description and require special techniques. So far, the literature has concentrated mostly on the numerical analysis and control of DAE systems. This paper investigates the problem of health monitoring and robust fault diagnosis of DAE systems. The main contributions are the design and analysis of a numerically feasible learning scheme for robust and stable fault diagnosis of DAE systems. The proposed fault diagnosis architecture monitors the physical system for any off-nominal behavior using nonlinear modeling techniques and learning algorithms. Online approximators, in the form of neural networks, are utilized in the detection of faults and in the derivation of models for the fault function, which can be used for fault isolation, fault identification, and fault accommodation. The stability and robustness properties of the fault diagnosis scheme are investigated. A simulation example illustrating the ability of the proposed fault diagnosis architecture to detect faults in a chemical reactive flash is presented.