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In this paper, we develop a hybrid approach for fault diagnosis in hybrid systems assuming that multiple faults may occur in both continuous dynamics and at the discrete level. In this approach, a diagnoser is designed for a discrete-event-system (DES) abstraction of the system. The DES abstraction is modified such that the actual condition of the system always remains a subset of the condition estimate provided by the DES diagnoser. Whenever the DES diagnoser fails to provide a certain condition for the system (or it is deemed to be necessary), a continuous diagnoser consisting of a bank of residual generators becomes active. Since the continuous dynamics is not used at all times for diagnosis, our approach reduces the online computing requirements of the diagnosis system. We also model a typical spacecraft propulsion system as a hybrid automaton and explain how our results can be applied to it.

This paper presents a novel methodology for fault detection in gas turbine engines based on the concept of dynamic neural networks. The neural network structure belongs to the class of locally recurrent globally feed-forward networks. The architecture of the network is similar to the feed-forward multi-layer perceptron with the difference that the processing units include dynamic characteristics. The dynamics present in these networks make them a powerful tool useful for identification of nonlinear systems. The dynamic neural network architecture that is described in this paper is used for fault detection in a dual-spool turbo fan engine. A number of simulation studies are conducted to demonstrate and verify the advantages of our proposed neural network diagnosis methodology.

This paper presents a hybrid framework for fault diagnosis of complex systems that are modeled by hybrid automata. A bank of residual generators is constructed based on the continuous models of the system. Each residual generator is modeled by a discrete-event system (DES). Next, the DES models of the residual generators and the DES model of the hybrid plant are combined to build an “extended DES” model. A hybrid diagnoser is constructed based on the extended DES model. The hybrid diagnoser effectively combines the readings of discrete sensors and the information supplied by the residual generators (which is based on continuous sensors) to determine the health status of the hybrid plant. The hybrid diagnosis approach is employed to investigate faults in the fuel supply system and the nozzle actuator of a single-spool turbojet engine with an afterburner.

In this paper, we investigate fault diagnosis and diagnosability in hybrid systems modeled by hybrid automata. Generally, in hybrid systems, there are discrete sensors generating discrete outputs available at the discrete-event system representation of the system and continuous sensors generating continuous outputs available at the continuous dynamics. We assume that there is a bank of residual generators (using continuous sensors) designed for the continuous dynamics of the system. We present a hybrid diagnosis approach in which faults are diagnosed by integrating the information generated by the residual generators and the information at the discrete-event system representation of the system. We investigate the diagnosability of faults in the hybrid diagnosis framework.Index Terms-Fault diagnosis, hybrid systems, finite-state automata, diagnosability.