Abstract—Techniques for diagnosing faults in hybrid systems that combine digital (discrete) supervisory controllers with analog (continuous) plants need to be different from those used for discrete or continuous systems. This paper presents a methodology for online tracking and diagnosis of hybrid systems. We demonstrate the effectiveness of the approach with experiments conducted on the fuel-transfer system of fighter aircraft. Index Terms—Fault detection and isolation, hybrid systems, model-based diagnosis (MBD). I.

The AI model-based diagnosis community has developed qualitative reasoning mechanisms for fault isolation in dynamic systems. Their emphasis has been on the fault isolation algorithms, and little attention has been paid to robust online detection and symbol generation that are essential components of a complete di-agnostic solution. This paper discusses a robust diagnosis methodology for hy-brid systems that combines fault detection with a combined qualitative and quan-titative fault isolation scheme. We focus on fault detection, symbol generation, and parameter estimation, and illustrate the effectiveness of this method by run-ning experiments on the fuel transfer system of aircraft. ©: 2003 IFAC

Safety of planned paths of autonomous cars with respect to the movement of other traffic participants is considered. Thereto, the stochastic occupancy of the road by other vehicles is predicted. The prediction considers uncertainties originating from the measurements and the possible behaviors of other traffic participants. In addition, the interaction of traffic participants as well as the limitation of driving maneuvers due to the road geometry is considered. The result of the presented approach is the probability of a crash for a specific trajectory of the autonomous car. The presented approach is efficient as most intensive computations are performed offline, resulting in a lean online algorithm for real-time application.

Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. Many

Abstract—Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. Although discrete-event diagnosis methods are used extensively, they do not easily address parametric fault isolation in systems with complex continuous dynamics. This paper presents a novel event-based approach for diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. From a continuous model of the system, we systematically derive dynamic fault signatures expressed as event-based fault models, which are used, in turn, for designing an event-based diagnoser of the system and determining system diagnosability. The proposed approach is applied to a subset of the Advanced Diagnostics and Prognostics Testbed, which is representative of a spacecraft’s electrical power system. We present experimental results from the actual testbed, as well as detailed simulation experiments that examine the performance of our diagnosis algorithms under different fault magnitudes and noise levels. Index Terms—Discrete-event system (DES), electrical power systems, model-based diagnosis.

Abstract—This paper aims to present a method of creating architectures which allow monitoring occurrence of failure in Service oriented Architectures (SoA). The presented approach extends Discrete Event Systems techniques to produce a method of automated creation of Diagnoser Service which monitors interaction between the services to identify if a failure has happened and the type of failure. To do so, a formal representation of business processes is introduced, which allows modeling of observable/unobservable events, failure and the type of failure. The paper puts forward a set of algorithms for creating models of Diagnoser Service. Such models are then transformed into new Services implemented in BPEL, which interact with the existing services to identify if a failure has happened and the type of failure. The approach has been applied to an example of diagnosis of Right-first-time failure in Services used in telecommunications. I.