On the Quality of Service of Failure Detectors
IEEE Transactions on Computers
Neural networks-based scheme for system failure detection and diagnosis
Mathematics and Computers in Simulation
Learning to Forget: Continual Prediction with LSTM
Neural Computation
A new adaptive accrual failure detector for dependable distributed systems
Proceedings of the 2007 ACM symposium on Applied computing
Towards Model-Based Failure-Management for Automotive Software
SEAS '07 Proceedings of the 4th International Workshop on Software Engineering for Automotive Systems
Finite state automata and simple recurrent networks
Neural Computation
Formal verification of diagnosability via symbolic model checking
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A model-based approach to reactive self-configuring systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Neural-network-based robust fault diagnosis in robotic systems
IEEE Transactions on Neural Networks
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In reliable systems fault detection is essential for ensuring the correct behavior. Todays automotive electronical systems consists of 30 to 80 electronic control units which provide up to 2.500 atomic functions. Because of the growing dependencies between the different functionality, very complex interactions between the software functions are often taking place. Within this paper the diagnosability of the behavior of distributed embedded software systems are addressed. In contrast to conventional fault detection the main target is to set up a self learning mechanism based on artificial neural networks (ANN). For reaching this goal, three basic characteristics have been identified which shall describe the observed network traffic within defined constraints. With a new extension to the reber grammar the possibility to cover the challenges on diagnosability with ANN can be shown.