Fault detection in discrete event based distributed systems by forecasting message sequences with neural networks

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.