Failure Detection in TSN Startup Using Deep Learning

Time triggered devices are increasingly deployed in safety-critical distributed applications. Failures that manifest during the startup process of the synchronisation service pose a challenge to diagnose. The difficulty stems from the fact that most implemented diagnostic services for time-triggered systems employ the prior knowledge of the schedule to provide diagnosis. However, at the beginning of the startup process, when the global time base is not yet established, these diagnostic services are not viable. This work proposes the use of a fault injection framework to generate data that resembles the behaviour of failed components during startup. The data generated can then be used for developing fault diagnostic mechanisms. Due to the large data set that can be provided by fault injection frameworks, deep learning is proposed as a strategy for failure identification. The data generated from a fault injection framework is used to train the neural network to distinguish between correct behaviour, corruption and omission failures during startup.