Enhancing Fault Detection in Time Sensitive Networks using Machine Learning

Time sensitive networking (TSN) is gaining attention in industrial automation networks since it brings essential real-time capabilities to the Ethernet layer. Safety-critical real-time applications based on TSN require both timeliness as well as fault-tolerance guarantees. The TSN standard 802. 1CB introduces seamless redundancy mechanisms for time-sensitive data whereby each data frame is sequenced and duplicated across a redundant link to prevent single points of failure (most commonly, link failures). However, a major shortcoming of 802. 1CB is the lack of fault detection mechanisms which can result in unnecessary replications even under good link conditions - clearly inefficient in terms of bandwidth use. This paper proposes a machine learning-based intelligent configuration synthesis mechanism that enhances bandwidth utilization by replicating frames only when a link has a higher propensity for failure.