With the introduction of Cyber-Physical Systems (CPS) and Internet of Things (IoT) technologies, the automation industry is undergoing significant changes, particularly in improving production efficiency and reducing maintenance costs. Industrial automation applications often need to transmit time- and safety-critical data to closely monitor and control industrial processes. Several Ethernet-based fieldbus solutions, such as PROFINET IRT, EtherNet/IP, and EtherCAT, are widely used to ensure real-time communications in industrial automation systems. These solutions, however, commonly incorporate additional mechanisms to provide latency guarantees, making their interoperability a grand challenge. The IEEE 802.1 Time-Sensitive Networking (TSN) task group was formed to enhance and optimize IEEE 802.1 network standards, particularly for Ethernet-based networks. These solutions can be evolved and adapted for cross-industry scenarios, such as large-scale distributed industrial plants requiring multiple industrial entities to work collaboratively. This paper provides a comprehensive review of current advances in TSN standards for industrial automation. It presents the state-of-the-art IEEE TSN standards and discusses the opportunities and challenges of integrating TSN into the automation industry. Some promising research directions are also highlighted for applying TSN technologies to industrial automation applications.

In chemical industries, process operations are usually comprised of several discrete operating regions with distributions that drift over time. These complexities complicate outlier detection in the presence of intrinsic process dynamics. In this article, we consider the problem of detecting univariate outliers in dynamic systems with multiple operating points. A novel method combining the time series Kalman filter (TSKF) with the pruned exact linear time (PELT) approach to detect outliers is proposed. The proposed method outperformed benchmark methods in outlier removal performance using simulated data sets of dynamic systems with mean shifts. The method was also able to maintain the integrity of the original data set after performing outlier removal. In addition, the methodology was tested on industrial flaring data to pre-process the flare data for discriminant analysis. The industrial test case shows that performing outlier removal dramatically improves flare monitoring results through Partial Least Squares Discriminant Analysis (PLS-DA), which further confirms the importance of data cleaning in process data analytics.