Anomaly Detection for Industrial Big Data

As the Industrial Internet of Things (IIoTa) grows, systems are increasingly being monitored by arrays of sensors returning time-series data at ever-increasing ‘volume, velocity and variety’b (i.e. Industrial Big Datac). An obvious use for these data is real-time systems condition monitoring and prognostic time to failure analysis (remaining useful life, RUL). (e.g. See white papers by Senseye.io, Prognostics The Future of Condition Monitoringd, and output of the NASA Prognostics Center of Excellence (PCoEe).) However, as noted by Agrawal and Choudharyf ‘Our ability to collect “big data” has greatly surpassed our capability to analyze it, underscoring the emergence of the fourth paradigm of science, which is data-driven discovery.’ In order to fully utilize the potential of Industrial Big Data we need data-driven techniques that operate at scales that process models cannot. Here we present a prototype technique for data-driven anomaly detection to operate at industrial scale. The method generalizes to application with almost any multivariate dataset based on independent ordinations of repeated (bootstrapped) partitions of the dataset and inspection of the joint distribution of ordinal distances. ahttp://www3.weforum.org/docs/WEFUSA_IndustrialInternet_Report2015.pdf bhttps://aip.scitation.org/doi/10.1063/1.4907823 chttp://www.vix.com.pl/wp-content/uploads/the_rise_of_industrial_big_data.pdf dhttps://www.senseye.io/white-papers-senseye-scalable-predictive-maintenance/ ehttps://ti.arc.nasa.gov/tech/dash/groups/pcoe/ fhttps://aip.scitation.org/doi/full/10.1063/1.4946894