State-of-the-art robot predictive maintenance with real-time sensor data

Mateusz Dymczyk and Mathieu Dumoulin showcase a working, practical, predictive maintenance pipeline in action and explain how they built a state-of-the-art anomaly detection system using big data frameworks like Spark, H2O, TensorFlow, and Kafka on the MapR Converged Data Platform.

Industry 4.0 IoT applications promise vast gains in productivity from reduced downtime, higher product quality, and higher efficiency. Modern industrial robots integrate hundreds of sensors of all kinds, generating tremendous volumes of data rich in valuable information. However, the reality is that some of the most advanced industrial makers in the world are barely getting started making use of this data, with relatively rudimentary bespoke monitoring systems built at tremendous cost. It is now possible to successfully deploy Industry 4.0 pilot use cases—using a well-chosen selection of big data enterprise products and open source projects— in a matter of months and at a small fraction of the cost of equivalent projects at leading high-tech makers. Mateusz Dymczyk and Mathieu Dumoulin showcase a working, practical, predictive maintenance pipeline in action and explain how they built a state-of-the-art anomaly detection system using big data frameworks like Spark, H2O, TensorFlow, and Kafka on the MapR Converged Data Platform. This is an improved version of the pipeline Mateusz and Mathieu demonstrated at Strata Beijing. This pipeline uses data collected from a Bluetooth wireless movement sensor attached to a realistic model of a standard industrial robot. Topics include: