SLIDES: State of the Art Robot Predictive Maintenance with Real-time Sensor Data

File uploaded by slimbaltagi on Sep 30, 2017
Version 1Show Document
  • View in full screen mode

Slides of a talk by Mathieu Dumoulin from MapR and Mateusz Dymczyk from H2O at the 2017 Strata Data Conference in NYC. 

"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:

  • How to integrate data from a second sensor type
  • Why the overall system predictions are better than models made from either data source taken separately
  • How easy it is to switch to a state-of-the-art LSTM anomaly detection model
  • A comparison with the baseline model"

Outcomes