Brief description: 

An easy way to export/ extract the sensor data (velocity, current, temperature)
preprocess the data and do feature engineering and analyze the data train unsupervised anomaly detection models using Spark MLlib and scikit learn deploy the models in a Big Data Stack using Apache Spark 2.3 create intuitive visualizations that will help and alert VW engineers

 

The software predicts mechanical/ electrical/ software failure do predictive maintenance(preventative vs corrective maintenance) handle the collisions or crashes do a robot diagnosis We work closely with VW engineers and discover their needs to create a product that will bring VW value (safety, cost savings, uptime)

Main Features: 

Even Robots need caring 

€ 22,000 per minute 

Unplanned Downtime Cost 

€ 2milion 

Incident Cost 

Areas of Application: 

Maintenance, Industrial rtobot

Customer Benefits: 
  • Improves overall equipment effectiveness.
  • Improves efficiency, safety and asset performance
  • Reduces true downtime cost by 30%
Technological novelty: 

First on-premise, end-to-end solution to plan, manage and optimize the factory floor assets

Workflow: 
Published
Component / Service / App

Owner

Razvan Pistolea
Romania
Type: 
Other
Contact: 
BDV Reference categories: 
Data Management
Markets: 
Manufacturing
Readiness Level: 
Keywords: 
Maintenance
Industrial robot