Automotive
Prediction and prevention of component failures based on sensor data
Starting point
- Real-time analytics are required to effectively utilise the vast amount of machine data from the relevant production machines
- The goal is to make production more efficient and failure-resistant both in the short and long term
- A feasibility study should, in the first step, validate the results and solution models using a prototype
Procedure
- Documentation of the gathered requirements in the form of user stories
- Implementation of the user stories in the agile process model using SCRUM within sprints
- Setting up the infrastructure as an OpenShift environment based on pre-configured images in MS Azure
- Visualisation of the analysis results in Splunk dashboards directly in the production environment
Funktionen/Projektergebnis
- Near-Realtime Bearbeitung aller Sensordaten in der Azure Cloud über Kafka zur splunk>- Visualisierung
- Near-Realtime Dashboards steuern unmittelbar die laufenden Produktions- und Maintenance-Prozesse
- Validierung der Ergebnisse direkt in der Produktion
Customer benefits
- Increase in productivity through real-time analytics
- Prevention of production machine or component failures using predictive analytics
- Improved product quality through targeted interventions in the production process
- Low cost of the analytics solution through the use of MS Azure and OpenShift

Joachim Kirschner | Senior Manager / Authorised Officer