Distributed real-time operational data analytics

Host: Technical University of Denmark (DTU), Embedded Systems Engineering (ESE) section.

  • Main supervisor: Prof. Lars Kai Hansen, lkh@imm.dtu.dk (contact person)
  • Co-supervisor: Prof. Jan Larsen janla@dtu.dk

Requirements:

Objectives:

  1. Propose data analytics for Industry 4.0 that exploit the connectivity and data access features of the Fog Nodes.
  2. Implement a distributed real-time data analytics solution based on Machine-Learning-as-a-Service.
  3. Demonstrate the data analytics solution on an industrial use case.

Expected Results:

  • Distributed data representation and modeling, using the services provided by the Fog Nodes, which are close to the machines (robots, actuators, sensors).
  • Distributed real-time machine learning algorithms that use the Fog Computing Platform.
  • Implementation of the ML algorithms using ML-as-a-Service microservices.
  • Evaluation of the approaches, and quantification of value gains obtained from the improved operations.

Planned visits and collaboration:

  • VCE (Dr. Peter Wallin): Demonstrate the data analytics apps on the VCE use case.

Description:

Please contact the main supervisor for a detailed description.

Relevant publications:

Please contact the main supervisor for relevant publications about the topic area.