Machine Learning For Predictive Analytics

Project leader: ,
Start date: 1. October 2018
End date: 30. September 2021
Funding source: Industrie


The main goal of this project is to improve the overall system quality and customer satisfaction.

In this project, we analyze IoT data (machine logs and sensory data) sent by thousands of high-end medical devices every day. The extracted information can include physical parameters and additional extracted event patterns. This data can be used to predict the failure of specific components and correlate malfunction to machine usage. As a consequence, system stability can be improved and procedures for system testing can be recommended.

Furthermore, information from customer service data (e.g. tickets) is processed and fused with machine data to predict customer sentiment. With that, customer satisfaction can be improved via proactive service.

Methods designed and used include:

  • Time Series Analysis (esp. mixed-typed and irregularly sampled)
  • Deep Learning
  • Process Mining
  • Data Fusion
  • Text Mining