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Smart Maintenance

Our Smart Maintenance solutions use data analytics and machine learning tools on machine data in order to predict and prevent potential machine failures and optimize maintenance scheduling.


Unexpected  machine failures can lead to costly and even catastrophic consequences. In order to avoid them, the use of systems that analyze the machine condition and behavior is necessary. Such condition monitoring systems can then provide health indicators and alarms for faulty behavior and even predict a future failure or the remaining useful life of the equipment. Analyzing machine condition is nowadays possible due to the fast evolution of advanced sensors, data collection and storage systems and intelligent data analytical tools.

At the smart maintenance team of IDP we work together with our partners from various industry and public sectors in order to develop data analytical tools for fault detection, diagnosis and prognostics of machine condition, which are appropriate for their specific application. We then use these tools for the optimization of maintenance decision making. Our smart maintenance algorithms utilize methods ranging from statistical analysis through machine learning and artificial intelligence algorithms in combination with physics-based models.

Selected Projects


  • MSE Module Lifecycle-Management von Infrastrukturen
  • Bachelor Module Instandhaltung (Verkehrsysteme)
  • Bachelor Module RAMS (Verkehrsysteme)
  • CAS Instandhaltungsmanagement
  • CAS Industrie 4.0

Selected industry partners