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Smart Maintenance

We develop AI solutions for technical engineered systems, using data analytics, machine learning and deep learning tools on machine data 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 intelligent tools that analyze the machine condition and behavior is necessary. Such condition monitoring systems can then provide automatic 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 becoming 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 AI-based tools for fault detection, diagnosis and prognostics of machine condition, which are tailored to 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 deep learningalgorithms in combination with physics-based models.


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

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