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.

• Condition-Based Maintenance for Marine Vessels (Mespas AG)
• Decision support system for predictive maintenance of laser cutting machines (Bystronic Laser AG)
• Machine Learning Based Fault Detection for wind Turbines (Nispera AG)

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