Daniel Strömbergsson

Om mig

Between 2015 and 2020, I pursued a PhD at the Division of Machine Elements in condition monitoring of wind turbines, with a particular focus on bearing faults in drivetrain components. My dissertation addressed several critical aspects of vibration-based monitoring of primarily gearbox bearings, including:

• Sensor placement – are we measuring at the right locations within the drivetrain subsystem?
• Measurement properties – are we storing the right type of signals to enable reliable post-event investigation and root cause analysis when component failures occur?
• Signal processing – are there alternative techniques that could improve the detectability of emerging bearing defects?

Since 2022, I have been working as a postdoctoral researcher in the Machine Learning Group, collaborating with Fredrik Sandin. Our research focuses on integrating neuromorphic technology into next-generation sensor systems. The goal is to develop energy-efficient, adaptive solutions for condition monitoring, with continued applications in rotating machinery and industrial environments.