This research team focuses on developing physics- and AI based methods to monitor the health status of subcomponents in mechatronic systems and energy systems, and to estimate their remaining useful lifetime under limited historical failure data.
For years, the team has built scaled testbeds and fault simulators, including inverter-fed electrical machines, scaled pitch/yaw system, planetary gearboxes, accelerated lifetime bearings, etc. to validate the developed methods. Towards intelligent electric vehicles and smart grids, the team aims to develop control and monitoring concepts to enhance security, reliability, and performance of cyber and physical components under faults/ cyber-attacks.
The in-house controllers, modularized power supplies, loads and real-time FPGA-based simulator OPAL-RT would allow for fast verifications of the proposed concepts. The team has led/participated national and international research projects and won IEEE outstanding journal/best paper awards.
PhD programme in Engineering and Science
Selected publications
Attestog, Sveinung; Senanayaka, Jagath Sri Lal; Huynh, Van Khang & Robbersmyr, Kjell Gunnar (2022). Robust Active Learning Multiple Fault Diagnosis of PMSM Drives with Sensorless Control under Dynamic Operations and Imbalanced Datasets. IEEE Transactions on Industrial Informatics. ISSN 1551-3203. 19(9)
Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2020). Towards Self-Supervised Feature Learning for Online Diagnosis of Multiple Faults in Electric Powertrains. IEEE Transactions on Industrial Informatics. ISSN 1551-3203.
Klausen, Andreas; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Multi-band identification for enhancing bearing fault detection in variable speed conditions. Mechanical systems and signal processing. ISSN 0888-3270. 139.
Kandukuri, Surya Teja; Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). A Two-Stage Fault Detection and Classification Scheme for Electrical Pitch Drives in Offshore Wind Farms Using Support Vector Machine. IEEE transactions on industry applications. ISSN 0093-9994. 55(5), s. 5109–5118.
Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Multiple Classifiers and Data Fusion for Robust Diagnosis of Gearbox Mixed Faults. IEEE Transactions on Industrial Informatics. ISSN 1551-3203. 15(8), s. 4569–4579.
Previous projects/doctoral dissertations
Attestog, Sveinung, Modelling and Detecting Faults of Permanent Magnet Synchronous Motors in Dynamic Operations, 2022.
Senanayaka, Jagath Sri Lal, Online Condition Monitoring of Electric Powertrains using Machine Learning and Data Fusion, 2020
Hemmer, Martin, Condition Monitoring Methods for Large, Low-speed Bearings, 2020
Klausen, Andreas, Condition monitoring of rolling element bearings during low and variable speed conditions
Kandukuri, Surya Teja, Health Monitoring of Pitch and Yaw Systems in Offshore Wind Farms, 2018, Dissertation. UIA
Current projects/fundings:
Performance and Health Monitoring of Hydro electric power plants
Funded by Research council of Norway
Analytics for Asset Integrity Management of Windfarms
Funded by Research council of Norway
Enhancing Capacity in Condition-based Maintenance of Wind Energy - ECO-WIND Project 101177220 - Erasmus+ European Commission), Khang - coordinator, 2025 – 2028.
Awards and Prizes
- Outstanding Journal Paper Award - IEEE Transactions on Industrial Informatics, 2020
- Best Paper Awards at IEEE International Conference on Electrical Machines and Systems, in Australia, Korea, and China in 2017, 2018, and 2019