I flere år har teamet bygget skalerte testbenker og feilsimulatorer, inkludert inverterdrevne elektriske maskiner, skalert pitch-/yaw-system, planetgir, akselererte levetidslager osv. for å validere de utviklede metodene. Med sikte på intelligente elektriske kjøretøy og smarte strømnett, har teamet som mål å utvikle kontroll- og overvåkingskonsepter for å forbedre sikkerheten, påliteligheten og ytelsen til cyber- og fysiske komponenter under feil og cyberangrep.
De egenutviklede kontrollerne, modulariserte strømforsyningene, lastene og den sanntids FPGA-baserte simulatoren OPAL-RT gir mulighet for rask verifisering av de foreslåtte konseptene. Teamet har ledet/deltatt i nasjonale og internasjonale forskningsprosjekter og vunnet IEEE-utmerkelser for fremragende tidsskriftartikler og beste artikkel.
Ph.d.-program i teknologi og realfag
Utvalgte publiseringer
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.
Tidligere prosjekter og ph.d.-grader
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
Nåværende prosjekter og tildelinger:
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.
Utmerkelser
- 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