Optical FBG-T Based Fault Detection Technique for EV Induction Machines

Wenping Cao, Belema P. Alalibo, Bing Ji, Xiangping Chen, Cungang Hu

Research output: Contribution to journalConference articlepeer-review


Abstract: Electric vehicles (EV) represent a key technology to achieve a low-carbon transportation objective, whist induction motors are one of the promising topologies. The reliability of these machines is crucial to minimize the downtime, cost and unwanted human lives. Although several techniques are utilized in the condition monitoring and fault detection of electrical machines, there is still no single technique that provides an all-round solution to fault detection in these machines and thus hybrid techniques are used widely. This paper presents a novel non-invasive optical fiber technique in condition monitoring of induction machines and in the process detecting inter-turn short circuit faults. Owing to optical fiber’s immunity to magnetic flux, a composite FBG-T sensor formed by bonding a giant magnetostrictive transducer, Terfenol-D, onto a fiber Bragg grating is utilized to sense machines’ stray flux as a signature to determine the internal winding condition of the machines. A tri-axial auto datalogging flux meter was used to obtain the stray magnetic flux and test results obtained via LabView were analyzed in MatLab. Experimental and numerical results agree with each other and how that the FBG-T sensor accurately and reliably detected the short-circuit faults. Bragg shifts observed under short-circuit faults were in 100s of picometre range under various operating frequencies compared to the mid-10s of picometre obtained under healthy machine condition. These provide much promise for future EVs.
Original languageEnglish
Article number012045
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 1 Feb 2022
Event2021 International Conference on Smart Transportation, Energy and Power (STEP 2021) -
Duration: 3 Dec 20215 Dec 2021

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