Fiber Optic Fiber Bragg Grating Sensing for Monitoring and Testing of Electric Machinery: Current State of the Art and Outlook

Suryandi, Asep Andi; Sarma, Nur; Mohammed, Anees; Peesapati, Vidyadhar; Djurović, Siniša

Abstract

This paper presents a review of the recent trends and the current state of the art in the application of fiber optic fiber Bragg gratings (FBG) sensing technology to condition the monitoring (CM) and testing of practical electric machinery and the associated power equipment. FBG technology has received considerable interest in this field in recent years, with research demonstrating that the flexible, multi-physical, and electromagnetic interference (EMI) immune in situ sensing of a multitude of physical measurands of CM interest is possible and cannot be obtained through conventional sensing means. The unique FBG sensing ability has the potential to unlock many of the electric machine CM and design validation restrictions imposed by the limitations of conventional sensing techniques but needs further research to attain wider adoption. This paper first presents the fundamental principles of FBG sensing. This is followed by a description of recent FBG sensing techniques proposed for electric machinery and associated power equipment and a discussion of their individual benefits and limitations. Finally, an outlook for the further application of this technique is presented. The underlying intention is for the review to provide an up-to-date overview of the state of the art in this area and inform future developments in FBG sensing in electric machinery.

Citation

Suryandi, A. A., Sarma, N., Mohammed, A., Peesapati, V., & Djurović, S. (2022). Fiber Optic Fiber Bragg Grating Sensing for Monitoring and Testing of Electric Machinery: Current State of the Art and Outlook. Machines, 10(11), Article 1103. https://doi.org/10.3390/machines10111103

• Journal Article Type: Article
• Acceptance Date: Nov 17, 2022
• Online Publication Date: Nov 21, 2022
• Publication Date: 2022
• Deposit Date: Apr 17, 2023
• Publicly Available Date: Apr 17, 2023
• Journal: Machines
• Electronic ISSN: 2075-1702
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 11
• Article Number: 1103
• DOI: https://doi.org/10.3390/machines10111103
• Public URL: https://durham-repository.worktribe.com/output/1174729