Reliability Research of Fiber-optic Hydrophone Acoustic Sensitive Component Based on Fiber Distributed Strain Testing

Acoustic sensitive component is the pivotal sensitivity core and the major weak links of fiber-optic hydrophone. In actual service environments, the fiber-optic hydrophone acoustic sensitive component needs to detect weak acoustic signals under hydrostatic pressure of tens of megapascals. Any reliability problems of the acoustic sensitive component may lead to the failure of the fiber-optic hydrophone. Thus, reliability is a critical technical specification for the fiber-optic hydrophone acoustic sensitive component. In this paper, we address the limitations of traditional overall optical path loss detection methods, which are insufficient to effectively characterize the performance changes of the fiber-optic hydrophone acoustic sensitive component under hydrostatic pressure service conditions, and propose a novel reliability research method for the fiber-optic hydrophone acoustic sensitive component based on fiber-optic distributed strain testing technology. Firstly, we analyzed the structural characteristics and reliability testing requirements of fiber-optic hydrophone acoustic sensitive component, and proposed a reliability research method based on distributed strain testing. Further, we built an experimental setup as described above and conducted mass of practical test experiments with different fiber-optic hydrophone acoustic sensitive component samples to prove the feasibility and repeatability of the new method. Finally, we used this method analyzed the reliability of acoustic sensitive component under hydrostatic pressure conditions, clarifying the location and mechanism of failure, and providing clear guidance for subsequent reliability improvement. The analysis of the experimental results shows that the nondestructive, in-situ testing method present in this paper can effectively characterize the internal strain variations of the acoustic sensitive component under hydrostatic pressure loads, thereby providing intuitive data support for reliability analysis.