Data compression in heterodyne phase-sensitive OTDR using quantization technology

Yu，Fei Hong1; Liu，Shuaiqi1,2; Xu，Wei Jie1; Shao，Liyang1

10.1117/12.2625115

2022

0277-786X

1996-756X

Proceedings of SPIE - The International Society for Optical Engineering

12169

The combination of phase-sensitive optical time-domain reflectometry (F-OTDR) and deep learning algorithms is a popular research topic in recent years. To train the classification models established by these algorithms, a huge amount of data is required. Though F-OTDR can monitor the applied perturbations along the sensing fiber continuously, the huge volume of data puts a heavy burden on storage devices. In this paper, we propose a lossy data compression method based on quantization technology to solve the data storage problem in heterodyne F-OTDR. Experimental results show that the vibration waveform can be successfully restored from the reconstructed signal. A compression ratio of 16 is achieved by quantifying a 128-MB data to 31.25 MB with 293.2-ms time consumption. With the proposed quantization method, it is expected to be able to store more sensing data without making modifications to the hardware.

data compression Optical fiber sensing phase-sensitive optical time-domain reflectometry quantization

第一 ; 通讯

英语

EI

Southern University of Science and Technology[LZC0019];Southern University of Science and Technology[LZC0020];

20221611967739

Data compression ; Deep learning ; Heterodyning ; Optical fibers ; Reflection ; Time domain analysis ; Virtual storage

Ergonomics and Human Factors Engineering:461.4 ; Data Storage, Equipment and Techniques:722.1 ; Fiber Optics:741.1.2 ; Mathematics:921 ; Optical Instruments:941.3

2-s2.0-85128099300

Scopus

1.Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.State Key Laboratory of Analog and Mixed-Signal VLSI,University of Macau,999078,Macao

Yu，Fei Hong,Liu，Shuaiqi,Xu，Wei Jie,et al. Data compression in heterodyne phase-sensitive OTDR using quantization technology[C],2022.