Data Acquisition And Processing Of Distributed Optical Fiber Vibration Sensor System

With the rapid development of optical fiber and optical fiber communication technology, optical fiber sensor technology, as a new technology, has made tremendous development. Through nearly 30 years' development, it has made great achievements. Based on the advantages such as non-touch measurement, high sensitivity, fast response, anti-electromagnetic interference high temperature resistance and corrosion resistance, the optical fiber sensor is widely used in the security alarm of sensitive areas. In this paper, the data acquisition and processing of distributed optical fiber vibration sensor system is analysed and discussed both in theory and experiments. The results of this paper have important theoretical significance and practical value in the commercialization of distributed interferometric fiber-optic sensor. And they also have a potential application in the field of industrial and national defence.This paper studies the optical fiber sensor system which is based on the Mach-Zehnder interferometer. The system primarily consists of the management center, the monitor node and the sengsing equipment. The monitor node primarily consists of the interface module, the data acquisition module, the data processing module and the database system module. This paper focuses on the studies of the design and implementation of the data acquisition module based on the PCI bus and the data processing algorithms. A new signal acquisition mode is adopted in the distributed optical fiber vibration sensor system. The drivers of the acquisition cards produced by different companies are packaged through the hierarchical design. As a result, the compatibility of the upper software is ensured when the acquisition cards produced by different companies are used in the system. The signal processing algorithms consists of the alarm algorithms and the positioning algorithms. In the alarm algorithms, the template database based on the characteristic values of the effective signals is adopted. The accuracy of the system alarm is improved through pattern comparison between the invasion and the templates. And this also facilitates the users to distinguish between the alarm event and the false event on their own needs. In the positioning algorithms, the PC processing mode, instead of the DSP mode, is adopted. The tder algorithm, in which ML window function is selected as weight function, is chosen to suppress the noise impact and increase the accuracy of time delay estimation.