基于相位敏感型光时域反射仪的袋式除尘器漏袋检测技术

采用相位敏感型光时域反射仪的分布式光纤传感系统对除尘器内滤袋进行实时监测.通过对光纤在除尘器滤袋内敷设方式的设计, 实现了对除尘器内滤袋的定位.对6种类型的破袋内光纤振动信号进行采集, 且当这些滤袋没有破损时, 对其光纤振动信号也进行采集.采用小波包分解法计算了滤袋内光纤振动信号的信息熵和相关系数, 并将两参数合并构成二维特征参量.分析了在不同的二维特征参量下, 好袋内光纤振动信号和破袋内光纤振动信号之间的特征差别.以类型3滤袋信号特征样本作为训练样本对反向传播神经网络进行训练, 然后对6种类型的滤袋信号特征样本进行识别, 结果显示该方法对6种类型的滤袋具有较高的识别稳定性, 且平均滤袋识别率分别达到96.2%、88.7%、98.4%、98.5%、98.5%、98.5%.

Abstract

A distributed optical fiber sensing system based on phase sensitive optical time domain reflectometer was used to monitor the filter bag in the dust collector in real time. The positioning of the filter bag in the dust collector is realized by designing the laying mode of the optical fiber in the dust filter bag. The optical fiber vibration signals in six types of damaged bags are collected, and the optical fiber vibration signals in these bags are also collected when they are not damaged. The information entropy and correlation coefficient of the optical fiber vibration signal in the filter bag are calculated by wavelet packet decomposition method and the two parameters are combined to form a two-dimensional characteristic parameter. The characteristic difference between the optical fiber vibration signal in the non-damaged bag and the optical fiber vibration signal in the damaged bag under different two-dimensional characteristic parameters is analyzed. The back propagation neural network is trained with the type 3 filter bag signal feature samples as the training samples, and then the six types of filter bag signal feature samples are identified. The results show that the method has higher recognition stability for the six types of filter bags. And the average recognition rate can reach 96.2%, 88.7%, 98.4%, 98.5%, 98.5%, 98.5%, respectively.

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刘旭安, 李俊, 史博, 丁国绅, 汤玉泉, 董凤忠, 张志荣. 基于相位敏感型光时域反射仪的袋式除尘器漏袋检测技术[J]. 光子学报, 2019, 48(8): 0806002. LIU Xu-an, LI Jun, SHI Bo, DING Guo-shen, TANG Yu-quan, DONG Feng-zhong, ZHANG Zhi-rong. Leakage Bag Detection Technology of Bag Filter Based on Phase-sensitive Optical Time Domain Reflectometry[J]. ACTA PHOTONICA SINICA, 2019, 48(8): 0806002.

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