Qu Hongquan, Feng Tingliang, Zhang Yuan, Wang Yanping
School of Information Science and Technology, North China University of Technology, Beijing 100144, China.
Sensors (Basel). 2019 Jul 26;19(15):3293. doi: 10.3390/s19153293.
Optical fiber pre-warning systems (OFPS) based on Φ-OTDR are applied to many different scenarios such as oil and gas pipeline protection. The recognition of fiber vibration signals is one of the most important parts of this system. According to the characteristics of small sample set, we choose stochastic configuration network (SCN) for recognition. However, due to the interference of environmental and mechanical noise, the recognition effect of vibration signals will be affected. In order to study the effect of noise on signal recognition performance, we recognize noisy optical fiber vibration signals, which superimposed analog white Gaussian noise, white uniform noise, Rayleigh distributed noise, and exponentially distributed noise. Meanwhile, bootstrap sampling (bagging) and AdaBoost ensemble learning methods are combined with original SCN, and Bootstrap-SCN, AdaBoost-SCN, and AdaBoost-Bootstrap-SCN are proposed and compared for noisy signals recognition. Results show that: (1) the recognition rates of two classifiers combined with AdaBoost are higher than the other two methods over the entire noise range; (2) the recognition for noisy signals of AdaBoost-Bootstrap-SCN is better than other methods in recognition of noisy signals.
基于Φ-OTDR的光纤预警系统（OFPS）被应用于许多不同场景，如油气管道保护。光纤振动信号的识别是该系统最重要的部分之一。根据小样本集的特点，我们选择随机配置网络（SCN）进行识别。然而，由于环境和机械噪声的干扰，振动信号的识别效果会受到影响。为了研究噪声对信号识别性能的影响，我们对叠加了模拟白高斯噪声、白均匀噪声、瑞利分布噪声和指数分布噪声的有噪声光纤振动信号进行识别。同时，将自助采样（bagging）和AdaBoost集成学习方法与原始的SCN相结合，提出了Bootstrap-SCN、AdaBoost-SCN和AdaBoost-Bootstrap-SCN，并对有噪声信号识别进行比较。结果表明：（1）在整个噪声范围内，结合AdaBoost的两种分类器的识别率高于其他两种方法；（2）AdaBoost-Bootstrap-SCN在有噪声信号识别方面优于其他方法。
