Journal of Sensors

Volume 2017 (2017), Article ID 6020645, 12 pages

https://doi.org/10.1155/2017/6020645

## The Research on Information Representation of Φ-OTDR Distributed Vibration Signals

^{1}Automation College, Beijing University of Posts and Telecommunications, Beijing 100876, China^{2}Automation College, Beijing Institute of Technology, Beijing 100080, China^{3}Institute of Optical Communication Engineering, Nanjing University, Nanjing 210093, China

Correspondence should be addressed to Song Wang

Received 25 June 2017; Revised 7 August 2017; Accepted 13 August 2017; Published 18 September 2017

Academic Editor: Taesun You

Copyright © 2017 Yanzhu Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper mainly focuses on the representable problem of -OTDR distributed vibration signals. The research included a signal extraction part and a signal representation part. Firstly, in order to extract the better -OTDR signal, the time-domain data should be fully preserved. The 2D-TESP method is used to extract data in this paper. There are 29 characters in the traditional TESP method. The characters’ number is reduced from 29 to 13 and the characters’ dimension is expanded from 1 to 2 in the 2D-TESP method. Secondly, in order to represent -OTDR signal better, the characteristics of -OTDR data and damped vibration signals are combined in the paper. The EMD method and the NMF method are combined to form the new method in the paper. Some parameters in the proposed method are optimized and adjusted by GA method. After -OTDR data is represented by the proposed method, there is excellent performance both on the length dimension and on the time dimension. Lastly, some experiments are carried out according to the physical truth in this paper. The experiments are carried out in the semianechoic room. The methods of the paper have better performance. The methods are proved to be effective through these experiments.

#### 1. Introduce

-OTDR (phase sensitive optical time-domain reflectometer) was put forward by Taylor and Lee in 1993. It serves as a typical technique for monitoring distributed vibrations. At present, it has a wide range of applications in the field of large building structure, health monitoring [1], perimeter security of important places [2], and so on. However, we hope to grasp its monitoring state more accurately, understand its vibration mode better, and enhance the monitoring efficiency. Therefore, it is of great significance for a standard time-domain analytic expression of the -OTDR data. The -OTDR data is a distributed vibration signal. In order to standardize the time-domain analytic expression, we need to extract and represent useful signals.

In signal extraction, the feature extraction methods in time domain mainly include probability analysis method, time series method, correlation function analysis method, and time-domain waveform feature analysis. Potočnik and Govekar analyzed probabilistic statistics of vibration signals. They combined probability statistical analysis, feature extraction, and principal component analysis. The combined method is used for evaluating the performance of multiple classifiers [3]. Delpha et al. also combined probability statistical analysis, feature extraction, and principal component analysis. The combined method is used for condition monitoring and fault diagnosis [4]. Ma et al. used the time series model to extract features of vibration signals. The fault signals and nonfault signals are identified effectively by the method [5]. Liu et al. used multiplex detection to eliminate trend correlation analysis. The feature extraction is achieved finally [6]. Alhazza analyzed the waveform of the signal. Alhazza used waveforms to control the system [7]. In a word, the time series, correlation function, and waveform analysis are all based on the condition of obvious signals. But for the condition of poor SNR (signal-to-noise ratio), the probability analysis method has incomparable advantages to other methods because of its unique statistical characteristics. In signal expression, some scholars focused on the NMF (nonnegative matrix factorization) method and its expansion methods. Gao et al. combined TDF method and NMF method. The combined method is used to diagnose faults [8]. Li et al. combined the transformation method, NMF method, mutual information method, and multiobjective evolutionary algorithm [9]. Li et al. also, respectively, combined generalized transform method to NMF method and 2DNMF method [10, 11]. Some other scholars focused on the EMD (empirical mode decomposition) method and its expansion methods. Rai and Upadhyay combined EMD method and -means method to process the signal [12]. Liu et al. put EMD method into the process of multiplex detection to eliminate trend correlation analysis [6]. In conclusion, NMF and the related methods pay more attention to data model. These researches proceed from the fitting curve of signal separation but ignore the research of vibration mechanism. EMD and the related methods pay more attention to mechanism model. These researches simply proceed from the vibration mechanism and the expression curve but ignore the research of data features.

A summary of the above signal extraction studies is presented. When the SNR is weak, there will be some errors in the extraction of signals. -OTDR technique monitors the vibration of an optical fiber. The shape of optical fiber is a line. There will be weak SNR on the farther point of the optical fiber with the attenuation of signal propagation. Therefore, a signal extraction method which is more suitable for the condition of weak SNR is needed. On the other hand, it is more necessary for data driven and mechanism driven organic combination for the representation of -OTDR distributed vibration signals. The combined method can not only reflect the real situation of data but also reflect the real state of vibration. This paper is divided into two parts. The first part uses the 2D-TESP method to extract the signal. The 2D-TESP method has a more appropriate coding interval and also takes into account the first derivative and the two derivatives. It has good effect on signal extraction under weak SNR. The second part uses the GAEMD-NMF method to express the signal. It combines the EMD method and the NMF method. Some parameters are optimized by GA (genetic algorithm) after the two methods are combined. It can better solve the relative standard time-domain expression problem of distributed vibration signals.

#### 2. Time-Domain Signal Extraction Based on 2D-TESP Algorithm

Signal extraction aims at better signal representation. Signal extraction requires full preservation of signal data prototypes in the time domain. Because of the characteristics of -OTDR technology, it is hoped that effective signals can be extracted on the point with weak SNR. Therefore, the 2D-TESP method is selected to extract the features of the signal.

##### 2.1. TESP Algorithm

TESP (time encoded signal processing) algorithm has two main features [13–16]. The first feature is that the algorithm is based on the time domain which directly processes the signal. The second feature is to convert signals into probabilistic models that contain finite elements. Simply put, the method is to reencode the signal in the time domain. Typical time-frequency domain analysis algorithms include FFT, WT, and HHT. These algorithms have a long operation time and are also unsuitable for the poor SNR. They are not suited for distributed vibration data characteristics. TESP algorithm has the advantages of intuitive, small computation and simple implementation process. The traditional TESP algorithm is suitable for the feature extraction of speech signals [13]. The TESP method was improved by Wang’s team in 2014. They apply the method to feature extraction of sound signals [14]. Wang et al. used it to develop two-dimensional feature extraction. They applied the improved algorithm to signal feature extraction in a complex environment [15, 16]. The specific implementation steps of TESP algorithm are as follows.

(1) The window is divided into time-domain signals on each length node of the optical fiber. In each window, the zero crossing rate of the signal is calculated. The interval between two adjacent zero points is a time period. According to the rule of TESP algorithm, each time period is called meta.

(2) There are two indexes in the meta. One is the duration; it is usually expressed in . The other one is the signal form; it is usually expressed in . At the same time, the following information is obtained according to these two indexes:(a)The number of sampling points existing in each element is the duration.(b)In each element’s duration, the data is derived one by one. The extreme number in each element is obtained.

(3) The matrix is constructed by using and as two dimensions. Each element is coded according to the elements in the matrix.

(4) The probability of each code in the matrix is compiled statistically. Finally, the probability distribution is used as a feature to be inserted into a classifier or cluster.

##### 2.2. Feature Extraction Based on 2D-TESP Algorithm

There are only 29 characters because of the limitations of the encoding principle in traditional TESP algorithms. At the same time, the traditional TESP method achieves higher recognition rate in recognition. This paper focuses on the study of damped vibration. The encoding of 29 characters has far exceeded the requirements of damped vibration in the -OTDR technique. Therefore, the - matrix is extended to -1 and -2 matrices in the paper at first. Secondly, the coding principle for each matrix is reduced from 29 to 13. Lastly, the -1 and -2 matrices are encoded together with joint probability distribution statistics to form the matrix.

###### 2.2.1. The Reduced TESP Symbol Table

In the traditional TESP algorithm, only represents the first derivative. There may be mutual influence between points in -OTDR technology. Therefore, further analysis of the -OTDR signal is needed. The concept of the inflection point of the two-order derivative is introduced in the traditional method. In this paper, the matrix that is the extreme point is set as -1, and the matrix that is the inflection point is set as -2.

Because of the characteristics of the -OTDR technology, the two indicators named and do not require such many codes. The new method employs 13 coded representations. Tables 1 and 2 represent the standard 29 encoded and the shrunk 13 encoded -1 matrices, respectively.